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e3c2578daebbd774e98ff03ad79fdcc301a875ea	# Dataset Card for Evaluation run of Delcos/Starling-LM-11B-alpha ## Dataset Description - Homepage: - Repository: https://huggingface.co/Delcos/Starling-LM-11B-alpha - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Delcos/Starling-LM-11B-alpha](https://huggingface.co/Delcos/Starling-LM-11B-alpha) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Delcos__Starling-LM-11B-alpha", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T16:46:23.982029](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__Starling-LM-11B-alpha/blob/main/results_2023-12-09T16-46-23.982029.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6362170386987497, "acc_stderr": 0.03232328033089801, "acc_norm": 0.6416906108621553, "acc_norm_stderr": 0.03297213400326341, "mc1": 0.38922888616891066, "mc1_stderr": 0.01706855268069033, "mc2": 0.5452023492477854, "mc2_stderr": 0.016056772234309992 }, "harness\|arc:challenge\|25": { "acc": 0.6006825938566553, "acc_stderr": 0.014312094557946705, "acc_norm": 0.6296928327645052, "acc_norm_stderr": 0.01411129875167495 }, "harness\|hellaswag\|10": { "acc": 0.668990240987851, "acc_stderr": 0.004696148339570979, "acc_norm": 0.8485361481776539, "acc_norm_stderr": 0.0035776774950640783 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6566037735849056, "acc_stderr": 0.02922452646912479, "acc_norm": 0.6566037735849056, "acc_norm_stderr": 0.02922452646912479 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.025305906241590632, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.025305906241590632 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7548387096774194, "acc_stderr": 0.024472243840895535, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.024472243840895535 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.458128078817734, "acc_stderr": 0.03505630140785741, "acc_norm": 0.458128078817734, "acc_norm_stderr": 0.03505630140785741 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8080808080808081, "acc_stderr": 0.02805779167298902, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.02805779167298902 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.02247325333276878, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.02247325333276878 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6410256410256411, "acc_stderr": 0.024321738484602354, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602354 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114986, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114986 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669235, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669235 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.47685185185185186, "acc_stderr": 0.034063153607115065, "acc_norm": 0.47685185185185186, "acc_norm_stderr": 0.034063153607115065 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588667, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588667 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233504, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233504 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7309417040358744, "acc_stderr": 0.029763779406874972, "acc_norm": 0.7309417040358744, "acc_norm_stderr": 0.029763779406874972 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7557251908396947, "acc_stderr": 0.037683359597287434, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.037683359597287434 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459754, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459754 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8109833971902938, "acc_stderr": 0.014000791294407004, "acc_norm": 0.8109833971902938, "acc_norm_stderr": 0.014000791294407004 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.025190181327608422, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.025190181327608422 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4223463687150838, "acc_stderr": 0.01651959427529712, "acc_norm": 0.4223463687150838, "acc_norm_stderr": 0.01651959427529712 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7026143790849673, "acc_stderr": 0.02617390850671858, "acc_norm": 0.7026143790849673, "acc_norm_stderr": 0.02617390850671858 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7253086419753086, "acc_stderr": 0.02483605786829468, "acc_norm": 0.7253086419753086, "acc_norm_stderr": 0.02483605786829468 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5106382978723404, "acc_stderr": 0.02982074719142244, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.02982074719142244 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46284224250325945, "acc_stderr": 0.012734923579532063, "acc_norm": 0.46284224250325945, "acc_norm_stderr": 0.012734923579532063 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.02824568739146292, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.02824568739146292 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.019047485239360375, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.019047485239360375 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616914, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616914 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896308, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896308 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03126781714663179, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38922888616891066, "mc1_stderr": 0.01706855268069033, "mc2": 0.5452023492477854, "mc2_stderr": 0.016056772234309992 }, "harness\|winogrande\|5": { "acc": 0.7782162588792423, "acc_stderr": 0.011676109244497813 }, "harness\|gsm8k\|5": { "acc": 0.379833206974981, "acc_stderr": 0.013368818096960498 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Delcos__Starling-LM-11B-alpha	[ "region:us" ]	2023-12-09T16:49:14+00:00	{"pretty_name": "Evaluation run of Delcos/Starling-LM-11B-alpha", "dataset_summary": "Dataset automatically created during the evaluation run of model [Delcos/Starling-LM-11B-alpha](https://huggingface.co/Delcos/Starling-LM-11B-alpha) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Delcos__Starling-LM-11B-alpha\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T16:46:23.982029](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__Starling-LM-11B-alpha/blob/main/results_2023-12-09T16-46-23.982029.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6362170386987497,\n \"acc_stderr\": 0.03232328033089801,\n \"acc_norm\": 0.6416906108621553,\n \"acc_norm_stderr\": 0.03297213400326341,\n \"mc1\": 0.38922888616891066,\n \"mc1_stderr\": 0.01706855268069033,\n \"mc2\": 0.5452023492477854,\n \"mc2_stderr\": 0.016056772234309992\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946705,\n \"acc_norm\": 0.6296928327645052,\n \"acc_norm_stderr\": 0.01411129875167495\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.668990240987851,\n \"acc_stderr\": 0.004696148339570979,\n \"acc_norm\": 0.8485361481776539,\n \"acc_norm_stderr\": 0.0035776774950640783\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.025305906241590632,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.025305906241590632\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5238095238095238,\n 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["/details_harness\|winogrande\|5_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T16-46-23.982029.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T16_46_23.982029", "path": ["results_2023-12-09T16-46-23.982029.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T16-46-23.982029.parquet"]}]}]}	2023-12-09T16:49:56+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Delcos/Starling-LM-11B-alpha ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Delcos/Starling-LM-11B-alpha on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T16:46:23.982029(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Delcos/Starling-LM-11B-alpha", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Delcos/Starling-LM-11B-alpha on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:46:23.982029(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Delcos/Starling-LM-11B-alpha", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Delcos/Starling-LM-11B-alpha on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:46:23.982029(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 21, 31, 170, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Delcos/Starling-LM-11B-alpha## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Delcos/Starling-LM-11B-alpha on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T16:46:23.982029(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
4256fb07972e90f6c8b3ba1c9e23a424d26ebe03	# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T16:52:16.188783](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Ties/blob/main/results_2023-12-09T16-52-16.188783.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6262329269588028, "acc_stderr": 0.03265531717656403, "acc_norm": 0.6261458795179596, "acc_norm_stderr": 0.033325096066245945, "mc1": 0.3623011015911873, "mc1_stderr": 0.016826646897262255, "mc2": 0.5206285653012832, "mc2_stderr": 0.015833320867777365 }, "harness\|arc:challenge\|25": { "acc": 0.6109215017064846, "acc_stderr": 0.014247309976045607, "acc_norm": 0.6348122866894198, "acc_norm_stderr": 0.014070265519268802 }, "harness\|hellaswag\|10": { "acc": 0.6538538139812786, "acc_stderr": 0.004747682003491466, "acc_norm": 0.8234415455088627, "acc_norm_stderr": 0.00380515334471309 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6074074074074074, "acc_stderr": 0.04218506215368881, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.04218506215368881 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146267, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146267 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4473684210526316, "acc_stderr": 0.046774730044911984, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.046774730044911984 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728762, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728762 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155254, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155254 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768177, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768177 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7419354838709677, "acc_stderr": 0.024892469172462836, "acc_norm": 0.7419354838709677, "acc_norm_stderr": 0.024892469172462836 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592154, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592154 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593552, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593552 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396997, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396997 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.02857834836547308, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547308 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8348623853211009, "acc_stderr": 0.015919557829976054, "acc_norm": 0.8348623853211009, "acc_norm_stderr": 0.015919557829976054 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7679324894514767, "acc_stderr": 0.02747974455080851, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.02747974455080851 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575499, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7520661157024794, "acc_stderr": 0.03941897526516302, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516302 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7055214723926381, "acc_stderr": 0.03581165790474082, "acc_norm": 0.7055214723926381, "acc_norm_stderr": 0.03581165790474082 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073336, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073336 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8071519795657727, "acc_stderr": 0.014108533515757431, "acc_norm": 0.8071519795657727, "acc_norm_stderr": 0.014108533515757431 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.025190181327608408, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.025190181327608408 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4324022346368715, "acc_stderr": 0.01656897123354861, "acc_norm": 0.4324022346368715, "acc_norm_stderr": 0.01656897123354861 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6928104575163399, "acc_stderr": 0.02641560191438899, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.02641560191438899 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885142, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885142 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.025630824975621358, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.025630824975621358 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4445893089960887, "acc_stderr": 0.01269157579265712, "acc_norm": 0.4445893089960887, "acc_norm_stderr": 0.01269157579265712 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6286764705882353, "acc_stderr": 0.029349803139765873, "acc_norm": 0.6286764705882353, "acc_norm_stderr": 0.029349803139765873 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6486928104575164, "acc_stderr": 0.01931267606578655, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.01931267606578655 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.04607582090719976, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.04607582090719976 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675606, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675606 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7860696517412935, "acc_stderr": 0.02899690969332891, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.02899690969332891 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3623011015911873, "mc1_stderr": 0.016826646897262255, "mc2": 0.5206285653012832, "mc2_stderr": 0.015833320867777365 }, "harness\|winogrande\|5": { "acc": 0.7687450670876085, "acc_stderr": 0.01185004012485051 }, "harness\|gsm8k\|5": { "acc": 0.6823351023502654, "acc_stderr": 0.012824066621488836 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Ties	[ "region:us" ]	2023-12-09T16:55:10+00:00	{"pretty_name": "Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Ties\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T16:52:16.188783](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Ties/blob/main/results_2023-12-09T16-52-16.188783.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6262329269588028,\n \"acc_stderr\": 0.03265531717656403,\n \"acc_norm\": 0.6261458795179596,\n \"acc_norm_stderr\": 0.033325096066245945,\n \"mc1\": 0.3623011015911873,\n \"mc1_stderr\": 0.016826646897262255,\n \"mc2\": 0.5206285653012832,\n \"mc2_stderr\": 0.015833320867777365\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6109215017064846,\n \"acc_stderr\": 0.014247309976045607,\n \"acc_norm\": 0.6348122866894198,\n \"acc_norm_stderr\": 0.014070265519268802\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6538538139812786,\n \"acc_stderr\": 0.004747682003491466,\n \"acc_norm\": 0.8234415455088627,\n \"acc_norm_stderr\": 0.00380515334471309\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.04218506215368881,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.04218506215368881\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146267,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146267\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4473684210526316,\n \"acc_stderr\": 0.046774730044911984,\n \"acc_norm\": 0.4473684210526316,\n \"acc_norm_stderr\": 0.046774730044911984\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728762,\n \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728762\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7419354838709677,\n \"acc_stderr\": 0.024892469172462836,\n \"acc_norm\": 0.7419354838709677,\n \"acc_norm_stderr\": 0.024892469172462836\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396997,\n \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396997\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8348623853211009,\n \"acc_stderr\": 0.015919557829976054,\n \"acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.015919557829976054\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516302,\n \"acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516302\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.021901905115073336,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.021901905115073336\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.69,\n 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-52-16.188783.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-52-16.188783.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T16_52_16.188783", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-52-16.188783.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-52-16.188783.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T16_52_16.188783", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-52-16.188783.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-52-16.188783.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T16_52_16.188783", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T16-52-16.188783.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T16-52-16.188783.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T16_52_16.188783", "path": ["/details_harness\|winogrande\|5_2023-12-09T16-52-16.188783.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T16-52-16.188783.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T16_52_16.188783", "path": ["results_2023-12-09T16-52-16.188783.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T16-52-16.188783.parquet"]}]}]}	2023-12-09T16:55:54+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T16:52:16.188783(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:52:16.188783(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:52:16.188783(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 29, 31, 178, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T16:52:16.188783(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
a00e82546848083f5a58d20092cb82d97ca8f42c	# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T16:53:19.272337](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp/blob/main/results_2023-12-09T16-53-19.272337.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6389439642939008, "acc_stderr": 0.03231020427870188, "acc_norm": 0.6389579295248086, "acc_norm_stderr": 0.03297676323880707, "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5522545162562386, "mc2_stderr": 0.015322345793520823 }, "harness\|arc:challenge\|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759093, "acc_norm": 0.6569965870307167, "acc_norm_stderr": 0.013872423223718164 }, "harness\|hellaswag\|10": { "acc": 0.6550487950607449, "acc_stderr": 0.004743808792037863, "acc_norm": 0.8450507866958773, "acc_norm_stderr": 0.0036111673029597833 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7645161290322581, "acc_stderr": 0.02413763242933771, "acc_norm": 0.7645161290322581, "acc_norm_stderr": 0.02413763242933771 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.047258156262526066, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526066 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7843137254901961, "acc_stderr": 0.028867431449849316, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.028867431449849316 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.036959801280988226, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.036959801280988226 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7177914110429447, "acc_stderr": 0.03536117886664742, "acc_norm": 0.7177914110429447, "acc_norm_stderr": 0.03536117886664742 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8186462324393359, "acc_stderr": 0.01377869377846408, "acc_norm": 0.8186462324393359, "acc_norm_stderr": 0.01377869377846408 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4212290502793296, "acc_stderr": 0.016513676031179602, "acc_norm": 0.4212290502793296, "acc_norm_stderr": 0.016513676031179602 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7191358024691358, "acc_stderr": 0.025006469755799208, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.025006469755799208 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873866, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873866 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4439374185136897, "acc_stderr": 0.012689708167787684, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787684 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681404, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681404 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6486928104575164, "acc_stderr": 0.019312676065786547, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.019312676065786547 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291293, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291293 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5522545162562386, "mc2_stderr": 0.015322345793520823 }, "harness\|winogrande\|5": { "acc": 0.7995264404104183, "acc_stderr": 0.011251958281205083 }, "harness\|gsm8k\|5": { "acc": 0.6982562547384382, "acc_stderr": 0.01264354476287336 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp	[ "region:us" ]	2023-12-09T16:56:08+00:00	{"pretty_name": "Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T16:53:19.272337](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-neural-chat-7b-v3-2-Slerp/blob/main/results_2023-12-09T16-53-19.272337.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6389439642939008,\n \"acc_stderr\": 0.03231020427870188,\n \"acc_norm\": 0.6389579295248086,\n \"acc_norm_stderr\": 0.03297676323880707,\n \"mc1\": 0.38922888616891066,\n \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5522545162562386,\n \"mc2_stderr\": 0.015322345793520823\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759093,\n \"acc_norm\": 0.6569965870307167,\n \"acc_norm_stderr\": 0.013872423223718164\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6550487950607449,\n \"acc_stderr\": 0.004743808792037863,\n \"acc_norm\": 0.8450507866958773,\n \"acc_norm_stderr\": 0.0036111673029597833\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7013888888888888,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849316,\n \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849316\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988226,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7177914110429447,\n \"acc_stderr\": 0.03536117886664742,\n \"acc_norm\": 0.7177914110429447,\n \"acc_norm_stderr\": 0.03536117886664742\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8186462324393359,\n \"acc_stderr\": 0.01377869377846408,\n \"acc_norm\": 0.8186462324393359,\n \"acc_norm_stderr\": 0.01377869377846408\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4212290502793296,\n \"acc_stderr\": 0.016513676031179602,\n \"acc_norm\": 0.4212290502793296,\n \"acc_norm_stderr\": 0.016513676031179602\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4439374185136897,\n \"acc_stderr\": 0.012689708167787684,\n \"acc_norm\": 0.4439374185136897,\n \"acc_norm_stderr\": 0.012689708167787684\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681404,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681404\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6486928104575164,\n \"acc_stderr\": 0.019312676065786547,\n \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.019312676065786547\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291293,\n \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291293\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.38922888616891066,\n \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5522545162562386,\n \"mc2_stderr\": 0.015322345793520823\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7995264404104183,\n \"acc_stderr\": 0.011251958281205083\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6982562547384382,\n \"acc_stderr\": 0.01264354476287336\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp", "leaderboard_url": 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of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T16:53:19.272337(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:53:19.272337(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:53:19.272337(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T16:53:19.272337(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
fbbdf2d39eea62411eff3c1212f44928cb20c356	# Dataset Card for "pile_dedupe_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	xwjiang2010/pile_dedupe_val	[ "region:us" ]	2023-12-09T16:58:31+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6062337711, "num_examples": 1000000}], "download_size": 3343428302, "dataset_size": 6062337711}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-09T18:28:58+00:00	[]	[]	TAGS #region-us	# Dataset Card for "pile_dedupe_val" More Information needed	[ "# Dataset Card for \"pile_dedupe_val\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"pile_dedupe_val\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"pile_dedupe_val\"\n\nMore Information needed" ]
d311cb08003cf168e22a52bdda3010ea94d8cdeb	# Dataset Card for Evaluation run of Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties](https://huggingface.co/Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Weyaxi__MetaMath-NeuralHermes-2.5-Mistral-7B-Ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T16:59:41.207552](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-NeuralHermes-2.5-Mistral-7B-Ties/blob/main/results_2023-12-09T16-59-41.207552.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6257025979407843, "acc_stderr": 0.03245342362812811, "acc_norm": 0.6259954931770727, "acc_norm_stderr": 0.03311192058156274, "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.501521774455576, "mc2_stderr": 0.01581364594434788 }, "harness\|arc:challenge\|25": { "acc": 0.5947098976109215, "acc_stderr": 0.014346869060229315, "acc_norm": 0.6245733788395904, "acc_norm_stderr": 0.014150631435111728 }, "harness\|hellaswag\|10": { "acc": 0.6485759808803028, "acc_stderr": 0.004764393985111037, "acc_norm": 0.828918542123083, "acc_norm_stderr": 0.0037581050431501253 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6754716981132075, "acc_stderr": 0.028815615713432115, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.028815615713432115 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.035868792800803406, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.035868792800803406 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099834, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099834 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055263, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055263 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7387096774193549, "acc_stderr": 0.024993053397764815, "acc_norm": 0.7387096774193549, "acc_norm_stderr": 0.024993053397764815 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7474747474747475, "acc_stderr": 0.03095405547036589, "acc_norm": 0.7474747474747475, "acc_norm_stderr": 0.03095405547036589 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768776, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768776 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5948717948717949, "acc_stderr": 0.024890471769938145, "acc_norm": 0.5948717948717949, "acc_norm_stderr": 0.024890471769938145 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8238532110091743, "acc_stderr": 0.016332882393431385, "acc_norm": 0.8238532110091743, "acc_norm_stderr": 0.016332882393431385 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502325, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502325 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7679324894514767, "acc_stderr": 0.02747974455080851, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.02747974455080851 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477518, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477518 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7557251908396947, "acc_stderr": 0.037683359597287434, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.037683359597287434 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.032910995786157686, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.032910995786157686 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5178571428571429, "acc_stderr": 0.04742762361243011, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.04742762361243011 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.02126271940040696, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.02126271940040696 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8160919540229885, "acc_stderr": 0.013853724170922526, "acc_norm": 0.8160919540229885, "acc_norm_stderr": 0.013853724170922526 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508287, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508287 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3575418994413408, "acc_stderr": 0.016029394474894886, "acc_norm": 0.3575418994413408, "acc_norm_stderr": 0.016029394474894886 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7516339869281046, "acc_stderr": 0.02473998135511359, "acc_norm": 0.7516339869281046, "acc_norm_stderr": 0.02473998135511359 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.024748624490537368, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.024748624490537368 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4595827900912647, "acc_stderr": 0.012728446067669963, "acc_norm": 0.4595827900912647, "acc_norm_stderr": 0.012728446067669963 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6544117647058824, "acc_stderr": 0.028888193103988626, "acc_norm": 0.6544117647058824, "acc_norm_stderr": 0.028888193103988626 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6421568627450981, "acc_stderr": 0.019393058402355435, "acc_norm": 0.6421568627450981, "acc_norm_stderr": 0.019393058402355435 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.04607582090719976, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.04607582090719976 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.501521774455576, "mc2_stderr": 0.01581364594434788 }, "harness\|winogrande\|5": { "acc": 0.7513812154696132, "acc_stderr": 0.012147314713403108 }, "harness\|gsm8k\|5": { "acc": 0.6929492039423806, "acc_stderr": 0.012705685723131709 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Weyaxi__MetaMath-NeuralHermes-2.5-Mistral-7B-Ties	[ "region:us" ]	2023-12-09T17:02:33+00:00	{"pretty_name": "Evaluation run of Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties](https://huggingface.co/Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__MetaMath-NeuralHermes-2.5-Mistral-7B-Ties\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T16:59:41.207552](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__MetaMath-NeuralHermes-2.5-Mistral-7B-Ties/blob/main/results_2023-12-09T16-59-41.207552.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6257025979407843,\n \"acc_stderr\": 0.03245342362812811,\n \"acc_norm\": 0.6259954931770727,\n \"acc_norm_stderr\": 0.03311192058156274,\n \"mc1\": 0.34149326805385555,\n \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.501521774455576,\n \"mc2_stderr\": 0.01581364594434788\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5947098976109215,\n \"acc_stderr\": 0.014346869060229315,\n \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.014150631435111728\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6485759808803028,\n \"acc_stderr\": 0.004764393985111037,\n \"acc_norm\": 0.828918542123083,\n \"acc_norm_stderr\": 0.0037581050431501253\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.028815615713432115,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.028815615713432115\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5953757225433526,\n \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099834,\n \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099834\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055263,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055263\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7387096774193549,\n \"acc_stderr\": 0.024993053397764815,\n \"acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.024993053397764815\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.03095405547036589,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.03095405547036589\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8238532110091743,\n \"acc_stderr\": 0.016332882393431385,\n \"acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.016332882393431385\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502325,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502325\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477518,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477518\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.032910995786157686,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.032910995786157686\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.04742762361243011,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.04742762361243011\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.02126271940040696,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.02126271940040696\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8160919540229885,\n \"acc_stderr\": 0.013853724170922526,\n \"acc_norm\": 0.8160919540229885,\n \"acc_norm_stderr\": 0.013853724170922526\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3575418994413408,\n \"acc_stderr\": 0.016029394474894886,\n \"acc_norm\": 0.3575418994413408,\n \"acc_norm_stderr\": 0.016029394474894886\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.024748624490537368,\n \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.024748624490537368\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4595827900912647,\n \"acc_stderr\": 0.012728446067669963,\n \"acc_norm\": 0.4595827900912647,\n \"acc_norm_stderr\": 0.012728446067669963\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6544117647058824,\n \"acc_stderr\": 0.028888193103988626,\n \"acc_norm\": 0.6544117647058824,\n \"acc_norm_stderr\": 0.028888193103988626\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6421568627450981,\n \"acc_stderr\": 0.019393058402355435,\n \"acc_norm\": 0.6421568627450981,\n \"acc_norm_stderr\": 0.019393058402355435\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8109452736318408,\n \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.34149326805385555,\n \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.501521774455576,\n \"mc2_stderr\": 0.01581364594434788\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403108\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6929492039423806,\n \"acc_stderr\": 0.012705685723131709\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|gsm8k\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hellaswag\|10_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T16-59-41.207552.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T16-59-41.207552.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["/details_harness\|winogrande\|5_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T16-59-41.207552.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T16_59_41.207552", "path": ["results_2023-12-09T16-59-41.207552.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T16-59-41.207552.parquet"]}]}]}	2023-12-09T17:03:21+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T16:59:41.207552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:59:41.207552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T16:59:41.207552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 31, 31, 180, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T16:59:41.207552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
2cc45015f5badebe0df62e77201687c79aa0a23a	# Dataset Card for Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [mwitiderrick/open_llama_3b_instruct_v_0.2](https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:00:38.950221](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2/blob/main/results_2023-12-09T17-00-38.950221.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.26151949284222964, "acc_stderr": 0.03090435201991025, "acc_norm": 0.26267138346311003, "acc_norm_stderr": 0.03166222554081637, "mc1": 0.25703794369645044, "mc1_stderr": 0.01529807750948508, "mc2": 0.3816212066330296, "mc2_stderr": 0.013929117644890942 }, "harness\|arc:challenge\|25": { "acc": 0.36177474402730375, "acc_stderr": 0.014041957945038059, "acc_norm": 0.3848122866894198, "acc_norm_stderr": 0.014218371065251107 }, "harness\|hellaswag\|10": { "acc": 0.4953196574387572, "acc_stderr": 0.00498956279828052, "acc_norm": 0.6676956781517626, "acc_norm_stderr": 0.004700767741735565 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847415, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847415 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.28888888888888886, "acc_stderr": 0.0391545063041425, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.0391545063041425 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.18421052631578946, "acc_stderr": 0.0315469804508223, "acc_norm": 0.18421052631578946, "acc_norm_stderr": 0.0315469804508223 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2528301886792453, "acc_stderr": 0.026749899771241238, "acc_norm": 0.2528301886792453, "acc_norm_stderr": 0.026749899771241238 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179961, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179961 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768077, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768077 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.30638297872340425, "acc_stderr": 0.030135906478517563, "acc_norm": 0.30638297872340425, "acc_norm_stderr": 0.030135906478517563 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2631578947368421, "acc_stderr": 0.0414243971948936, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.0414243971948936 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2482758620689655, "acc_stderr": 0.03600105692727771, "acc_norm": 0.2482758620689655, "acc_norm_stderr": 0.03600105692727771 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.21428571428571427, "acc_stderr": 0.021132859182754444, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.021132859182754444 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.040735243221471276, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.040735243221471276 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.26129032258064516, "acc_stderr": 0.024993053397764815, "acc_norm": 0.26129032258064516, "acc_norm_stderr": 0.024993053397764815 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2660098522167488, "acc_stderr": 0.03108982600293753, "acc_norm": 0.2660098522167488, "acc_norm_stderr": 0.03108982600293753 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.17, "acc_stderr": 0.03775251680686371, "acc_norm": 0.17, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2727272727272727, "acc_stderr": 0.03477691162163659, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.03477691162163659 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.22727272727272727, "acc_stderr": 0.02985751567338641, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.02985751567338641 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.21761658031088082, "acc_stderr": 0.029778663037752954, "acc_norm": 0.21761658031088082, "acc_norm_stderr": 0.029778663037752954 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.23076923076923078, "acc_stderr": 0.02136202772522271, "acc_norm": 0.23076923076923078, "acc_norm_stderr": 0.02136202772522271 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275805, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275805 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.21428571428571427, "acc_stderr": 0.026653531596715484, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.026653531596715484 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.271523178807947, "acc_stderr": 0.036313298039696545, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.036313298039696545 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.23669724770642203, "acc_stderr": 0.018224078117299085, "acc_norm": 0.23669724770642203, "acc_norm_stderr": 0.018224078117299085 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.18055555555555555, "acc_stderr": 0.026232878971491666, "acc_norm": 0.18055555555555555, "acc_norm_stderr": 0.026232878971491666 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.030964517926923393, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.030964517926923393 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.26582278481012656, "acc_stderr": 0.02875679962965834, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.02875679962965834 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.336322869955157, "acc_stderr": 0.031708824268455, "acc_norm": 0.336322869955157, "acc_norm_stderr": 0.031708824268455 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.22137404580152673, "acc_stderr": 0.036412970813137276, "acc_norm": 0.22137404580152673, "acc_norm_stderr": 0.036412970813137276 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.24793388429752067, "acc_stderr": 0.039418975265163025, "acc_norm": 0.24793388429752067, "acc_norm_stderr": 0.039418975265163025 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.04414343666854933, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.04414343666854933 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.19631901840490798, "acc_stderr": 0.031207970394709215, "acc_norm": 0.19631901840490798, "acc_norm_stderr": 0.031207970394709215 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.22321428571428573, "acc_stderr": 0.039523019677025116, "acc_norm": 0.22321428571428573, "acc_norm_stderr": 0.039523019677025116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2524271844660194, "acc_stderr": 0.04301250399690877, "acc_norm": 0.2524271844660194, "acc_norm_stderr": 0.04301250399690877 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2692307692307692, "acc_stderr": 0.02905858830374884, "acc_norm": 0.2692307692307692, "acc_norm_stderr": 0.02905858830374884 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2886334610472541, "acc_stderr": 0.016203792703197797, "acc_norm": 0.2886334610472541, "acc_norm_stderr": 0.016203792703197797 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.29190751445086704, "acc_stderr": 0.024476994076247333, "acc_norm": 0.29190751445086704, "acc_norm_stderr": 0.024476994076247333 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808835, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808835 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.23529411764705882, "acc_stderr": 0.024288619466046105, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.024288619466046105 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2765273311897106, "acc_stderr": 0.025403832978179622, "acc_norm": 0.2765273311897106, "acc_norm_stderr": 0.025403832978179622 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2654320987654321, "acc_stderr": 0.024569223600460845, "acc_norm": 0.2654320987654321, "acc_norm_stderr": 0.024569223600460845 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2730496453900709, "acc_stderr": 0.02657786094330786, "acc_norm": 0.2730496453900709, "acc_norm_stderr": 0.02657786094330786 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.23989569752281617, "acc_stderr": 0.010906282617981634, "acc_norm": 0.23989569752281617, "acc_norm_stderr": 0.010906282617981634 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.20220588235294118, "acc_stderr": 0.02439819298665492, "acc_norm": 0.20220588235294118, "acc_norm_stderr": 0.02439819298665492 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2581699346405229, "acc_stderr": 0.017704531653250075, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.017704531653250075 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.36363636363636365, "acc_stderr": 0.04607582090719976, "acc_norm": 0.36363636363636365, "acc_norm_stderr": 0.04607582090719976 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.2163265306122449, "acc_stderr": 0.026358916334904035, "acc_norm": 0.2163265306122449, "acc_norm_stderr": 0.026358916334904035 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.27860696517412936, "acc_stderr": 0.031700561834973086, "acc_norm": 0.27860696517412936, "acc_norm_stderr": 0.031700561834973086 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3313253012048193, "acc_stderr": 0.036643147772880864, "acc_norm": 0.3313253012048193, "acc_norm_stderr": 0.036643147772880864 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.28654970760233917, "acc_stderr": 0.034678266857038266, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.034678266857038266 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.25703794369645044, "mc1_stderr": 0.01529807750948508, "mc2": 0.3816212066330296, "mc2_stderr": 0.013929117644890942 }, "harness\|winogrande\|5": { "acc": 0.6345698500394633, "acc_stderr": 0.013533965097638795 }, "harness\|gsm8k\|5": { "acc": 0.01592115238817286, "acc_stderr": 0.0034478192723889946 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2	[ "region:us" ]	2023-12-09T17:02:51+00:00	{"pretty_name": "Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [mwitiderrick/open_llama_3b_instruct_v_0.2](https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:00:38.950221](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2/blob/main/results_2023-12-09T17-00-38.950221.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26151949284222964,\n \"acc_stderr\": 0.03090435201991025,\n \"acc_norm\": 0.26267138346311003,\n \"acc_norm_stderr\": 0.03166222554081637,\n \"mc1\": 0.25703794369645044,\n \"mc1_stderr\": 0.01529807750948508,\n \"mc2\": 0.3816212066330296,\n \"mc2_stderr\": 0.013929117644890942\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.36177474402730375,\n \"acc_stderr\": 0.014041957945038059,\n \"acc_norm\": 0.3848122866894198,\n \"acc_norm_stderr\": 0.014218371065251107\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4953196574387572,\n \"acc_stderr\": 0.00498956279828052,\n \"acc_norm\": 0.6676956781517626,\n \"acc_norm_stderr\": 0.004700767741735565\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.28888888888888886,\n \"acc_stderr\": 0.0391545063041425,\n \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.0391545063041425\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.026749899771241238,\n \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.026749899771241238\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.030135906478517563,\n \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.030135906478517563\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2631578947368421,\n \"acc_stderr\": 0.0414243971948936,\n \"acc_norm\": 0.2631578947368421,\n \"acc_norm_stderr\": 0.0414243971948936\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727771,\n \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727771\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.021132859182754444,\n \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.021132859182754444\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.29365079365079366,\n \"acc_stderr\": 0.040735243221471276,\n \"acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.040735243221471276\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.26129032258064516,\n \"acc_stderr\": 0.024993053397764815,\n \"acc_norm\": 0.26129032258064516,\n \"acc_norm_stderr\": 0.024993053397764815\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.2660098522167488,\n \"acc_stderr\": 0.03108982600293753,\n \"acc_norm\": 0.2660098522167488,\n \"acc_norm_stderr\": 0.03108982600293753\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n 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of model mwitiderrick/open_llama_3b_instruct_v_0.2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:00:38.950221(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model mwitiderrick/open_llama_3b_instruct_v_0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:00:38.950221(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model mwitiderrick/open_llama_3b_instruct_v_0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:00:38.950221(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 28, 31, 177, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model mwitiderrick/open_llama_3b_instruct_v_0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:00:38.950221(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
bba602698fd6f18226f6404fd8fb637ef67fa1eb	# Financial Sentiment Analysis Dataset ## Overview This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector. ## Data Description The dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency. ### Dataset Structure - Tweet: The text of the tweet, providing insights into financial discussions. - Sentiment: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral). ## Dataset Size - Bullish Sentiments: 17,368 - Bearish Sentiments: 8,542 - Neutral Sentiments: 12,181 ## Sources This dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment: - [FIQA Sentiment Classification](https://huggingface.co/datasets/ChanceFocus/fiqa-sentiment-classification): A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments. - [Stock Market Tweets Data](https://ieee-dataport.org/open-access/stock-market-tweets-data): A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments. - [Stock Related Tweet Sentiment](https://www.kaggle.com/datasets/mattgilgo/stock-related-tweet-sentiment): A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments. - [Master Thesis Data](https://github.com/moritzwilksch/MasterThesis/tree/main): Includes 3711 positive, 2784 neutral, and 2167 negative sentiments. - [Twitter Stock Sentiment](https://github.com/poojathakoor/twitter-stock-sentiment): Comprises 702 positive, 595 negative, and 481 neutral sentiments. - [Crypto Sentiment](https://github.com/surge-ai/crypto-sentiment/tree/main): Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments. - [Stock Sentiment](https://github.com/surge-ai/stock-sentiment/tree/main): Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments. - [Stockmarket Sentiment Dataset](https://www.kaggle.com/datasets/yash612/stockmarket-sentiment-dataset): Features 3685 positive and 2106 negative sentiments. - [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment): Contains 2398 positive, 1789 negative, and 7744 neutral sentiments. ## Usage This dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions. ## Acknowledgments We extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource. ## License This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets.	TimKoornstra/financial-tweets-sentiment	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:mit", "sentiment", "twitter", "finance", "crypto", "stocks", "tweet", "collection", "region:us" ]	2023-12-09T17:03:27+00:00	{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Financial Tweets with Sentiment class", "dataset_info": {"features": [{"name": "tweet", "dtype": "string"}, {"name": "sentiment", "dtype": {"class_label": {"names": {"0": "neutral", "1": "bullish", "2": "bearish"}}}}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6848991, "num_examples": 38091}], "download_size": 2648082, "dataset_size": 6848991}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["sentiment", "twitter", "finance", "crypto", "stocks", "tweet", "collection"]}	2023-12-20T11:04:21+00:00	[]	[ "en" ]	TAGS #task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #sentiment #twitter #finance #crypto #stocks #tweet #collection #region-us	# Financial Sentiment Analysis Dataset ## Overview This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector. ## Data Description The dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency. ### Dataset Structure - Tweet: The text of the tweet, providing insights into financial discussions. - Sentiment: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral). ## Dataset Size - Bullish Sentiments: 17,368 - Bearish Sentiments: 8,542 - Neutral Sentiments: 12,181 ## Sources This dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment: - FIQA Sentiment Classification: A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments. - Stock Market Tweets Data: A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments. - Stock Related Tweet Sentiment: A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments. - Master Thesis Data: Includes 3711 positive, 2784 neutral, and 2167 negative sentiments. - Twitter Stock Sentiment: Comprises 702 positive, 595 negative, and 481 neutral sentiments. - Crypto Sentiment: Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments. - Stock Sentiment: Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments. - Stockmarket Sentiment Dataset: Features 3685 positive and 2106 negative sentiments. - Twitter Financial News Sentiment: Contains 2398 positive, 1789 negative, and 7744 neutral sentiments. ## Usage This dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions. ## Acknowledgments We extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource. ## License This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets.	[ "# Financial Sentiment Analysis Dataset", "## Overview\nThis dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.", "## Data Description\nThe dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency.", "### Dataset Structure\n- Tweet: The text of the tweet, providing insights into financial discussions.\n- Sentiment: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral).", "## Dataset Size\n- Bullish Sentiments: 17,368\n- Bearish Sentiments: 8,542\n- Neutral Sentiments: 12,181", "## Sources\nThis dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment:\n\n- FIQA Sentiment Classification: A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments.\n- Stock Market Tweets Data: A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments.\n- Stock Related Tweet Sentiment: A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments.\n- Master Thesis Data: Includes 3711 positive, 2784 neutral, and 2167 negative sentiments.\n- Twitter Stock Sentiment: Comprises 702 positive, 595 negative, and 481 neutral sentiments.\n- Crypto Sentiment: Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments.\n- Stock Sentiment: Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments.\n- Stockmarket Sentiment Dataset: Features 3685 positive and 2106 negative sentiments.\n- Twitter Financial News Sentiment: Contains 2398 positive, 1789 negative, and 7744 neutral sentiments.", "## Usage\nThis dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions.", "## Acknowledgments\nWe extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource.", "## License\nThis dataset is made available under the MIT license, adhering to the licensing terms of the original datasets." ]	[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #sentiment #twitter #finance #crypto #stocks #tweet #collection #region-us \n", "# Financial Sentiment Analysis Dataset", "## Overview\nThis dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.", "## Data Description\nThe dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency.", "### Dataset Structure\n- Tweet: The text of the tweet, providing insights into financial discussions.\n- Sentiment: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral).", "## Dataset Size\n- Bullish Sentiments: 17,368\n- Bearish Sentiments: 8,542\n- Neutral Sentiments: 12,181", "## Sources\nThis dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment:\n\n- FIQA Sentiment Classification: A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments.\n- Stock Market Tweets Data: A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments.\n- Stock Related Tweet Sentiment: A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments.\n- Master Thesis Data: Includes 3711 positive, 2784 neutral, and 2167 negative sentiments.\n- Twitter Stock Sentiment: Comprises 702 positive, 595 negative, and 481 neutral sentiments.\n- Crypto Sentiment: Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments.\n- Stock Sentiment: Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments.\n- Stockmarket Sentiment Dataset: Features 3685 positive and 2106 negative sentiments.\n- Twitter Financial News Sentiment: Contains 2398 positive, 1789 negative, and 7744 neutral sentiments.", "## Usage\nThis dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions.", "## Acknowledgments\nWe extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource.", "## License\nThis dataset is made available under the MIT license, adhering to the licensing terms of the original datasets." ]	[ 57, 8, 71, 98, 54, 30, 250, 53, 47, 29 ]	[ "passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #sentiment #twitter #finance #crypto #stocks #tweet #collection #region-us \n# Financial Sentiment Analysis Dataset## Overview\nThis dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.## Data Description\nThe dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency.### Dataset Structure\n- Tweet: The text of the tweet, providing insights into financial discussions.\n- Sentiment: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral).## Dataset Size\n- Bullish Sentiments: 17,368\n- Bearish Sentiments: 8,542\n- Neutral Sentiments: 12,181" ]
a82b439c4c9afe4290f4517c7a764a75a2501e3c	# Dataset Card for Evaluation run of aloobun/open-llama-3b-v2-elmv3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/aloobun/open-llama-3b-v2-elmv3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [aloobun/open-llama-3b-v2-elmv3](https://huggingface.co/aloobun/open-llama-3b-v2-elmv3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_aloobun__open-llama-3b-v2-elmv3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T18:25:59.224844](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__open-llama-3b-v2-elmv3/blob/main/results_2023-12-09T18-25-59.224844.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.2804692579613333, "acc_stderr": 0.03160774886030324, "acc_norm": 0.28199113779250456, "acc_norm_stderr": 0.0323576565422058, "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.3550624387136162, "mc2_stderr": 0.01364292328900912 }, "harness\|arc:challenge\|25": { "acc": 0.3873720136518771, "acc_stderr": 0.014235872487909874, "acc_norm": 0.42150170648464164, "acc_norm_stderr": 0.014430197069326023 }, "harness\|hellaswag\|10": { "acc": 0.551185022903804, "acc_stderr": 0.004963567029129055, "acc_norm": 0.7326229834694284, "acc_norm_stderr": 0.004416861919100999 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3111111111111111, "acc_stderr": 0.03999262876617721, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.03999262876617721 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.2894736842105263, "acc_stderr": 0.03690677986137282, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.03690677986137282 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2943396226415094, "acc_stderr": 0.028049186315695248, "acc_norm": 0.2943396226415094, "acc_norm_stderr": 0.028049186315695248 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.24305555555555555, "acc_stderr": 0.0358687928008034, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.26011560693641617, "acc_stderr": 0.033450369167889925, "acc_norm": 0.26011560693641617, "acc_norm_stderr": 0.033450369167889925 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.17647058823529413, "acc_stderr": 0.03793281185307811, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.03793281185307811 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.32340425531914896, "acc_stderr": 0.030579442773610334, "acc_norm": 0.32340425531914896, "acc_norm_stderr": 0.030579442773610334 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2543859649122807, "acc_stderr": 0.04096985139843673, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.04096985139843673 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.21379310344827587, "acc_stderr": 0.03416520447747549, "acc_norm": 0.21379310344827587, "acc_norm_stderr": 0.03416520447747549 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2804232804232804, "acc_stderr": 0.023135287974325628, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.023135287974325628 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.16666666666666666, "acc_stderr": 0.03333333333333339, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.03333333333333339 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.25483870967741934, "acc_stderr": 0.024790118459332208, "acc_norm": 0.25483870967741934, "acc_norm_stderr": 0.024790118459332208 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.28078817733990147, "acc_stderr": 0.03161856335358611, "acc_norm": 0.28078817733990147, "acc_norm_stderr": 0.03161856335358611 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.296969696969697, "acc_stderr": 0.03567969772268049, "acc_norm": 0.296969696969697, "acc_norm_stderr": 0.03567969772268049 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.31313131313131315, "acc_stderr": 0.033042050878136525, "acc_norm": 0.31313131313131315, "acc_norm_stderr": 0.033042050878136525 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.24352331606217617, "acc_stderr": 0.030975436386845426, "acc_norm": 0.24352331606217617, "acc_norm_stderr": 0.030975436386845426 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.28974358974358977, "acc_stderr": 0.02300062824368796, "acc_norm": 0.28974358974358977, "acc_norm_stderr": 0.02300062824368796 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.22592592592592592, "acc_stderr": 0.02549753263960955, "acc_norm": 0.22592592592592592, "acc_norm_stderr": 0.02549753263960955 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.2773109243697479, "acc_stderr": 0.029079374539480007, "acc_norm": 0.2773109243697479, "acc_norm_stderr": 0.029079374539480007 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.25137614678899084, "acc_stderr": 0.018599206360287415, "acc_norm": 0.25137614678899084, "acc_norm_stderr": 0.018599206360287415 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.17592592592592593, "acc_stderr": 0.025967420958258533, "acc_norm": 0.17592592592592593, "acc_norm_stderr": 0.025967420958258533 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.22058823529411764, "acc_stderr": 0.029102254389674082, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.029102254389674082 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.2616033755274262, "acc_stderr": 0.028609516716994934, "acc_norm": 0.2616033755274262, "acc_norm_stderr": 0.028609516716994934 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.37668161434977576, "acc_stderr": 0.03252113489929188, "acc_norm": 0.37668161434977576, "acc_norm_stderr": 0.03252113489929188 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.25190839694656486, "acc_stderr": 0.03807387116306086, "acc_norm": 0.25190839694656486, "acc_norm_stderr": 0.03807387116306086 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.35537190082644626, "acc_stderr": 0.04369236326573981, "acc_norm": 0.35537190082644626, "acc_norm_stderr": 0.04369236326573981 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.04414343666854933, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.04414343666854933 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.2331288343558282, "acc_stderr": 0.0332201579577674, "acc_norm": 0.2331288343558282, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.2767857142857143, "acc_stderr": 0.042466243366976256, "acc_norm": 0.2767857142857143, "acc_norm_stderr": 0.042466243366976256 }, "harness\|hendrycksTest-management\|5": { "acc": 0.32038834951456313, "acc_stderr": 0.04620284082280039, "acc_norm": 0.32038834951456313, "acc_norm_stderr": 0.04620284082280039 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.029343114798094476, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.029343114798094476 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.28607918263090676, "acc_stderr": 0.016160871405127532, "acc_norm": 0.28607918263090676, "acc_norm_stderr": 0.016160871405127532 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.25722543352601157, "acc_stderr": 0.023532925431044287, "acc_norm": 0.25722543352601157, "acc_norm_stderr": 0.023532925431044287 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.3006535947712418, "acc_stderr": 0.02625605383571896, "acc_norm": 0.3006535947712418, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.27009646302250806, "acc_stderr": 0.025218040373410622, "acc_norm": 0.27009646302250806, "acc_norm_stderr": 0.025218040373410622 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.29012345679012347, "acc_stderr": 0.025251173936495022, "acc_norm": 0.29012345679012347, "acc_norm_stderr": 0.025251173936495022 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843017, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843017 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.24185136897001303, "acc_stderr": 0.010936550813827065, "acc_norm": 0.24185136897001303, "acc_norm_stderr": 0.010936550813827065 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.22794117647058823, "acc_stderr": 0.025483081468029804, "acc_norm": 0.22794117647058823, "acc_norm_stderr": 0.025483081468029804 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2630718954248366, "acc_stderr": 0.017812676542320657, "acc_norm": 0.2630718954248366, "acc_norm_stderr": 0.017812676542320657 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.33636363636363636, "acc_stderr": 0.04525393596302505, "acc_norm": 0.33636363636363636, "acc_norm_stderr": 0.04525393596302505 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.33877551020408164, "acc_stderr": 0.030299506562154185, "acc_norm": 0.33877551020408164, "acc_norm_stderr": 0.030299506562154185 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3253012048192771, "acc_stderr": 0.03647168523683227, "acc_norm": 0.3253012048192771, "acc_norm_stderr": 0.03647168523683227 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.3508771929824561, "acc_stderr": 0.03660298834049163, "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.03660298834049163 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.3550624387136162, "mc2_stderr": 0.01364292328900912 }, "harness\|winogrande\|5": { "acc": 0.6495659037095501, "acc_stderr": 0.013409047676670184 }, "harness\|gsm8k\|5": { "acc": 0.037149355572403335, "acc_stderr": 0.0052095162830737675 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_aloobun__open-llama-3b-v2-elmv3	[ "region:us" ]	2023-12-09T17:20:39+00:00	{"pretty_name": "Evaluation run of aloobun/open-llama-3b-v2-elmv3", "dataset_summary": "Dataset automatically created during the evaluation run of model [aloobun/open-llama-3b-v2-elmv3](https://huggingface.co/aloobun/open-llama-3b-v2-elmv3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_aloobun__open-llama-3b-v2-elmv3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T18:25:59.224844](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__open-llama-3b-v2-elmv3/blob/main/results_2023-12-09T18-25-59.224844.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2804692579613333,\n \"acc_stderr\": 0.03160774886030324,\n \"acc_norm\": 0.28199113779250456,\n \"acc_norm_stderr\": 0.0323576565422058,\n \"mc1\": 0.22888616891064872,\n \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.3550624387136162,\n \"mc2_stderr\": 0.01364292328900912\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.3873720136518771,\n \"acc_stderr\": 0.014235872487909874,\n \"acc_norm\": 0.42150170648464164,\n \"acc_norm_stderr\": 0.014430197069326023\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.551185022903804,\n \"acc_stderr\": 0.004963567029129055,\n \"acc_norm\": 0.7326229834694284,\n \"acc_norm_stderr\": 0.004416861919100999\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3111111111111111,\n \"acc_stderr\": 0.03999262876617721,\n \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.03999262876617721\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.03690677986137282,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.03690677986137282\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.2943396226415094,\n \"acc_stderr\": 0.028049186315695248,\n \"acc_norm\": 0.2943396226415094,\n \"acc_norm_stderr\": 0.028049186315695248\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.24305555555555555,\n \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.033450369167889925,\n \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.033450369167889925\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307811,\n \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307811\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.32340425531914896,\n \"acc_stderr\": 0.030579442773610334,\n \"acc_norm\": 0.32340425531914896,\n \"acc_norm_stderr\": 0.030579442773610334\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.04096985139843673,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.04096985139843673\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.21379310344827587,\n \"acc_stderr\": 0.03416520447747549,\n \"acc_norm\": 0.21379310344827587,\n \"acc_norm_stderr\": 0.03416520447747549\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.2804232804232804,\n \"acc_stderr\": 0.023135287974325628,\n \"acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.023135287974325628\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 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["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2023-12-09T18-25-59.224844.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T17_18_30.999840", "path": ["results_2023-12-09T17-18-30.999840.parquet"]}, {"split": "2023_12_09T18_25_59.224844", "path": ["results_2023-12-09T18-25-59.224844.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T18-25-59.224844.parquet"]}]}]}	2023-12-09T18:28:56+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of aloobun/open-llama-3b-v2-elmv3 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model aloobun/open-llama-3b-v2-elmv3 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T18:25:59.224844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of aloobun/open-llama-3b-v2-elmv3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model aloobun/open-llama-3b-v2-elmv3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:25:59.224844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of aloobun/open-llama-3b-v2-elmv3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model aloobun/open-llama-3b-v2-elmv3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:25:59.224844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 25, 31, 174, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of aloobun/open-llama-3b-v2-elmv3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model aloobun/open-llama-3b-v2-elmv3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T18:25:59.224844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
ec601e6256d2cf3b165370b2377774952bbf7738	# Dataset Card for Evaluation run of yyjjtt/test-model ## Dataset Description - Homepage: - Repository: https://huggingface.co/yyjjtt/test-model - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [yyjjtt/test-model](https://huggingface.co/yyjjtt/test-model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_yyjjtt__test-model", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:29:33.707881](https://huggingface.co/datasets/open-llm-leaderboard/details_yyjjtt__test-model/blob/main/results_2023-12-09T17-29-33.707881.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.2582521615185269, "acc_stderr": 0.030847868754913528, "acc_norm": 0.2593091182470286, "acc_norm_stderr": 0.03166965639566685, "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015025, "mc2": 0.44593057174416123, "mc2_stderr": 0.015586502428911173 }, "harness\|arc:challenge\|25": { "acc": 0.20392491467576793, "acc_stderr": 0.011774262478702247, "acc_norm": 0.2440273037542662, "acc_norm_stderr": 0.012551447627856262 }, "harness\|hellaswag\|10": { "acc": 0.2876916948814977, "acc_stderr": 0.004517614647703248, "acc_norm": 0.30173272256522604, "acc_norm_stderr": 0.0045807181159925135 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.039725528847851375, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.039725528847851375 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.21710526315789475, "acc_stderr": 0.03355045304882921, "acc_norm": 0.21710526315789475, "acc_norm_stderr": 0.03355045304882921 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.26037735849056604, "acc_stderr": 0.027008766090708083, "acc_norm": 0.26037735849056604, "acc_norm_stderr": 0.027008766090708083 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2361111111111111, "acc_stderr": 0.03551446610810826, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.16, "acc_stderr": 0.0368452949177471, "acc_norm": 0.16, "acc_norm_stderr": 0.0368452949177471 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.18497109826589594, "acc_stderr": 0.02960562398177122, "acc_norm": 0.18497109826589594, "acc_norm_stderr": 0.02960562398177122 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.18627450980392157, "acc_stderr": 0.03873958714149351, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.03873958714149351 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.30638297872340425, "acc_stderr": 0.030135906478517563, "acc_norm": 0.30638297872340425, "acc_norm_stderr": 0.030135906478517563 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2689655172413793, "acc_stderr": 0.036951833116502325, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.036951833116502325 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.21428571428571427, "acc_stderr": 0.03670066451047182, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.03670066451047182 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.2709677419354839, "acc_stderr": 0.02528441611490016, "acc_norm": 0.2709677419354839, "acc_norm_stderr": 0.02528441611490016 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.31527093596059114, "acc_stderr": 0.03269080871970186, "acc_norm": 0.31527093596059114, "acc_norm_stderr": 0.03269080871970186 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2727272727272727, "acc_stderr": 0.03477691162163659, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.03477691162163659 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.23737373737373738, "acc_stderr": 0.030313710538198913, "acc_norm": 0.23737373737373738, "acc_norm_stderr": 0.030313710538198913 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.32124352331606215, "acc_stderr": 0.033699508685490674, "acc_norm": 0.32124352331606215, "acc_norm_stderr": 0.033699508685490674 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.23076923076923078, "acc_stderr": 0.02136202772522273, "acc_norm": 0.23076923076923078, "acc_norm_stderr": 0.02136202772522273 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.26666666666666666, "acc_stderr": 0.02696242432507384, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.02696242432507384 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.2184873949579832, "acc_stderr": 0.026841514322958945, "acc_norm": 0.2184873949579832, "acc_norm_stderr": 0.026841514322958945 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2185430463576159, "acc_stderr": 0.03374235550425694, "acc_norm": 0.2185430463576159, "acc_norm_stderr": 0.03374235550425694 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.3192660550458716, "acc_stderr": 0.01998782906975001, "acc_norm": 0.3192660550458716, "acc_norm_stderr": 0.01998782906975001 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.030190282453501967, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.030190282453501967 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.26582278481012656, "acc_stderr": 0.02875679962965834, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.02875679962965834 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.3004484304932735, "acc_stderr": 0.030769352008229136, "acc_norm": 0.3004484304932735, "acc_norm_stderr": 0.030769352008229136 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.22900763358778625, "acc_stderr": 0.036853466317118506, "acc_norm": 0.22900763358778625, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.256198347107438, "acc_stderr": 0.03984979653302872, "acc_norm": 0.256198347107438, "acc_norm_stderr": 0.03984979653302872 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.19444444444444445, "acc_stderr": 0.03826076324884864, "acc_norm": 0.19444444444444445, "acc_norm_stderr": 0.03826076324884864 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.25766871165644173, "acc_stderr": 0.03436150827846917, "acc_norm": 0.25766871165644173, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.042878587513404544, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.042878587513404544 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2815533980582524, "acc_stderr": 0.044532548363264673, "acc_norm": 0.2815533980582524, "acc_norm_stderr": 0.044532548363264673 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.20085470085470086, "acc_stderr": 0.02624677294689048, "acc_norm": 0.20085470085470086, "acc_norm_stderr": 0.02624677294689048 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.28735632183908044, "acc_stderr": 0.0161824107306827, "acc_norm": 0.28735632183908044, "acc_norm_stderr": 0.0161824107306827 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.23699421965317918, "acc_stderr": 0.022894082489925992, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.022894082489925992 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808835, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808835 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.23529411764705882, "acc_stderr": 0.024288619466046105, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.024288619466046105 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.24437299035369775, "acc_stderr": 0.024406162094668907, "acc_norm": 0.24437299035369775, "acc_norm_stderr": 0.024406162094668907 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.02438366553103545, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.25886524822695034, "acc_stderr": 0.026129572527180848, "acc_norm": 0.25886524822695034, "acc_norm_stderr": 0.026129572527180848 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.23272490221642764, "acc_stderr": 0.010792595553888496, "acc_norm": 0.23272490221642764, "acc_norm_stderr": 0.010792595553888496 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4264705882352941, "acc_stderr": 0.030042615832714857, "acc_norm": 0.4264705882352941, "acc_norm_stderr": 0.030042615832714857 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.01663931935031326, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.01663931935031326 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.19090909090909092, "acc_stderr": 0.03764425585984925, "acc_norm": 0.19090909090909092, "acc_norm_stderr": 0.03764425585984925 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.19591836734693877, "acc_stderr": 0.02540930195322568, "acc_norm": 0.19591836734693877, "acc_norm_stderr": 0.02540930195322568 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3433734939759036, "acc_stderr": 0.03696584317010601, "acc_norm": 0.3433734939759036, "acc_norm_stderr": 0.03696584317010601 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.2046783625730994, "acc_stderr": 0.03094445977853321, "acc_norm": 0.2046783625730994, "acc_norm_stderr": 0.03094445977853321 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015025, "mc2": 0.44593057174416123, "mc2_stderr": 0.015586502428911173 }, "harness\|winogrande\|5": { "acc": 0.5082872928176796, "acc_stderr": 0.014050555322824189 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_yyjjtt__test-model	[ "region:us" ]	2023-12-09T17:32:32+00:00	{"pretty_name": "Evaluation run of yyjjtt/test-model", "dataset_summary": "Dataset automatically created during the evaluation run of model [yyjjtt/test-model](https://huggingface.co/yyjjtt/test-model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_yyjjtt__test-model\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:29:33.707881](https://huggingface.co/datasets/open-llm-leaderboard/details_yyjjtt__test-model/blob/main/results_2023-12-09T17-29-33.707881.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2582521615185269,\n \"acc_stderr\": 0.030847868754913528,\n \"acc_norm\": 0.2593091182470286,\n \"acc_norm_stderr\": 0.03166965639566685,\n \"mc1\": 0.26193390452876375,\n \"mc1_stderr\": 0.015392118805015025,\n \"mc2\": 0.44593057174416123,\n \"mc2_stderr\": 0.015586502428911173\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.20392491467576793,\n \"acc_stderr\": 0.011774262478702247,\n \"acc_norm\": 0.2440273037542662,\n \"acc_norm_stderr\": 0.012551447627856262\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.2876916948814977,\n \"acc_stderr\": 0.004517614647703248,\n \"acc_norm\": 0.30173272256522604,\n \"acc_norm_stderr\": 0.0045807181159925135\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3037037037037037,\n \"acc_stderr\": 0.039725528847851375,\n \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.039725528847851375\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.21710526315789475,\n \"acc_stderr\": 0.03355045304882921,\n \"acc_norm\": 0.21710526315789475,\n \"acc_norm_stderr\": 0.03355045304882921\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.26037735849056604,\n \"acc_stderr\": 0.027008766090708083,\n \"acc_norm\": 0.26037735849056604,\n \"acc_norm_stderr\": 0.027008766090708083\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2361111111111111,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.16,\n \"acc_stderr\": 0.0368452949177471,\n \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.0368452949177471\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.02960562398177122,\n \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.02960562398177122\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.03873958714149351,\n \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.03873958714149351\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.030135906478517563,\n \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.030135906478517563\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.03670066451047182,\n \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.03670066451047182\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.2709677419354839,\n \"acc_stderr\": 0.02528441611490016,\n \"acc_norm\": 0.2709677419354839,\n \"acc_norm_stderr\": 0.02528441611490016\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.31527093596059114,\n \"acc_stderr\": 0.03269080871970186,\n \"acc_norm\": 0.31527093596059114,\n \"acc_norm_stderr\": 0.03269080871970186\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.2727272727272727,\n \"acc_stderr\": 0.03477691162163659,\n \"acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.03477691162163659\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.23737373737373738,\n \"acc_stderr\": 0.030313710538198913,\n \"acc_norm\": 0.23737373737373738,\n \"acc_norm_stderr\": 0.030313710538198913\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.32124352331606215,\n \"acc_stderr\": 0.033699508685490674,\n \"acc_norm\": 0.32124352331606215,\n \"acc_norm_stderr\": 0.033699508685490674\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.23076923076923078,\n \"acc_stderr\": 0.02136202772522273,\n \"acc_norm\": 0.23076923076923078,\n \"acc_norm_stderr\": 0.02136202772522273\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507384,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507384\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.2184873949579832,\n \"acc_stderr\": 0.026841514322958945,\n \"acc_norm\": 0.2184873949579832,\n \"acc_norm_stderr\": 0.026841514322958945\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.3192660550458716,\n \"acc_stderr\": 0.01998782906975001,\n \"acc_norm\": 0.3192660550458716,\n \"acc_norm_stderr\": 0.01998782906975001\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.030190282453501967,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.030190282453501967\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.26582278481012656,\n \"acc_stderr\": 0.02875679962965834,\n \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.02875679962965834\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.3004484304932735,\n \"acc_stderr\": 0.030769352008229136,\n \"acc_norm\": 0.3004484304932735,\n \"acc_norm_stderr\": 0.030769352008229136\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.256198347107438,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\": 0.256198347107438,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.19444444444444445,\n \"acc_stderr\": 0.03826076324884864,\n \"acc_norm\": 0.19444444444444445,\n \"acc_norm_stderr\": 0.03826076324884864\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.042878587513404544,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.042878587513404544\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.2815533980582524,\n \"acc_stderr\": 0.044532548363264673,\n \"acc_norm\": 0.2815533980582524,\n \"acc_norm_stderr\": 0.044532548363264673\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.20085470085470086,\n \"acc_stderr\": 0.02624677294689048,\n \"acc_norm\": 0.20085470085470086,\n \"acc_norm_stderr\": 0.02624677294689048\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.28735632183908044,\n \"acc_stderr\": 0.0161824107306827,\n \"acc_norm\": 0.28735632183908044,\n \"acc_norm_stderr\": 0.0161824107306827\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.022894082489925992,\n \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.022894082489925992\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24692737430167597,\n \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.024288619466046105,\n \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.024288619466046105\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.24437299035369775,\n \"acc_stderr\": 0.024406162094668907,\n \"acc_norm\": 0.24437299035369775,\n \"acc_norm_stderr\": 0.024406162094668907\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.02438366553103545,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02438366553103545\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180848,\n \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180848\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.23272490221642764,\n \"acc_stderr\": 0.010792595553888496,\n \"acc_norm\": 0.23272490221642764,\n \"acc_norm_stderr\": 0.010792595553888496\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n 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yyjjtt/test-model on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:29:33.707881(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of yyjjtt/test-model", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model yyjjtt/test-model on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:29:33.707881(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of yyjjtt/test-model", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model yyjjtt/test-model on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:29:33.707881(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 17, 31, 166, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of yyjjtt/test-model## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model yyjjtt/test-model on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:29:33.707881(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
76adcae79aaf7dcb15551fe88274c600f1e2b55a	# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-7B-v2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-7B-v2](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:37:34.905167](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v2/blob/main/results_2023-12-09T17-37-34.905167.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6381884045265307, "acc_stderr": 0.03230282577031498, "acc_norm": 0.6386105198682612, "acc_norm_stderr": 0.03296099199433783, "mc1": 0.4724602203182375, "mc1_stderr": 0.017476930190712187, "mc2": 0.6195852908845921, "mc2_stderr": 0.015562566424717855 }, "harness\|arc:challenge\|25": { "acc": 0.659556313993174, "acc_stderr": 0.013847460518892978, "acc_norm": 0.6825938566552902, "acc_norm_stderr": 0.013602239088038167 }, "harness\|hellaswag\|10": { "acc": 0.6810396335391357, "acc_stderr": 0.00465121131163384, "acc_norm": 0.8626767576180043, "acc_norm_stderr": 0.0034348485253881847 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404907, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404907 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7676767676767676, "acc_stderr": 0.03008862949021749, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.03008862949021749 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812143, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812143 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6307692307692307, "acc_stderr": 0.024468615241478926, "acc_norm": 0.6307692307692307, "acc_norm_stderr": 0.024468615241478926 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.818348623853211, "acc_stderr": 0.016530617409266857, "acc_norm": 0.818348623853211, "acc_norm_stderr": 0.016530617409266857 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8088235294117647, "acc_stderr": 0.02759917430064076, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.02759917430064076 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313729, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313729 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8098159509202454, "acc_stderr": 0.030833491146281235, "acc_norm": 0.8098159509202454, "acc_norm_stderr": 0.030833491146281235 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165612, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165612 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577612, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577612 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3877094972067039, "acc_stderr": 0.016295332328155814, "acc_norm": 0.3877094972067039, "acc_norm_stderr": 0.016295332328155814 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7124183006535948, "acc_stderr": 0.025917806117147158, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.025917806117147158 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765137, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765137 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.43617021276595747, "acc_stderr": 0.029583452036284066, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.029583452036284066 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4595827900912647, "acc_stderr": 0.012728446067669968, "acc_norm": 0.4595827900912647, "acc_norm_stderr": 0.012728446067669968 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.018926082916083383, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.018926082916083383 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7510204081632653, "acc_stderr": 0.027682979522960234, "acc_norm": 0.7510204081632653, "acc_norm_stderr": 0.027682979522960234 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4724602203182375, "mc1_stderr": 0.017476930190712187, "mc2": 0.6195852908845921, "mc2_stderr": 0.015562566424717855 }, "harness\|winogrande\|5": { "acc": 0.8011049723756906, "acc_stderr": 0.01121862997251532 }, "harness\|gsm8k\|5": { "acc": 0.6550416982562547, "acc_stderr": 0.013093630133666247 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v2	[ "region:us" ]	2023-12-09T17:40:28+00:00	{"pretty_name": "Evaluation run of AIDC-ai-business/Marcoroni-7B-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-7B-v2](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:37:34.905167](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-7B-v2/blob/main/results_2023-12-09T17-37-34.905167.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6381884045265307,\n \"acc_stderr\": 0.03230282577031498,\n \"acc_norm\": 0.6386105198682612,\n \"acc_norm_stderr\": 0.03296099199433783,\n \"mc1\": 0.4724602203182375,\n \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6195852908845921,\n \"mc2_stderr\": 0.015562566424717855\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.659556313993174,\n \"acc_stderr\": 0.013847460518892978,\n \"acc_norm\": 0.6825938566552902,\n \"acc_norm_stderr\": 0.013602239088038167\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6810396335391357,\n \"acc_stderr\": 0.00465121131163384,\n \"acc_norm\": 0.8626767576180043,\n \"acc_norm_stderr\": 0.0034348485253881847\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404907,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404907\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.03008862949021749,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.03008862949021749\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812143,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812143\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6307692307692307,\n \"acc_stderr\": 0.024468615241478926,\n \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.024468615241478926\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.818348623853211,\n \"acc_stderr\": 0.016530617409266857,\n \"acc_norm\": 0.818348623853211,\n \"acc_norm_stderr\": 0.016530617409266857\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.02759917430064076,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.02759917430064076\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.030833491146281235,\n \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.030833491146281235\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8314176245210728,\n \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577612,\n \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577612\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3877094972067039,\n \"acc_stderr\": 0.016295332328155814,\n \"acc_norm\": 0.3877094972067039,\n \"acc_norm_stderr\": 0.016295332328155814\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.025917806117147158,\n \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.025917806117147158\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765137,\n \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765137\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.43617021276595747,\n \"acc_stderr\": 0.029583452036284066,\n \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.029583452036284066\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4595827900912647,\n \"acc_stderr\": 0.012728446067669968,\n \"acc_norm\": 0.4595827900912647,\n \"acc_norm_stderr\": 0.012728446067669968\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960234,\n \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960234\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4724602203182375,\n \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6195852908845921,\n \"mc2_stderr\": 0.015562566424717855\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8011049723756906,\n \"acc_stderr\": 0.01121862997251532\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6550416982562547,\n \"acc_stderr\": 0.013093630133666247\n }\n}\n```", "repo_url": "https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T17-37-34.905167.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T17-37-34.905167.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-37-34.905167.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T17_37_34.905167", "path": ["results_2023-12-09T17-37-34.905167.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T17-37-34.905167.parquet"]}]}]}	2023-12-09T17:41:11+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-7B-v2 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-7B-v2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:37:34.905167(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-7B-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-7B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:37:34.905167(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-7B-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-7B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:37:34.905167(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 24, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-7B-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-7B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:37:34.905167(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
408f4d882191c2d72774f6e56300527ec4d4a2b2	# Dataset Card for Evaluation run of itsliupeng/openllama-7b-base ## Dataset Description - Homepage: - Repository: https://huggingface.co/itsliupeng/openllama-7b-base - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [itsliupeng/openllama-7b-base](https://huggingface.co/itsliupeng/openllama-7b-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_itsliupeng__openllama-7b-base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:41:52.346369](https://huggingface.co/datasets/open-llm-leaderboard/details_itsliupeng__openllama-7b-base/blob/main/results_2023-12-09T17-41-52.346369.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.42989152566033884, "acc_stderr": 0.03449698744058074, "acc_norm": 0.43443471590575655, "acc_norm_stderr": 0.03530126937236681, "mc1": 0.23133414932680538, "mc1_stderr": 0.014761945174862677, "mc2": 0.3664912047351792, "mc2_stderr": 0.01364656500793206 }, "harness\|arc:challenge\|25": { "acc": 0.44197952218430037, "acc_stderr": 0.014512682523128343, "acc_norm": 0.4616040955631399, "acc_norm_stderr": 0.01456824555029636 }, "harness\|hellaswag\|10": { "acc": 0.5703047201752639, "acc_stderr": 0.004940208641372079, "acc_norm": 0.7639912368054173, "acc_norm_stderr": 0.0042375981420072475 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421296, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421296 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.43703703703703706, "acc_stderr": 0.042849586397533994, "acc_norm": 0.43703703703703706, "acc_norm_stderr": 0.042849586397533994 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.42105263157894735, "acc_stderr": 0.04017901275981749, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.04017901275981749 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4830188679245283, "acc_stderr": 0.030755120364119905, "acc_norm": 0.4830188679245283, "acc_norm_stderr": 0.030755120364119905 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.04174752578923185, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.04174752578923185 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3930635838150289, "acc_stderr": 0.03724249595817729, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.03724249595817729 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.039505818611799616, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.039505818611799616 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3702127659574468, "acc_stderr": 0.03156564682236784, "acc_norm": 0.3702127659574468, "acc_norm_stderr": 0.03156564682236784 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.30701754385964913, "acc_stderr": 0.04339138322579861, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.04339138322579861 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.041618085035015295, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.28835978835978837, "acc_stderr": 0.0233306540545359, "acc_norm": 0.28835978835978837, "acc_norm_stderr": 0.0233306540545359 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2698412698412698, "acc_stderr": 0.03970158273235172, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.03970158273235172 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.45806451612903226, "acc_stderr": 0.028343787250540618, "acc_norm": 0.45806451612903226, "acc_norm_stderr": 0.028343787250540618 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3103448275862069, "acc_stderr": 0.03255086769970103, "acc_norm": 0.3103448275862069, "acc_norm_stderr": 0.03255086769970103 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.42, "acc_stderr": 0.04960449637488584, "acc_norm": 0.42, "acc_norm_stderr": 0.04960449637488584 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.4909090909090909, "acc_stderr": 0.0390369864774844, "acc_norm": 0.4909090909090909, "acc_norm_stderr": 0.0390369864774844 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.4898989898989899, "acc_stderr": 0.035616254886737454, "acc_norm": 0.4898989898989899, "acc_norm_stderr": 0.035616254886737454 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6321243523316062, "acc_stderr": 0.034801756684660366, "acc_norm": 0.6321243523316062, "acc_norm_stderr": 0.034801756684660366 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4076923076923077, "acc_stderr": 0.024915243985987837, "acc_norm": 0.4076923076923077, "acc_norm_stderr": 0.024915243985987837 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712163, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712163 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.36134453781512604, "acc_stderr": 0.031204691225150013, "acc_norm": 0.36134453781512604, "acc_norm_stderr": 0.031204691225150013 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.271523178807947, "acc_stderr": 0.03631329803969653, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.03631329803969653 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5614678899082569, "acc_stderr": 0.021274713073954565, "acc_norm": 0.5614678899082569, "acc_norm_stderr": 0.021274713073954565 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3148148148148148, "acc_stderr": 0.0316746870682898, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.0316746870682898 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.45098039215686275, "acc_stderr": 0.03492406104163613, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.03492406104163613 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5569620253164557, "acc_stderr": 0.032335327775334835, "acc_norm": 0.5569620253164557, "acc_norm_stderr": 0.032335327775334835 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.4260089686098655, "acc_stderr": 0.033188332862172806, "acc_norm": 0.4260089686098655, "acc_norm_stderr": 0.033188332862172806 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.48854961832061067, "acc_stderr": 0.043841400240780176, "acc_norm": 0.48854961832061067, "acc_norm_stderr": 0.043841400240780176 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5537190082644629, "acc_stderr": 0.0453793517794788, "acc_norm": 0.5537190082644629, "acc_norm_stderr": 0.0453793517794788 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.04830366024635331, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.04830366024635331 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.48466257668711654, "acc_stderr": 0.039265223787088424, "acc_norm": 0.48466257668711654, "acc_norm_stderr": 0.039265223787088424 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5242718446601942, "acc_stderr": 0.049449010929737795, "acc_norm": 0.5242718446601942, "acc_norm_stderr": 0.049449010929737795 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.03193705726200293, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.03193705726200293 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.017570705239256558, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.017570705239256558 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4884393063583815, "acc_stderr": 0.02691189868637792, "acc_norm": 0.4884393063583815, "acc_norm_stderr": 0.02691189868637792 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2435754189944134, "acc_stderr": 0.01435591196476786, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.01435591196476786 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.477124183006536, "acc_stderr": 0.028599936776089786, "acc_norm": 0.477124183006536, "acc_norm_stderr": 0.028599936776089786 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.45980707395498394, "acc_stderr": 0.028306190403305693, "acc_norm": 0.45980707395498394, "acc_norm_stderr": 0.028306190403305693 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4876543209876543, "acc_stderr": 0.027812262269327242, "acc_norm": 0.4876543209876543, "acc_norm_stderr": 0.027812262269327242 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3546099290780142, "acc_stderr": 0.02853865002887864, "acc_norm": 0.3546099290780142, "acc_norm_stderr": 0.02853865002887864 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3389830508474576, "acc_stderr": 0.012089941857584477, "acc_norm": 0.3389830508474576, "acc_norm_stderr": 0.012089941857584477 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.41544117647058826, "acc_stderr": 0.02993534270787775, "acc_norm": 0.41544117647058826, "acc_norm_stderr": 0.02993534270787775 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4133986928104575, "acc_stderr": 0.019922115682786682, "acc_norm": 0.4133986928104575, "acc_norm_stderr": 0.019922115682786682 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5, "acc_stderr": 0.04789131426105757, "acc_norm": 0.5, "acc_norm_stderr": 0.04789131426105757 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.45714285714285713, "acc_stderr": 0.031891418324213966, "acc_norm": 0.45714285714285713, "acc_norm_stderr": 0.031891418324213966 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5771144278606966, "acc_stderr": 0.034932317774212816, "acc_norm": 0.5771144278606966, "acc_norm_stderr": 0.034932317774212816 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3614457831325301, "acc_stderr": 0.037400593820293204, "acc_norm": 0.3614457831325301, "acc_norm_stderr": 0.037400593820293204 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.5964912280701754, "acc_stderr": 0.03762738699917057, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.03762738699917057 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23133414932680538, "mc1_stderr": 0.014761945174862677, "mc2": 0.3664912047351792, "mc2_stderr": 0.01364656500793206 }, "harness\|winogrande\|5": { "acc": 0.7087608524072613, "acc_stderr": 0.012769029305370702 }, "harness\|gsm8k\|5": { "acc": 0.09628506444275967, "acc_stderr": 0.008125264128215908 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_itsliupeng__openllama-7b-base	[ "region:us" ]	2023-12-09T17:44:02+00:00	{"pretty_name": "Evaluation run of itsliupeng/openllama-7b-base", "dataset_summary": "Dataset automatically created during the evaluation run of model [itsliupeng/openllama-7b-base](https://huggingface.co/itsliupeng/openllama-7b-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_itsliupeng__openllama-7b-base\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:41:52.346369](https://huggingface.co/datasets/open-llm-leaderboard/details_itsliupeng__openllama-7b-base/blob/main/results_2023-12-09T17-41-52.346369.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.42989152566033884,\n \"acc_stderr\": 0.03449698744058074,\n \"acc_norm\": 0.43443471590575655,\n \"acc_norm_stderr\": 0.03530126937236681,\n \"mc1\": 0.23133414932680538,\n \"mc1_stderr\": 0.014761945174862677,\n \"mc2\": 0.3664912047351792,\n \"mc2_stderr\": 0.01364656500793206\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.44197952218430037,\n \"acc_stderr\": 0.014512682523128343,\n \"acc_norm\": 0.4616040955631399,\n \"acc_norm_stderr\": 0.01456824555029636\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5703047201752639,\n \"acc_stderr\": 0.004940208641372079,\n \"acc_norm\": 0.7639912368054173,\n \"acc_norm_stderr\": 0.0042375981420072475\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421296,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421296\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.042849586397533994,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 0.042849586397533994\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.04017901275981749,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.04017901275981749\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.4830188679245283,\n \"acc_stderr\": 0.030755120364119905,\n \"acc_norm\": 0.4830188679245283,\n \"acc_norm_stderr\": 0.030755120364119905\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.04174752578923185,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.04174752578923185\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3930635838150289,\n \"acc_stderr\": 0.03724249595817729,\n \"acc_norm\": 0.3930635838150289,\n \"acc_norm_stderr\": 0.03724249595817729\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.039505818611799616,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.039505818611799616\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.3702127659574468,\n \"acc_stderr\": 0.03156564682236784,\n \"acc_norm\": 0.3702127659574468,\n \"acc_norm_stderr\": 0.03156564682236784\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.30701754385964913,\n \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.28835978835978837,\n \"acc_stderr\": 0.0233306540545359,\n \"acc_norm\": 0.28835978835978837,\n \"acc_norm_stderr\": 0.0233306540545359\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2698412698412698,\n \"acc_stderr\": 0.03970158273235172,\n \"acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.03970158273235172\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.45806451612903226,\n \"acc_stderr\": 0.028343787250540618,\n \"acc_norm\": 0.45806451612903226,\n \"acc_norm_stderr\": 0.028343787250540618\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.3103448275862069,\n \"acc_stderr\": 0.03255086769970103,\n \"acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03255086769970103\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.4909090909090909,\n \"acc_stderr\": 0.0390369864774844,\n \"acc_norm\": 0.4909090909090909,\n \"acc_norm_stderr\": 0.0390369864774844\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.4898989898989899,\n \"acc_stderr\": 0.035616254886737454,\n \"acc_norm\": 0.4898989898989899,\n \"acc_norm_stderr\": 0.035616254886737454\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.6321243523316062,\n \"acc_stderr\": 0.034801756684660366,\n \"acc_norm\": 0.6321243523316062,\n \"acc_norm_stderr\": 0.034801756684660366\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.4076923076923077,\n \"acc_stderr\": 0.024915243985987837,\n \"acc_norm\": 0.4076923076923077,\n \"acc_norm_stderr\": 0.024915243985987837\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.25925925925925924,\n 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"latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-41-52.346369.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T17_41_52.346369", "path": ["results_2023-12-09T17-41-52.346369.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T17-41-52.346369.parquet"]}]}]}	2023-12-09T17:44:46+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of itsliupeng/openllama-7b-base ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model itsliupeng/openllama-7b-base on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:41:52.346369(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of itsliupeng/openllama-7b-base", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model itsliupeng/openllama-7b-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:41:52.346369(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of itsliupeng/openllama-7b-base", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model itsliupeng/openllama-7b-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:41:52.346369(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 20, 31, 169, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of itsliupeng/openllama-7b-base## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model itsliupeng/openllama-7b-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:41:52.346369(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
e1bf464cf8dc377e784dba4b0fc84b0165c95975	# Dataset Card for Evaluation run of itsliupeng/openllama-7b-icl ## Dataset Description - Homepage: - Repository: https://huggingface.co/itsliupeng/openllama-7b-icl - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [itsliupeng/openllama-7b-icl](https://huggingface.co/itsliupeng/openllama-7b-icl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_itsliupeng__openllama-7b-icl", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:48:05.024924](https://huggingface.co/datasets/open-llm-leaderboard/details_itsliupeng__openllama-7b-icl/blob/main/results_2023-12-09T17-48-05.024924.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.44441569324312047, "acc_stderr": 0.03430171403503658, "acc_norm": 0.4497976132436587, "acc_norm_stderr": 0.035089311320789345, "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041836, "mc2": 0.3706359177223847, "mc2_stderr": 0.01391522805511699 }, "harness\|arc:challenge\|25": { "acc": 0.44197952218430037, "acc_stderr": 0.014512682523128345, "acc_norm": 0.47952218430034127, "acc_norm_stderr": 0.014599131353035007 }, "harness\|hellaswag\|10": { "acc": 0.5676160127464649, "acc_stderr": 0.0049439450696114546, "acc_norm": 0.7703644692292372, "acc_norm_stderr": 0.004197388626940065 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.46710526315789475, "acc_stderr": 0.040601270352363966, "acc_norm": 0.46710526315789475, "acc_norm_stderr": 0.040601270352363966 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4867924528301887, "acc_stderr": 0.030762134874500482, "acc_norm": 0.4867924528301887, "acc_norm_stderr": 0.030762134874500482 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4930555555555556, "acc_stderr": 0.04180806750294938, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.04180806750294938 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.47398843930635837, "acc_stderr": 0.038073017265045105, "acc_norm": 0.47398843930635837, "acc_norm_stderr": 0.038073017265045105 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.63, "acc_stderr": 0.048523658709390974, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709390974 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.34893617021276596, "acc_stderr": 0.03115852213135778, "acc_norm": 0.34893617021276596, "acc_norm_stderr": 0.03115852213135778 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192118, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192118 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2804232804232804, "acc_stderr": 0.02313528797432563, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.02313528797432563 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.1984126984126984, "acc_stderr": 0.03567016675276864, "acc_norm": 0.1984126984126984, "acc_norm_stderr": 0.03567016675276864 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.45161290322580644, "acc_stderr": 0.028310500348568392, "acc_norm": 0.45161290322580644, "acc_norm_stderr": 0.028310500348568392 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.33497536945812806, "acc_stderr": 0.033208527423483104, "acc_norm": 0.33497536945812806, "acc_norm_stderr": 0.033208527423483104 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.509090909090909, "acc_stderr": 0.0390369864774844, "acc_norm": 0.509090909090909, "acc_norm_stderr": 0.0390369864774844 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5202020202020202, "acc_stderr": 0.03559443565563918, "acc_norm": 0.5202020202020202, "acc_norm_stderr": 0.03559443565563918 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.616580310880829, "acc_stderr": 0.03508984236295342, "acc_norm": 0.616580310880829, "acc_norm_stderr": 0.03508984236295342 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4, "acc_stderr": 0.024838811988033158, "acc_norm": 0.4, "acc_norm_stderr": 0.024838811988033158 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.02534809746809784, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.02534809746809784 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.3487394957983193, "acc_stderr": 0.030956636328566548, "acc_norm": 0.3487394957983193, "acc_norm_stderr": 0.030956636328566548 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6110091743119266, "acc_stderr": 0.020902300887392866, "acc_norm": 0.6110091743119266, "acc_norm_stderr": 0.020902300887392866 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.029157522184605607, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.029157522184605607 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5, "acc_stderr": 0.03509312031717982, "acc_norm": 0.5, "acc_norm_stderr": 0.03509312031717982 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6160337552742616, "acc_stderr": 0.031658678064106674, "acc_norm": 0.6160337552742616, "acc_norm_stderr": 0.031658678064106674 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5022421524663677, "acc_stderr": 0.03355746535223265, "acc_norm": 0.5022421524663677, "acc_norm_stderr": 0.03355746535223265 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.48854961832061067, "acc_stderr": 0.043841400240780176, "acc_norm": 0.48854961832061067, "acc_norm_stderr": 0.043841400240780176 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5537190082644629, "acc_stderr": 0.0453793517794788, "acc_norm": 0.5537190082644629, "acc_norm_stderr": 0.0453793517794788 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.048262172941398944, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.048262172941398944 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5521472392638037, "acc_stderr": 0.03906947479456605, "acc_norm": 0.5521472392638037, "acc_norm_stderr": 0.03906947479456605 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.32142857142857145, "acc_stderr": 0.04432804055291519, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.04432804055291519 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5825242718446602, "acc_stderr": 0.048828405482122375, "acc_norm": 0.5825242718446602, "acc_norm_stderr": 0.048828405482122375 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6239316239316239, "acc_stderr": 0.03173393632969481, "acc_norm": 0.6239316239316239, "acc_norm_stderr": 0.03173393632969481 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5977011494252874, "acc_stderr": 0.01753529452906895, "acc_norm": 0.5977011494252874, "acc_norm_stderr": 0.01753529452906895 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4682080924855491, "acc_stderr": 0.026864624366756653, "acc_norm": 0.4682080924855491, "acc_norm_stderr": 0.026864624366756653 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23910614525139665, "acc_stderr": 0.014265554192331161, "acc_norm": 0.23910614525139665, "acc_norm_stderr": 0.014265554192331161 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.48366013071895425, "acc_stderr": 0.028614624752805413, "acc_norm": 0.48366013071895425, "acc_norm_stderr": 0.028614624752805413 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.49517684887459806, "acc_stderr": 0.02839677044411129, "acc_norm": 0.49517684887459806, "acc_norm_stderr": 0.02839677044411129 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5030864197530864, "acc_stderr": 0.02782021415859437, "acc_norm": 0.5030864197530864, "acc_norm_stderr": 0.02782021415859437 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35815602836879434, "acc_stderr": 0.02860208586275942, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.02860208586275942 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3474576271186441, "acc_stderr": 0.012161417729749798, "acc_norm": 0.3474576271186441, "acc_norm_stderr": 0.012161417729749798 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.41544117647058826, "acc_stderr": 0.029935342707877753, "acc_norm": 0.41544117647058826, "acc_norm_stderr": 0.029935342707877753 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.42320261437908496, "acc_stderr": 0.01998780976948207, "acc_norm": 0.42320261437908496, "acc_norm_stderr": 0.01998780976948207 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661896, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661896 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.4816326530612245, "acc_stderr": 0.03198761546763126, "acc_norm": 0.4816326530612245, "acc_norm_stderr": 0.03198761546763126 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5472636815920398, "acc_stderr": 0.035197027175769155, "acc_norm": 0.5472636815920398, "acc_norm_stderr": 0.035197027175769155 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6491228070175439, "acc_stderr": 0.03660298834049163, "acc_norm": 0.6491228070175439, "acc_norm_stderr": 0.03660298834049163 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041836, "mc2": 0.3706359177223847, "mc2_stderr": 0.01391522805511699 }, "harness\|winogrande\|5": { "acc": 0.7016574585635359, "acc_stderr": 0.012858885010030421 }, "harness\|gsm8k\|5": { "acc": 0.10993176648976498, "acc_stderr": 0.008616195587865418 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_itsliupeng__openllama-7b-icl	[ "region:us" ]	2023-12-09T17:50:15+00:00	{"pretty_name": "Evaluation run of itsliupeng/openllama-7b-icl", "dataset_summary": "Dataset automatically created during the evaluation run of model [itsliupeng/openllama-7b-icl](https://huggingface.co/itsliupeng/openllama-7b-icl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_itsliupeng__openllama-7b-icl\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:48:05.024924](https://huggingface.co/datasets/open-llm-leaderboard/details_itsliupeng__openllama-7b-icl/blob/main/results_2023-12-09T17-48-05.024924.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.44441569324312047,\n \"acc_stderr\": 0.03430171403503658,\n \"acc_norm\": 0.4497976132436587,\n \"acc_norm_stderr\": 0.035089311320789345,\n \"mc1\": 0.23745410036719705,\n \"mc1_stderr\": 0.014896277441041836,\n \"mc2\": 0.3706359177223847,\n \"mc2_stderr\": 0.01391522805511699\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.44197952218430037,\n \"acc_stderr\": 0.014512682523128345,\n \"acc_norm\": 0.47952218430034127,\n \"acc_norm_stderr\": 0.014599131353035007\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5676160127464649,\n \"acc_stderr\": 0.0049439450696114546,\n \"acc_norm\": 0.7703644692292372,\n \"acc_norm_stderr\": 0.004197388626940065\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.46710526315789475,\n \"acc_stderr\": 0.040601270352363966,\n \"acc_norm\": 0.46710526315789475,\n \"acc_norm_stderr\": 0.040601270352363966\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.4867924528301887,\n \"acc_stderr\": 0.030762134874500482,\n \"acc_norm\": 0.4867924528301887,\n \"acc_norm_stderr\": 0.030762134874500482\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4930555555555556,\n \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.4930555555555556,\n \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.47398843930635837,\n \"acc_stderr\": 0.038073017265045105,\n \"acc_norm\": 0.47398843930635837,\n \"acc_norm_stderr\": 0.038073017265045105\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709390974,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709390974\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.34893617021276596,\n \"acc_stderr\": 0.03115852213135778,\n \"acc_norm\": 0.34893617021276596,\n \"acc_norm_stderr\": 0.03115852213135778\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.22807017543859648,\n \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.2804232804232804,\n \"acc_stderr\": 0.02313528797432563,\n \"acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.02313528797432563\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.1984126984126984,\n \"acc_stderr\": 0.03567016675276864,\n \"acc_norm\": 0.1984126984126984,\n \"acc_norm_stderr\": 0.03567016675276864\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.45161290322580644,\n \"acc_stderr\": 0.028310500348568392,\n \"acc_norm\": 0.45161290322580644,\n \"acc_norm_stderr\": 0.028310500348568392\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n \"acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.509090909090909,\n \"acc_stderr\": 0.0390369864774844,\n \"acc_norm\": 0.509090909090909,\n \"acc_norm_stderr\": 0.0390369864774844\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.5202020202020202,\n \"acc_stderr\": 0.03559443565563918,\n \"acc_norm\": 0.5202020202020202,\n \"acc_norm_stderr\": 0.03559443565563918\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.616580310880829,\n \"acc_stderr\": 0.03508984236295342,\n \"acc_norm\": 0.616580310880829,\n \"acc_norm_stderr\": 0.03508984236295342\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.024838811988033158,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.024838811988033158\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.02534809746809784,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02534809746809784\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.3487394957983193,\n \"acc_stderr\": 0.030956636328566548,\n \"acc_norm\": 0.3487394957983193,\n \"acc_norm_stderr\": 0.030956636328566548\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.6110091743119266,\n \"acc_stderr\": 0.020902300887392866,\n \"acc_norm\": 0.6110091743119266,\n \"acc_norm_stderr\": 0.020902300887392866\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.029157522184605607,\n \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.029157522184605607\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.03509312031717982,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.03509312031717982\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.6160337552742616,\n \"acc_stderr\": 0.031658678064106674,\n \"acc_norm\": 0.6160337552742616,\n \"acc_norm_stderr\": 0.031658678064106674\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5022421524663677,\n \"acc_stderr\": 0.03355746535223265,\n \"acc_norm\": 0.5022421524663677,\n \"acc_norm_stderr\": 0.03355746535223265\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.48854961832061067,\n \"acc_stderr\": 0.043841400240780176,\n \"acc_norm\": 0.48854961832061067,\n \"acc_norm_stderr\": 0.043841400240780176\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.5537190082644629,\n \"acc_stderr\": 0.0453793517794788,\n \"acc_norm\": 0.5537190082644629,\n \"acc_norm_stderr\": 0.0453793517794788\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.5521472392638037,\n \"acc_stderr\": 0.03906947479456605,\n \"acc_norm\": 0.5521472392638037,\n \"acc_norm_stderr\": 0.03906947479456605\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.32142857142857145,\n \"acc_stderr\": 0.04432804055291519,\n \"acc_norm\": 0.32142857142857145,\n \"acc_norm_stderr\": 0.04432804055291519\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.5825242718446602,\n \"acc_stderr\": 0.048828405482122375,\n \"acc_norm\": 0.5825242718446602,\n \"acc_norm_stderr\": 0.048828405482122375\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6239316239316239,\n \"acc_stderr\": 0.03173393632969481,\n \"acc_norm\": 0.6239316239316239,\n \"acc_norm_stderr\": 0.03173393632969481\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5977011494252874,\n \"acc_stderr\": 0.01753529452906895,\n \"acc_norm\": 0.5977011494252874,\n \"acc_norm_stderr\": 0.01753529452906895\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.4682080924855491,\n \"acc_stderr\": 0.026864624366756653,\n \"acc_norm\": 0.4682080924855491,\n \"acc_norm_stderr\": 0.026864624366756653\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23910614525139665,\n \"acc_stderr\": 0.014265554192331161,\n \"acc_norm\": 0.23910614525139665,\n \"acc_norm_stderr\": 0.014265554192331161\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.48366013071895425,\n \"acc_stderr\": 0.028614624752805413,\n \"acc_norm\": 0.48366013071895425,\n \"acc_norm_stderr\": 0.028614624752805413\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.49517684887459806,\n \"acc_stderr\": 0.02839677044411129,\n \"acc_norm\": 0.49517684887459806,\n \"acc_norm_stderr\": 0.02839677044411129\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5030864197530864,\n \"acc_stderr\": 0.02782021415859437,\n \"acc_norm\": 0.5030864197530864,\n \"acc_norm_stderr\": 0.02782021415859437\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.35815602836879434,\n \"acc_stderr\": 0.02860208586275942,\n \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.02860208586275942\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3474576271186441,\n \"acc_stderr\": 0.012161417729749798,\n \"acc_norm\": 0.3474576271186441,\n \"acc_norm_stderr\": 0.012161417729749798\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.41544117647058826,\n \"acc_stderr\": 0.029935342707877753,\n \"acc_norm\": 0.41544117647058826,\n \"acc_norm_stderr\": 0.029935342707877753\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.42320261437908496,\n \"acc_stderr\": 0.01998780976948207,\n \"acc_norm\": 0.42320261437908496,\n \"acc_norm_stderr\": 0.01998780976948207\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5909090909090909,\n \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.4816326530612245,\n \"acc_stderr\": 0.03198761546763126,\n \"acc_norm\": 0.4816326530612245,\n \"acc_norm_stderr\": 0.03198761546763126\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.5472636815920398,\n \"acc_stderr\": 0.035197027175769155,\n \"acc_norm\": 0.5472636815920398,\n \"acc_norm_stderr\": 0.035197027175769155\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-48-05.024924.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-48-05.024924.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T17_48_05.024924", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-48-05.024924.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-48-05.024924.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T17_48_05.024924", "path": ["results_2023-12-09T17-48-05.024924.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T17-48-05.024924.parquet"]}]}]}	2023-12-09T17:50:59+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of itsliupeng/openllama-7b-icl ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model itsliupeng/openllama-7b-icl on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:48:05.024924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of itsliupeng/openllama-7b-icl", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model itsliupeng/openllama-7b-icl on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:48:05.024924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of itsliupeng/openllama-7b-icl", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model itsliupeng/openllama-7b-icl on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:48:05.024924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 21, 31, 170, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of itsliupeng/openllama-7b-icl## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model itsliupeng/openllama-7b-icl on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:48:05.024924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
021e8182fecdc40586bdae190b6bf26e075afd17	# Dataset Card for Evaluation run of rwitz/go-bruins ## Dataset Description - Homepage: - Repository: https://huggingface.co/rwitz/go-bruins - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [rwitz/go-bruins](https://huggingface.co/rwitz/go-bruins) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_rwitz__go-bruins", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:56:51.445836](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins/blob/main/results_2023-12-09T17-56-51.445836.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6537762475221197, "acc_stderr": 0.03208085743053689, "acc_norm": 0.6538246694322897, "acc_norm_stderr": 0.032742779319017035, "mc1": 0.4320685434516524, "mc1_stderr": 0.017341202394988257, "mc2": 0.5871006945090181, "mc2_stderr": 0.015474717474561337 }, "harness\|arc:challenge\|25": { "acc": 0.6638225255972696, "acc_stderr": 0.013804855026205761, "acc_norm": 0.6911262798634812, "acc_norm_stderr": 0.013501770929344003 }, "harness\|hellaswag\|10": { "acc": 0.6857199761003784, "acc_stderr": 0.004632797375289765, "acc_norm": 0.867257518422625, "acc_norm_stderr": 0.0033860277997584177 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119668, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119668 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6589595375722543, "acc_stderr": 0.036146654241808254, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.036146654241808254 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5957446808510638, "acc_stderr": 0.03208115750788684, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.03208115750788684 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033456, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033456 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8186274509803921, "acc_stderr": 0.027044621719474082, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.027044621719474082 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579665, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579665 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594654, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594654 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8846153846153846, "acc_stderr": 0.020930193185179326, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179326 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.822477650063857, "acc_stderr": 0.01366423099583483, "acc_norm": 0.822477650063857, "acc_norm_stderr": 0.01366423099583483 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508287, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508287 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4301675977653631, "acc_stderr": 0.016558601636041035, "acc_norm": 0.4301675977653631, "acc_norm_stderr": 0.016558601636041035 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4654498044328553, "acc_stderr": 0.012739711554045704, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045704 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.028418208619406755, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.028418208619406755 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6830065359477124, "acc_stderr": 0.018824219512706207, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.018824219512706207 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291296, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291296 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4320685434516524, "mc1_stderr": 0.017341202394988257, "mc2": 0.5871006945090181, "mc2_stderr": 0.015474717474561337 }, "harness\|winogrande\|5": { "acc": 0.8145224940805051, "acc_stderr": 0.010923965303140505 }, "harness\|gsm8k\|5": { "acc": 0.6990144048521607, "acc_stderr": 0.012634504465211185 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_rwitz__go-bruins	[ "region:us" ]	2023-12-09T17:50:17+00:00	{"pretty_name": "Evaluation run of rwitz/go-bruins", "dataset_summary": "Dataset automatically created during the evaluation run of model [rwitz/go-bruins](https://huggingface.co/rwitz/go-bruins) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_rwitz__go-bruins\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:56:51.445836](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins/blob/main/results_2023-12-09T17-56-51.445836.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6537762475221197,\n \"acc_stderr\": 0.03208085743053689,\n \"acc_norm\": 0.6538246694322897,\n \"acc_norm_stderr\": 0.032742779319017035,\n \"mc1\": 0.4320685434516524,\n \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5871006945090181,\n \"mc2_stderr\": 0.015474717474561337\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6638225255972696,\n \"acc_stderr\": 0.013804855026205761,\n \"acc_norm\": 0.6911262798634812,\n \"acc_norm_stderr\": 0.013501770929344003\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6857199761003784,\n \"acc_stderr\": 0.004632797375289765,\n \"acc_norm\": 0.867257518422625,\n \"acc_norm_stderr\": 0.0033860277997584177\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119668,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119668\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7709677419354839,\n \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579665,\n \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579665\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.020930193185179326,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.020930193185179326\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.822477650063857,\n \"acc_stderr\": 0.01366423099583483,\n \"acc_norm\": 0.822477650063857,\n \"acc_norm_stderr\": 0.01366423099583483\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4301675977653631,\n \"acc_stderr\": 0.016558601636041035,\n \"acc_norm\": 0.4301675977653631,\n \"acc_norm_stderr\": 0.016558601636041035\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4654498044328553,\n \"acc_stderr\": 0.012739711554045704,\n \"acc_norm\": 0.4654498044328553,\n \"acc_norm_stderr\": 0.012739711554045704\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4320685434516524,\n \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5871006945090181,\n \"mc2_stderr\": 0.015474717474561337\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8145224940805051,\n \"acc_stderr\": 0.010923965303140505\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6990144048521607,\n \"acc_stderr\": 0.012634504465211185\n }\n}\n```", "repo_url": "https://huggingface.co/rwitz/go-bruins", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T17-47-26.960183.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T17-47-26.960183.parquet", 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2023-12-09T17-56-51.445836.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T17_47_26.960183", "path": ["results_2023-12-09T17-47-26.960183.parquet"]}, {"split": "2023_12_09T17_56_51.445836", "path": ["results_2023-12-09T17-56-51.445836.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T17-56-51.445836.parquet"]}]}]}	2023-12-09T18:00:30+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of rwitz/go-bruins ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model rwitz/go-bruins on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:56:51.445836(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of rwitz/go-bruins", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model rwitz/go-bruins on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:56:51.445836(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of rwitz/go-bruins", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model rwitz/go-bruins on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:56:51.445836(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 16, 31, 165, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of rwitz/go-bruins## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model rwitz/go-bruins on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:56:51.445836(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
f402965bf15d803974053361199318a9c25286bc	# Dataset Card for Evaluation run of PulsarAI/MetaMath-Tulpar-7b-v2-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/MetaMath-Tulpar-7b-v2-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-Tulpar-7b-v2-Slerp](https://huggingface.co/PulsarAI/MetaMath-Tulpar-7b-v2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PulsarAI__MetaMath-Tulpar-7b-v2-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:55:14.434225](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-Tulpar-7b-v2-Slerp/blob/main/results_2023-12-09T17-55-14.434225.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.639251601749628, "acc_stderr": 0.03221647012444142, "acc_norm": 0.6389576323016398, "acc_norm_stderr": 0.03288102806405326, "mc1": 0.401468788249694, "mc1_stderr": 0.017160273901693654, "mc2": 0.564970662967412, "mc2_stderr": 0.015518503176886996 }, "harness\|arc:challenge\|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042194, "acc_norm": 0.6561433447098977, "acc_norm_stderr": 0.013880644570156213 }, "harness\|hellaswag\|10": { "acc": 0.6677952599083847, "acc_stderr": 0.004700413824942566, "acc_norm": 0.8516231826329417, "acc_norm_stderr": 0.0035474663103253973 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7171052631578947, "acc_stderr": 0.03665349695640767, "acc_norm": 0.7171052631578947, "acc_norm_stderr": 0.03665349695640767 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493864, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493864 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400351, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41798941798941797, "acc_stderr": 0.02540255550326091, "acc_norm": 0.41798941798941797, "acc_norm_stderr": 0.02540255550326091 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356852, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356852 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4729064039408867, "acc_stderr": 0.03512819077876106, "acc_norm": 0.4729064039408867, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812143, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812143 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396997, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396997 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.02889774874113115, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.02889774874113115 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886786, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886786 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009244, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009244 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306085, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306085 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.036401182719909456, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.036401182719909456 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.03826076324884866, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.03826076324884866 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.024105712607754307, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.024105712607754307 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.41787709497206704, "acc_stderr": 0.016495400635820084, "acc_norm": 0.41787709497206704, "acc_norm_stderr": 0.016495400635820084 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.02573885479781874, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.02573885479781874 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7138263665594855, "acc_stderr": 0.02567025924218893, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.02567025924218893 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4716312056737589, "acc_stderr": 0.029779450957303062, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.029779450957303062 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4602346805736636, "acc_stderr": 0.012729785386598559, "acc_norm": 0.4602346805736636, "acc_norm_stderr": 0.012729785386598559 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6507352941176471, "acc_stderr": 0.02895975519682487, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.02895975519682487 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6617647058823529, "acc_stderr": 0.019139943748487043, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.019139943748487043 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.401468788249694, "mc1_stderr": 0.017160273901693654, "mc2": 0.564970662967412, "mc2_stderr": 0.015518503176886996 }, "harness\|winogrande\|5": { "acc": 0.7947908445146015, "acc_stderr": 0.011350315707462063 }, "harness\|gsm8k\|5": { "acc": 0.709628506444276, "acc_stderr": 0.012503592481818948 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_PulsarAI__MetaMath-Tulpar-7b-v2-Slerp	[ "region:us" ]	2023-12-09T17:58:06+00:00	{"pretty_name": "Evaluation run of PulsarAI/MetaMath-Tulpar-7b-v2-Slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-Tulpar-7b-v2-Slerp](https://huggingface.co/PulsarAI/MetaMath-Tulpar-7b-v2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__MetaMath-Tulpar-7b-v2-Slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:55:14.434225](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-Tulpar-7b-v2-Slerp/blob/main/results_2023-12-09T17-55-14.434225.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.639251601749628,\n \"acc_stderr\": 0.03221647012444142,\n \"acc_norm\": 0.6389576323016398,\n \"acc_norm_stderr\": 0.03288102806405326,\n \"mc1\": 0.401468788249694,\n \"mc1_stderr\": 0.017160273901693654,\n \"mc2\": 0.564970662967412,\n \"mc2_stderr\": 0.015518503176886996\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042194,\n \"acc_norm\": 0.6561433447098977,\n \"acc_norm_stderr\": 0.013880644570156213\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6677952599083847,\n \"acc_stderr\": 0.004700413824942566,\n \"acc_norm\": 0.8516231826329417,\n \"acc_norm_stderr\": 0.0035474663103253973\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.41798941798941797,\n \"acc_stderr\": 0.02540255550326091,\n \"acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.02540255550326091\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356852,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356852\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812143,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812143\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396997,\n \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396997\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113115,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113115\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306085,\n \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306085\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.036401182719909456,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.036401182719909456\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.03826076324884866,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.03826076324884866\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5089285714285714,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-55-14.434225.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T17-55-14.434225.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T17_55_14.434225", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-55-14.434225.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T17-55-14.434225.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T17_55_14.434225", "path": ["results_2023-12-09T17-55-14.434225.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T17-55-14.434225.parquet"]}]}]}	2023-12-09T17:58:50+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of PulsarAI/MetaMath-Tulpar-7b-v2-Slerp ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model PulsarAI/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:55:14.434225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of PulsarAI/MetaMath-Tulpar-7b-v2-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:55:14.434225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of PulsarAI/MetaMath-Tulpar-7b-v2-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:55:14.434225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 28, 31, 177, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PulsarAI/MetaMath-Tulpar-7b-v2-Slerp## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:55:14.434225(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
86b9f45073ae0a9f432dff2f2649edea66b8086b	# Dataset Card for Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp](https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T17:58:17.272756](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp/blob/main/results_2023-12-09T17-58-17.272756.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6430394737674227, "acc_stderr": 0.03225098588955544, "acc_norm": 0.643238473261251, "acc_norm_stderr": 0.03291299264153459, "mc1": 0.39412484700122397, "mc1_stderr": 0.017106588140700322, "mc2": 0.5614591813728808, "mc2_stderr": 0.015408154626799953 }, "harness\|arc:challenge\|25": { "acc": 0.6271331058020477, "acc_stderr": 0.014131176760131169, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness\|hellaswag\|10": { "acc": 0.6669986058554073, "acc_stderr": 0.004703238534045804, "acc_norm": 0.8546106353316073, "acc_norm_stderr": 0.0035177257870177433 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322663, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322663 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778408, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778408 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7677419354838709, "acc_stderr": 0.024022256130308235, "acc_norm": 0.7677419354838709, "acc_norm_stderr": 0.024022256130308235 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586818, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586818 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919446, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919446 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.02874204090394848, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.02874204090394848 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6974789915966386, "acc_stderr": 0.02983796238829194, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.02983796238829194 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8440366972477065, "acc_stderr": 0.01555580271359017, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.01555580271359017 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5509259259259259, "acc_stderr": 0.03392238405321617, "acc_norm": 0.5509259259259259, "acc_norm_stderr": 0.03392238405321617 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639325, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639325 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601446, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601446 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7557251908396947, "acc_stderr": 0.037683359597287434, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.037683359597287434 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037182, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037182 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.02158649400128138, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.02158649400128138 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8199233716475096, "acc_stderr": 0.013740797258579828, "acc_norm": 0.8199233716475096, "acc_norm_stderr": 0.013740797258579828 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.023948512905468365, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.023948512905468365 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.41787709497206704, "acc_stderr": 0.016495400635820084, "acc_norm": 0.41787709497206704, "acc_norm_stderr": 0.016495400635820084 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7156862745098039, "acc_stderr": 0.025829163272757485, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.025829163272757485 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.02474862449053737, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.02474862449053737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.45045632333767927, "acc_stderr": 0.012707390438502346, "acc_norm": 0.45045632333767927, "acc_norm_stderr": 0.012707390438502346 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6544117647058824, "acc_stderr": 0.028888193103988633, "acc_norm": 0.6544117647058824, "acc_norm_stderr": 0.028888193103988633 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169143, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169143 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.39412484700122397, "mc1_stderr": 0.017106588140700322, "mc2": 0.5614591813728808, "mc2_stderr": 0.015408154626799953 }, "harness\|winogrande\|5": { "acc": 0.7947908445146015, "acc_stderr": 0.01135031570746206 }, "harness\|gsm8k\|5": { "acc": 0.7012888551933283, "acc_stderr": 0.012607137125693625 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp	[ "region:us" ]	2023-12-09T18:01:07+00:00	{"pretty_name": "Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp](https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T17:58:17.272756](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp/blob/main/results_2023-12-09T17-58-17.272756.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6430394737674227,\n \"acc_stderr\": 0.03225098588955544,\n \"acc_norm\": 0.643238473261251,\n \"acc_norm_stderr\": 0.03291299264153459,\n \"mc1\": 0.39412484700122397,\n \"mc1_stderr\": 0.017106588140700322,\n \"mc2\": 0.5614591813728808,\n \"mc2_stderr\": 0.015408154626799953\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131169,\n \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6669986058554073,\n \"acc_stderr\": 0.004703238534045804,\n \"acc_norm\": 0.8546106353316073,\n \"acc_norm_stderr\": 0.0035177257870177433\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322663,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322663\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7291666666666666,\n \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778408,\n \"acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778408\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919446,\n \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919446\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829194,\n \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829194\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639325,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639325\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8199233716475096,\n \"acc_stderr\": 0.013740797258579828,\n \"acc_norm\": 0.8199233716475096,\n \"acc_norm_stderr\": 0.013740797258579828\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468365,\n \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468365\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.41787709497206704,\n \"acc_stderr\": 0.016495400635820084,\n \"acc_norm\": 0.41787709497206704,\n \"acc_norm_stderr\": 0.016495400635820084\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757485,\n \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757485\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.45045632333767927,\n \"acc_stderr\": 0.012707390438502346,\n \"acc_norm\": 0.45045632333767927,\n \"acc_norm_stderr\": 0.012707390438502346\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6544117647058824,\n \"acc_stderr\": 0.028888193103988633,\n \"acc_norm\": 0.6544117647058824,\n \"acc_norm_stderr\": 0.028888193103988633\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n \"acc_stderr\": 0.025870646766169143,\n \"acc_norm\": 0.8407960199004975,\n \"acc_norm_stderr\": 0.025870646766169143\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.39412484700122397,\n \"mc1_stderr\": 0.017106588140700322,\n \"mc2\": 0.5614591813728808,\n \"mc2_stderr\": 0.015408154626799953\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7947908445146015,\n \"acc_stderr\": 0.01135031570746206\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7012888551933283,\n \"acc_stderr\": 0.012607137125693625\n }\n}\n```", "repo_url": "https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T17_58_17.272756", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-58-17.272756.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T17-58-17.272756.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T17_58_17.272756", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-58-17.272756.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T17-58-17.272756.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T17_58_17.272756", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-58-17.272756.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T17-58-17.272756.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T17_58_17.272756", "path": 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of model PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T17:58:17.272756(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:58:17.272756(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T17:58:17.272756(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T17:58:17.272756(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
c9c3d046e30c42df35edb5b67a2fae455501486f	# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp](https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-2-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T18:04:51.228408](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-2-Slerp/blob/main/results_2023-12-09T18-04-51.228408.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.644055937606071, "acc_stderr": 0.032184807364406556, "acc_norm": 0.6454677507073991, "acc_norm_stderr": 0.03283460519387843, "mc1": 0.4504283965728274, "mc1_stderr": 0.017417264371967646, "mc2": 0.6104827225746667, "mc2_stderr": 0.014972794318436832 }, "harness\|arc:challenge\|25": { "acc": 0.6459044368600683, "acc_stderr": 0.013975454122756557, "acc_norm": 0.6749146757679181, "acc_norm_stderr": 0.013688147309729124 }, "harness\|hellaswag\|10": { "acc": 0.6569408484365664, "acc_stderr": 0.0047376083401634, "acc_norm": 0.8542123083051185, "acc_norm_stderr": 0.0035217202839105555 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7171052631578947, "acc_stderr": 0.03665349695640767, "acc_norm": 0.7171052631578947, "acc_norm_stderr": 0.03665349695640767 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800886, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800886 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768776, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768776 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.02866120111652457, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.02866120111652457 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7016806722689075, "acc_stderr": 0.02971914287634286, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.02971914287634286 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601443, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601443 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752599, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752599 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8173690932311622, "acc_stderr": 0.013816335389973138, "acc_norm": 0.8173690932311622, "acc_norm_stderr": 0.013816335389973138 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577615, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577615 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4, "acc_stderr": 0.01638463841038082, "acc_norm": 0.4, "acc_norm_stderr": 0.01638463841038082 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.024630048979824782, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.024630048979824782 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818763, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818763 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7530864197530864, "acc_stderr": 0.023993501709042103, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.023993501709042103 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4602346805736636, "acc_stderr": 0.012729785386598568, "acc_norm": 0.4602346805736636, "acc_norm_stderr": 0.012729785386598568 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.02824568739146293, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.02824568739146293 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0190709855896875, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0190709855896875 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827075, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827075 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4504283965728274, "mc1_stderr": 0.017417264371967646, "mc2": 0.6104827225746667, "mc2_stderr": 0.014972794318436832 }, "harness\|winogrande\|5": { "acc": 0.8003157063930545, "acc_stderr": 0.011235328382625849 }, "harness\|gsm8k\|5": { "acc": 0.6307808946171342, "acc_stderr": 0.013293019538066244 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-2-Slerp	[ "region:us" ]	2023-12-09T18:07:42+00:00	{"pretty_name": "Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp](https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-2-Slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T18:04:51.228408](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-2-Slerp/blob/main/results_2023-12-09T18-04-51.228408.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.644055937606071,\n \"acc_stderr\": 0.032184807364406556,\n \"acc_norm\": 0.6454677507073991,\n \"acc_norm_stderr\": 0.03283460519387843,\n \"mc1\": 0.4504283965728274,\n \"mc1_stderr\": 0.017417264371967646,\n \"mc2\": 0.6104827225746667,\n \"mc2_stderr\": 0.014972794318436832\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6459044368600683,\n \"acc_stderr\": 0.013975454122756557,\n \"acc_norm\": 0.6749146757679181,\n \"acc_norm_stderr\": 0.013688147309729124\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6569408484365664,\n \"acc_stderr\": 0.0047376083401634,\n \"acc_norm\": 0.8542123083051185,\n \"acc_norm_stderr\": 0.0035217202839105555\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800886,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800886\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652457,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652457\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.02971914287634286,\n \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.02971914287634286\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752599,\n \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752599\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8173690932311622,\n \"acc_stderr\": 0.013816335389973138,\n \"acc_norm\": 0.8173690932311622,\n \"acc_norm_stderr\": 0.013816335389973138\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.01638463841038082,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.01638463841038082\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824782,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824782\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7041800643086816,\n \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042103,\n \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042103\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4602346805736636,\n \"acc_stderr\": 0.012729785386598568,\n \"acc_norm\": 0.4602346805736636,\n \"acc_norm_stderr\": 0.012729785386598568\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146293,\n \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146293\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.0190709855896875,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.0190709855896875\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4504283965728274,\n \"mc1_stderr\": 0.017417264371967646,\n \"mc2\": 0.6104827225746667,\n \"mc2_stderr\": 0.014972794318436832\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625849\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6307808946171342,\n \"acc_stderr\": 0.013293019538066244\n }\n}\n```", "repo_url": "https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|gsm8k\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hellaswag\|10_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-international_law\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-management\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-marketing\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-04-51.228408.parquet", "/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-04-51.228408.parquet", 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["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-04-51.228408.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T18_04_51.228408", "path": ["results_2023-12-09T18-04-51.228408.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T18-04-51.228408.parquet"]}]}]}	2023-12-09T18:08:27+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T18:04:51.228408(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:04:51.228408(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:04:51.228408(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T18:04:51.228408(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
e04d24c12154638ff6ce00cccb222946b48b303b	# Dataset Card for "rapidapi-example-responses" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses	[ "region:us" ]	2023-12-09T18:25:26+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "api_name", "dtype": "string"}, {"name": "api_description", "dtype": "string"}, {"name": "api_score", "dtype": "float64"}, {"name": "endpoint_name", "dtype": "string"}, {"name": "endpoint_description", "dtype": "string"}, {"name": "response_status_code", "dtype": "int64"}, {"name": "response_summary", "dtype": "string"}, {"name": "response_json", "dtype": "string"}, {"name": "response_json_schema", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 115936364, "num_examples": 28059}], "download_size": 27933521, "dataset_size": 115936364}}	2023-12-10T11:16:52+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses\"\n\nMore Information needed" ]
039426294a1c166522c7ca349782104640515b8d	# Dataset Card for Evaluation run of abacusai/Giraffe-13b-32k-v3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/abacusai/Giraffe-13b-32k-v3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [abacusai/Giraffe-13b-32k-v3](https://huggingface.co/abacusai/Giraffe-13b-32k-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_abacusai__Giraffe-13b-32k-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T18:24:23.140202](https://huggingface.co/datasets/open-llm-leaderboard/details_abacusai__Giraffe-13b-32k-v3/blob/main/results_2023-12-09T18-24-23.140202.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5497505679930987, "acc_stderr": 0.033867836191543606, "acc_norm": 0.554916151896208, "acc_norm_stderr": 0.03459261280501611, "mc1": 0.32558139534883723, "mc1_stderr": 0.016403989469907825, "mc2": 0.4667915335080478, "mc2_stderr": 0.014974105305176868 }, "harness\|arc:challenge\|25": { "acc": 0.5520477815699659, "acc_stderr": 0.014532011498211676, "acc_norm": 0.590443686006826, "acc_norm_stderr": 0.014370358632472444 }, "harness\|hellaswag\|10": { "acc": 0.5978888667596096, "acc_stderr": 0.0048932206350117925, "acc_norm": 0.795857398924517, "acc_norm_stderr": 0.004022499210760734 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464243, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464243 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5526315789473685, "acc_stderr": 0.04046336883978251, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.04046336883978251 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5849056603773585, "acc_stderr": 0.03032594578928611, "acc_norm": 0.5849056603773585, "acc_norm_stderr": 0.03032594578928611 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.041553199555931467, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.041553199555931467 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4624277456647399, "acc_stderr": 0.0380168510452446, "acc_norm": 0.4624277456647399, "acc_norm_stderr": 0.0380168510452446 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4127659574468085, "acc_stderr": 0.03218471141400351, "acc_norm": 0.4127659574468085, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537316, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537316 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.041618085035015295, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.34656084656084657, "acc_stderr": 0.024508777521028428, "acc_norm": 0.34656084656084657, "acc_norm_stderr": 0.024508777521028428 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147124, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147124 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6161290322580645, "acc_stderr": 0.02766618207553965, "acc_norm": 0.6161290322580645, "acc_norm_stderr": 0.02766618207553965 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.034819048444388045, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.034819048444388045 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091706, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091706 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.033586181457325226, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.033586181457325226 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7772020725388601, "acc_stderr": 0.030031147977641538, "acc_norm": 0.7772020725388601, "acc_norm_stderr": 0.030031147977641538 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5025641025641026, "acc_stderr": 0.025350672979412202, "acc_norm": 0.5025641025641026, "acc_norm_stderr": 0.025350672979412202 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.028406533090608463, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.028406533090608463 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.4957983193277311, "acc_stderr": 0.03247734334448111, "acc_norm": 0.4957983193277311, "acc_norm_stderr": 0.03247734334448111 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7192660550458716, "acc_stderr": 0.019266055045871616, "acc_norm": 0.7192660550458716, "acc_norm_stderr": 0.019266055045871616 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.42592592592592593, "acc_stderr": 0.033723432716530645, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.033723432716530645 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.030964517926923403, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.030964517926923403 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7426160337552743, "acc_stderr": 0.0284588209914603, "acc_norm": 0.7426160337552743, "acc_norm_stderr": 0.0284588209914603 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6278026905829597, "acc_stderr": 0.03244305283008732, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.03244305283008732 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6183206106870229, "acc_stderr": 0.04260735157644559, "acc_norm": 0.6183206106870229, "acc_norm_stderr": 0.04260735157644559 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.71900826446281, "acc_stderr": 0.04103203830514511, "acc_norm": 0.71900826446281, "acc_norm_stderr": 0.04103203830514511 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497751, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497751 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935574, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935574 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04547960999764376, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04547960999764376 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6601941747572816, "acc_stderr": 0.04689765937278135, "acc_norm": 0.6601941747572816, "acc_norm_stderr": 0.04689765937278135 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8162393162393162, "acc_stderr": 0.02537213967172293, "acc_norm": 0.8162393162393162, "acc_norm_stderr": 0.02537213967172293 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7292464878671775, "acc_stderr": 0.015889888362560486, "acc_norm": 0.7292464878671775, "acc_norm_stderr": 0.015889888362560486 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5895953757225434, "acc_stderr": 0.026483392042098174, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.026483392042098174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4, "acc_stderr": 0.01638463841038082, "acc_norm": 0.4, "acc_norm_stderr": 0.01638463841038082 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5915032679738562, "acc_stderr": 0.028146405993096358, "acc_norm": 0.5915032679738562, "acc_norm_stderr": 0.028146405993096358 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6302250803858521, "acc_stderr": 0.02741799670563099, "acc_norm": 0.6302250803858521, "acc_norm_stderr": 0.02741799670563099 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6265432098765432, "acc_stderr": 0.026915003011380154, "acc_norm": 0.6265432098765432, "acc_norm_stderr": 0.026915003011380154 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4574468085106383, "acc_stderr": 0.029719281272236855, "acc_norm": 0.4574468085106383, "acc_norm_stderr": 0.029719281272236855 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.39504563233376794, "acc_stderr": 0.012485727813251562, "acc_norm": 0.39504563233376794, "acc_norm_stderr": 0.012485727813251562 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5110294117647058, "acc_stderr": 0.03036544647727568, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.03036544647727568 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.020102583895887188, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.020102583895887188 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6, "acc_stderr": 0.0469237132203465, "acc_norm": 0.6, "acc_norm_stderr": 0.0469237132203465 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6612244897959184, "acc_stderr": 0.030299506562154185, "acc_norm": 0.6612244897959184, "acc_norm_stderr": 0.030299506562154185 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7412935323383084, "acc_stderr": 0.030965903123573012, "acc_norm": 0.7412935323383084, "acc_norm_stderr": 0.030965903123573012 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4879518072289157, "acc_stderr": 0.03891364495835821, "acc_norm": 0.4879518072289157, "acc_norm_stderr": 0.03891364495835821 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03188578017686399, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03188578017686399 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.32558139534883723, "mc1_stderr": 0.016403989469907825, "mc2": 0.4667915335080478, "mc2_stderr": 0.014974105305176868 }, "harness\|winogrande\|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836676 }, "harness\|gsm8k\|5": { "acc": 0.26156178923426837, "acc_stderr": 0.012105605733382444 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_abacusai__Giraffe-13b-32k-v3	[ "region:us" ]	2023-12-09T18:27:19+00:00	{"pretty_name": "Evaluation run of abacusai/Giraffe-13b-32k-v3", "dataset_summary": "Dataset automatically created during the evaluation run of model [abacusai/Giraffe-13b-32k-v3](https://huggingface.co/abacusai/Giraffe-13b-32k-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abacusai__Giraffe-13b-32k-v3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T18:24:23.140202](https://huggingface.co/datasets/open-llm-leaderboard/details_abacusai__Giraffe-13b-32k-v3/blob/main/results_2023-12-09T18-24-23.140202.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5497505679930987,\n \"acc_stderr\": 0.033867836191543606,\n \"acc_norm\": 0.554916151896208,\n \"acc_norm_stderr\": 0.03459261280501611,\n \"mc1\": 0.32558139534883723,\n \"mc1_stderr\": 0.016403989469907825,\n \"mc2\": 0.4667915335080478,\n \"mc2_stderr\": 0.014974105305176868\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5520477815699659,\n \"acc_stderr\": 0.014532011498211676,\n \"acc_norm\": 0.590443686006826,\n \"acc_norm_stderr\": 0.014370358632472444\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5978888667596096,\n \"acc_stderr\": 0.0048932206350117925,\n \"acc_norm\": 0.795857398924517,\n \"acc_norm_stderr\": 0.004022499210760734\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45925925925925926,\n \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5526315789473685,\n \"acc_stderr\": 0.04046336883978251,\n \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.04046336883978251\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.5849056603773585,\n \"acc_stderr\": 0.03032594578928611,\n \"acc_norm\": 0.5849056603773585,\n \"acc_norm_stderr\": 0.03032594578928611\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.041553199555931467,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.041553199555931467\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4624277456647399,\n \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.4624277456647399,\n \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4127659574468085,\n \"acc_stderr\": 0.03218471141400351,\n \"acc_norm\": 0.4127659574468085,\n \"acc_norm_stderr\": 0.03218471141400351\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.04303684033537316,\n \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.04303684033537316\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.34656084656084657,\n \"acc_stderr\": 0.024508777521028428,\n \"acc_norm\": 0.34656084656084657,\n \"acc_norm_stderr\": 0.024508777521028428\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.29365079365079366,\n \"acc_stderr\": 0.04073524322147124,\n \"acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.04073524322147124\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6161290322580645,\n \"acc_stderr\": 0.02766618207553965,\n \"acc_norm\": 0.6161290322580645,\n \"acc_norm_stderr\": 0.02766618207553965\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.034819048444388045,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.034819048444388045\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091706,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091706\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.033586181457325226,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.033586181457325226\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.030031147977641538,\n \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.030031147977641538\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5025641025641026,\n \"acc_stderr\": 0.025350672979412202,\n \"acc_norm\": 0.5025641025641026,\n \"acc_norm_stderr\": 0.025350672979412202\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608463,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-24-23.140202.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T18_24_23.140202", "path": ["results_2023-12-09T18-24-23.140202.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T18-24-23.140202.parquet"]}]}]}	2023-12-09T18:28:04+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of abacusai/Giraffe-13b-32k-v3 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model abacusai/Giraffe-13b-32k-v3 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T18:24:23.140202(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of abacusai/Giraffe-13b-32k-v3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model abacusai/Giraffe-13b-32k-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:24:23.140202(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of abacusai/Giraffe-13b-32k-v3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model abacusai/Giraffe-13b-32k-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:24:23.140202(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of abacusai/Giraffe-13b-32k-v3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model abacusai/Giraffe-13b-32k-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T18:24:23.140202(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
7a924e8fa53d351c4d0759cc56d9d218ff9cfc9e	# Dataset Card for Evaluation run of amazingvince/where-llambo-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/amazingvince/where-llambo-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [amazingvince/where-llambo-7b](https://huggingface.co/amazingvince/where-llambo-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_amazingvince__where-llambo-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T18:44:39.604520](https://huggingface.co/datasets/open-llm-leaderboard/details_amazingvince__where-llambo-7b/blob/main/results_2023-12-09T18-44-39.604520.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6276007814719067, "acc_stderr": 0.03245983620498288, "acc_norm": 0.6287066769044074, "acc_norm_stderr": 0.03312214889081226, "mc1": 0.34394124847001223, "mc1_stderr": 0.01662908751427678, "mc2": 0.4961220088630948, "mc2_stderr": 0.014820546287012869 }, "harness\|arc:challenge\|25": { "acc": 0.5452218430034129, "acc_stderr": 0.014551507060836357, "acc_norm": 0.5844709897610921, "acc_norm_stderr": 0.014401366641216386 }, "harness\|hellaswag\|10": { "acc": 0.612427803226449, "acc_stderr": 0.004862003566798543, "acc_norm": 0.8205536745668194, "acc_norm_stderr": 0.00382941380511398 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.04171654161354543, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.04171654161354543 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926604, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926604 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7677419354838709, "acc_stderr": 0.024022256130308235, "acc_norm": 0.7677419354838709, "acc_norm_stderr": 0.024022256130308235 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.797979797979798, "acc_stderr": 0.028606204289229862, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.028606204289229862 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8549222797927462, "acc_stderr": 0.025416343096306433, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.025416343096306433 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6205128205128205, "acc_stderr": 0.02460362692409742, "acc_norm": 0.6205128205128205, "acc_norm_stderr": 0.02460362692409742 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524575, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524575 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6218487394957983, "acc_stderr": 0.031499305777849054, "acc_norm": 0.6218487394957983, "acc_norm_stderr": 0.031499305777849054 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8348623853211009, "acc_stderr": 0.01591955782997604, "acc_norm": 0.8348623853211009, "acc_norm_stderr": 0.01591955782997604 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7745098039215687, "acc_stderr": 0.02933116229425174, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.02933116229425174 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302872, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302872 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073325, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073325 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876173, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876173 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500097, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500097 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.27039106145251396, "acc_stderr": 0.014854993938010076, "acc_norm": 0.27039106145251396, "acc_norm_stderr": 0.014854993938010076 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6928104575163399, "acc_stderr": 0.026415601914388992, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.026415601914388992 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.024383665531035454, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.024383665531035454 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4574468085106383, "acc_stderr": 0.029719281272236844, "acc_norm": 0.4574468085106383, "acc_norm_stderr": 0.029719281272236844 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4511082138200782, "acc_stderr": 0.012709037347346233, "acc_norm": 0.4511082138200782, "acc_norm_stderr": 0.012709037347346233 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6139705882352942, "acc_stderr": 0.02957326913441112, "acc_norm": 0.6139705882352942, "acc_norm_stderr": 0.02957326913441112 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6486928104575164, "acc_stderr": 0.019312676065786565, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.019312676065786565 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6181818181818182, "acc_stderr": 0.046534298079135075, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7510204081632653, "acc_stderr": 0.027682979522960238, "acc_norm": 0.7510204081632653, "acc_norm_stderr": 0.027682979522960238 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8059701492537313, "acc_stderr": 0.027962677604768914, "acc_norm": 0.8059701492537313, "acc_norm_stderr": 0.027962677604768914 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34394124847001223, "mc1_stderr": 0.01662908751427678, "mc2": 0.4961220088630948, "mc2_stderr": 0.014820546287012869 }, "harness\|winogrande\|5": { "acc": 0.7853196527229677, "acc_stderr": 0.011539912734345402 }, "harness\|gsm8k\|5": { "acc": 0.6520090978013646, "acc_stderr": 0.013120581030382134 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_amazingvince__where-llambo-7b	[ "region:us" ]	2023-12-09T18:47:31+00:00	{"pretty_name": "Evaluation run of amazingvince/where-llambo-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [amazingvince/where-llambo-7b](https://huggingface.co/amazingvince/where-llambo-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_amazingvince__where-llambo-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T18:44:39.604520](https://huggingface.co/datasets/open-llm-leaderboard/details_amazingvince__where-llambo-7b/blob/main/results_2023-12-09T18-44-39.604520.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6276007814719067,\n \"acc_stderr\": 0.03245983620498288,\n \"acc_norm\": 0.6287066769044074,\n \"acc_norm_stderr\": 0.03312214889081226,\n \"mc1\": 0.34394124847001223,\n \"mc1_stderr\": 0.01662908751427678,\n \"mc2\": 0.4961220088630948,\n \"mc2_stderr\": 0.014820546287012869\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5452218430034129,\n \"acc_stderr\": 0.014551507060836357,\n \"acc_norm\": 0.5844709897610921,\n \"acc_norm_stderr\": 0.014401366641216386\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.612427803226449,\n \"acc_stderr\": 0.004862003566798543,\n \"acc_norm\": 0.8205536745668194,\n \"acc_norm_stderr\": 0.00382941380511398\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926604,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926604\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7361111111111112,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.797979797979798,\n \"acc_stderr\": 0.028606204289229862,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229862\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306433,\n \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306433\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6205128205128205,\n \"acc_stderr\": 0.02460362692409742,\n \"acc_norm\": 0.6205128205128205,\n \"acc_norm_stderr\": 0.02460362692409742\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.031499305777849054,\n \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.031499305777849054\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8348623853211009,\n \"acc_stderr\": 0.01591955782997604,\n \"acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.01591955782997604\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.02933116229425174,\n \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02933116229425174\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.013890862162876173,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.013890862162876173\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.27039106145251396,\n \"acc_stderr\": 0.014854993938010076,\n \"acc_norm\": 0.27039106145251396,\n \"acc_norm_stderr\": 0.014854993938010076\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.026415601914388992,\n \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.026415601914388992\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7106109324758842,\n \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035454,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035454\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4574468085106383,\n \"acc_stderr\": 0.029719281272236844,\n \"acc_norm\": 0.4574468085106383,\n \"acc_norm_stderr\": 0.029719281272236844\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4511082138200782,\n \"acc_stderr\": 0.012709037347346233,\n \"acc_norm\": 0.4511082138200782,\n \"acc_norm_stderr\": 0.012709037347346233\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6139705882352942,\n \"acc_stderr\": 0.02957326913441112,\n \"acc_norm\": 0.6139705882352942,\n \"acc_norm_stderr\": 0.02957326913441112\n },\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-44-39.604520.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-44-39.604520.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T18_44_39.604520", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-44-39.604520.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-44-39.604520.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T18_44_39.604520", "path": ["results_2023-12-09T18-44-39.604520.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T18-44-39.604520.parquet"]}]}]}	2023-12-09T18:48:15+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of amazingvince/where-llambo-7b ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model amazingvince/where-llambo-7b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T18:44:39.604520(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of amazingvince/where-llambo-7b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model amazingvince/where-llambo-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:44:39.604520(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of amazingvince/where-llambo-7b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model amazingvince/where-llambo-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:44:39.604520(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 19, 31, 168, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of amazingvince/where-llambo-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model amazingvince/where-llambo-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T18:44:39.604520(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
114b7df46a1df5842195d686215163f5a806111a	# Dataset Card for Evaluation run of mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1](https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mwitiderrick__shearedplats-2.7b-v2-instruct-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T18:49:52.400292](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__shearedplats-2.7b-v2-instruct-v0.1/blob/main/results_2023-12-09T18-49-52.400292.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.2882361931913223, "acc_stderr": 0.031895486998552665, "acc_norm": 0.2903928342274915, "acc_norm_stderr": 0.03267939625046512, "mc1": 0.2729498164014688, "mc1_stderr": 0.015594753632006535, "mc2": 0.41227748774876055, "mc2_stderr": 0.014572961912704371 }, "harness\|arc:challenge\|25": { "acc": 0.3660409556313993, "acc_stderr": 0.01407722310847014, "acc_norm": 0.40187713310580203, "acc_norm_stderr": 0.014327268614578274 }, "harness\|hellaswag\|10": { "acc": 0.5142401911969727, "acc_stderr": 0.00498775731476984, "acc_norm": 0.7007568213503286, "acc_norm_stderr": 0.00456990648509029 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.040247784019771096, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.040247784019771096 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03317672787533157, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03317672787533157 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2830188679245283, "acc_stderr": 0.027724236492700904, "acc_norm": 0.2830188679245283, "acc_norm_stderr": 0.027724236492700904 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.3263888888888889, "acc_stderr": 0.03921067198982266, "acc_norm": 0.3263888888888889, "acc_norm_stderr": 0.03921067198982266 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.2, "acc_stderr": 0.04020151261036846, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.2254335260115607, "acc_stderr": 0.03186209851641144, "acc_norm": 0.2254335260115607, "acc_norm_stderr": 0.03186209851641144 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.23529411764705882, "acc_stderr": 0.042207736591714534, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.042207736591714534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.31063829787234043, "acc_stderr": 0.030251237579213174, "acc_norm": 0.31063829787234043, "acc_norm_stderr": 0.030251237579213174 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.31724137931034485, "acc_stderr": 0.038783523721386215, "acc_norm": 0.31724137931034485, "acc_norm_stderr": 0.038783523721386215 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.20899470899470898, "acc_stderr": 0.020940481565334835, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.020940481565334835 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.24516129032258063, "acc_stderr": 0.024472243840895525, "acc_norm": 0.24516129032258063, "acc_norm_stderr": 0.024472243840895525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.030108330718011625, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.030108330718011625 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2909090909090909, "acc_stderr": 0.03546563019624335, "acc_norm": 0.2909090909090909, "acc_norm_stderr": 0.03546563019624335 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.29292929292929293, "acc_stderr": 0.03242497958178815, "acc_norm": 0.29292929292929293, "acc_norm_stderr": 0.03242497958178815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.25906735751295334, "acc_stderr": 0.031618779179354115, "acc_norm": 0.25906735751295334, "acc_norm_stderr": 0.031618779179354115 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.23333333333333334, "acc_stderr": 0.02144454730156047, "acc_norm": 0.23333333333333334, "acc_norm_stderr": 0.02144454730156047 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.0263357394040558 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.226890756302521, "acc_stderr": 0.027205371538279496, "acc_norm": 0.226890756302521, "acc_norm_stderr": 0.027205371538279496 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.23841059602649006, "acc_stderr": 0.034791855725996586, "acc_norm": 0.23841059602649006, "acc_norm_stderr": 0.034791855725996586 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.28990825688073396, "acc_stderr": 0.019453066609201597, "acc_norm": 0.28990825688073396, "acc_norm_stderr": 0.019453066609201597 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.27314814814814814, "acc_stderr": 0.030388051301678116, "acc_norm": 0.27314814814814814, "acc_norm_stderr": 0.030388051301678116 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25980392156862747, "acc_stderr": 0.03077855467869326, "acc_norm": 0.25980392156862747, "acc_norm_stderr": 0.03077855467869326 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.31645569620253167, "acc_stderr": 0.03027497488021898, "acc_norm": 0.31645569620253167, "acc_norm_stderr": 0.03027497488021898 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.29596412556053814, "acc_stderr": 0.0306365913486998, "acc_norm": 0.29596412556053814, "acc_norm_stderr": 0.0306365913486998 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.29770992366412213, "acc_stderr": 0.04010358942462203, "acc_norm": 0.29770992366412213, "acc_norm_stderr": 0.04010358942462203 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.38016528925619836, "acc_stderr": 0.04431324501968432, "acc_norm": 0.38016528925619836, "acc_norm_stderr": 0.04431324501968432 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04557239513497751, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04557239513497751 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.32515337423312884, "acc_stderr": 0.036803503712864616, "acc_norm": 0.32515337423312884, "acc_norm_stderr": 0.036803503712864616 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.29464285714285715, "acc_stderr": 0.043270409325787296, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.043270409325787296 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2815533980582524, "acc_stderr": 0.04453254836326469, "acc_norm": 0.2815533980582524, "acc_norm_stderr": 0.04453254836326469 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.3162393162393162, "acc_stderr": 0.030463656747340268, "acc_norm": 0.3162393162393162, "acc_norm_stderr": 0.030463656747340268 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.37292464878671777, "acc_stderr": 0.017292868269453924, "acc_norm": 0.37292464878671777, "acc_norm_stderr": 0.017292868269453924 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.32947976878612717, "acc_stderr": 0.025305258131879716, "acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.025305258131879716 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2558659217877095, "acc_stderr": 0.014593620923210742, "acc_norm": 0.2558659217877095, "acc_norm_stderr": 0.014593620923210742 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.26143790849673204, "acc_stderr": 0.025160998214292456, "acc_norm": 0.26143790849673204, "acc_norm_stderr": 0.025160998214292456 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2990353697749196, "acc_stderr": 0.02600330111788513, "acc_norm": 0.2990353697749196, "acc_norm_stderr": 0.02600330111788513 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.3271604938271605, "acc_stderr": 0.026105673861409825, "acc_norm": 0.3271604938271605, "acc_norm_stderr": 0.026105673861409825 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.29432624113475175, "acc_stderr": 0.027187127011503793, "acc_norm": 0.29432624113475175, "acc_norm_stderr": 0.027187127011503793 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.28096479791395046, "acc_stderr": 0.011479684550077692, "acc_norm": 0.28096479791395046, "acc_norm_stderr": 0.011479684550077692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.20220588235294118, "acc_stderr": 0.024398192986654924, "acc_norm": 0.20220588235294118, "acc_norm_stderr": 0.024398192986654924 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2875816993464052, "acc_stderr": 0.018311653053648222, "acc_norm": 0.2875816993464052, "acc_norm_stderr": 0.018311653053648222 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2909090909090909, "acc_stderr": 0.04350271442923243, "acc_norm": 0.2909090909090909, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.22040816326530613, "acc_stderr": 0.026537045312145287, "acc_norm": 0.22040816326530613, "acc_norm_stderr": 0.026537045312145287 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.32338308457711445, "acc_stderr": 0.033076159479790326, "acc_norm": 0.32338308457711445, "acc_norm_stderr": 0.033076159479790326 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3072289156626506, "acc_stderr": 0.035915667978246635, "acc_norm": 0.3072289156626506, "acc_norm_stderr": 0.035915667978246635 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.32748538011695905, "acc_stderr": 0.035993357714560276, "acc_norm": 0.32748538011695905, "acc_norm_stderr": 0.035993357714560276 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2729498164014688, "mc1_stderr": 0.015594753632006535, "mc2": 0.41227748774876055, "mc2_stderr": 0.014572961912704371 }, "harness\|winogrande\|5": { "acc": 0.6503551696921863, "acc_stderr": 0.013402073680850515 }, "harness\|gsm8k\|5": { "acc": 0.02122820318423048, "acc_stderr": 0.003970449129848635 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_mwitiderrick__shearedplats-2.7b-v2-instruct-v0.1	[ "region:us" ]	2023-12-09T18:52:51+00:00	{"pretty_name": "Evaluation run of mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1](https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mwitiderrick__shearedplats-2.7b-v2-instruct-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T18:49:52.400292](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__shearedplats-2.7b-v2-instruct-v0.1/blob/main/results_2023-12-09T18-49-52.400292.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2882361931913223,\n \"acc_stderr\": 0.031895486998552665,\n \"acc_norm\": 0.2903928342274915,\n \"acc_norm_stderr\": 0.03267939625046512,\n \"mc1\": 0.2729498164014688,\n \"mc1_stderr\": 0.015594753632006535,\n \"mc2\": 0.41227748774876055,\n \"mc2_stderr\": 0.014572961912704371\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.3660409556313993,\n \"acc_stderr\": 0.01407722310847014,\n \"acc_norm\": 0.40187713310580203,\n \"acc_norm_stderr\": 0.014327268614578274\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5142401911969727,\n \"acc_stderr\": 0.00498775731476984,\n \"acc_norm\": 0.7007568213503286,\n \"acc_norm_stderr\": 0.00456990648509029\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.040247784019771096,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.040247784019771096\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.03317672787533157,\n \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.03317672787533157\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.2830188679245283,\n \"acc_stderr\": 0.027724236492700904,\n \"acc_norm\": 0.2830188679245283,\n \"acc_norm_stderr\": 0.027724236492700904\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.3263888888888889,\n \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.3263888888888889,\n \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.2254335260115607,\n \"acc_stderr\": 0.03186209851641144,\n \"acc_norm\": 0.2254335260115607,\n \"acc_norm_stderr\": 0.03186209851641144\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.042207736591714534,\n \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.042207736591714534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.030251237579213174,\n \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.030251237579213174\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.22807017543859648,\n \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.31724137931034485,\n \"acc_stderr\": 0.038783523721386215,\n \"acc_norm\": 0.31724137931034485,\n \"acc_norm_stderr\": 0.038783523721386215\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.20899470899470898,\n \"acc_stderr\": 0.020940481565334835,\n \"acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.020940481565334835\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.24516129032258063,\n \"acc_stderr\": 0.024472243840895525,\n \"acc_norm\": 0.24516129032258063,\n \"acc_norm_stderr\": 0.024472243840895525\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.030108330718011625,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.030108330718011625\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.2909090909090909,\n \"acc_stderr\": 0.03546563019624335,\n \"acc_norm\": 0.2909090909090909,\n \"acc_norm_stderr\": 0.03546563019624335\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.29292929292929293,\n \"acc_stderr\": 0.03242497958178815,\n \"acc_norm\": 0.29292929292929293,\n \"acc_norm_stderr\": 0.03242497958178815\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.25906735751295334,\n \"acc_stderr\": 0.031618779179354115,\n \"acc_norm\": 0.25906735751295334,\n \"acc_norm_stderr\": 0.031618779179354115\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.23333333333333334,\n \"acc_stderr\": 0.02144454730156047,\n \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.02144454730156047\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.226890756302521,\n \"acc_stderr\": 0.027205371538279496,\n \"acc_norm\": 0.226890756302521,\n \"acc_norm_stderr\": 0.027205371538279496\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.23841059602649006,\n \"acc_stderr\": 0.034791855725996586,\n \"acc_norm\": 0.23841059602649006,\n \"acc_norm_stderr\": 0.034791855725996586\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.28990825688073396,\n \"acc_stderr\": 0.019453066609201597,\n \"acc_norm\": 0.28990825688073396,\n \"acc_norm_stderr\": 0.019453066609201597\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.27314814814814814,\n \"acc_stderr\": 0.030388051301678116,\n \"acc_norm\": 0.27314814814814814,\n \"acc_norm_stderr\": 0.030388051301678116\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.25980392156862747,\n \"acc_stderr\": 0.03077855467869326,\n \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.03077855467869326\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.31645569620253167,\n \"acc_stderr\": 0.03027497488021898,\n \"acc_norm\": 0.31645569620253167,\n \"acc_norm_stderr\": 0.03027497488021898\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.29596412556053814,\n \"acc_stderr\": 0.0306365913486998,\n \"acc_norm\": 0.29596412556053814,\n \"acc_norm_stderr\": 0.0306365913486998\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.29770992366412213,\n \"acc_stderr\": 0.04010358942462203,\n \"acc_norm\": 0.29770992366412213,\n \"acc_norm_stderr\": 0.04010358942462203\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.38016528925619836,\n \"acc_stderr\": 0.04431324501968432,\n \"acc_norm\": 0.38016528925619836,\n \"acc_norm_stderr\": 0.04431324501968432\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.32515337423312884,\n \"acc_stderr\": 0.036803503712864616,\n \"acc_norm\": 0.32515337423312884,\n \"acc_norm_stderr\": 0.036803503712864616\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.2815533980582524,\n \"acc_stderr\": 0.04453254836326469,\n \"acc_norm\": 0.2815533980582524,\n \"acc_norm_stderr\": 0.04453254836326469\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.3162393162393162,\n \"acc_stderr\": 0.030463656747340268,\n \"acc_norm\": 0.3162393162393162,\n \"acc_norm_stderr\": 0.030463656747340268\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.37292464878671777,\n \"acc_stderr\": 0.017292868269453924,\n \"acc_norm\": 0.37292464878671777,\n \"acc_norm_stderr\": 0.017292868269453924\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.32947976878612717,\n \"acc_stderr\": 0.025305258131879716,\n \"acc_norm\": 0.32947976878612717,\n \"acc_norm_stderr\": 0.025305258131879716\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2558659217877095,\n \"acc_stderr\": 0.014593620923210742,\n \"acc_norm\": 0.2558659217877095,\n \"acc_norm_stderr\": 0.014593620923210742\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.26143790849673204,\n \"acc_stderr\": 0.025160998214292456,\n \"acc_norm\": 0.26143790849673204,\n \"acc_norm_stderr\": 0.025160998214292456\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2990353697749196,\n \"acc_stderr\": 0.02600330111788513,\n \"acc_norm\": 0.2990353697749196,\n \"acc_norm_stderr\": 0.02600330111788513\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.3271604938271605,\n \"acc_stderr\": 0.026105673861409825,\n \"acc_norm\": 0.3271604938271605,\n \"acc_norm_stderr\": 0.026105673861409825\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.29432624113475175,\n \"acc_stderr\": 0.027187127011503793,\n \"acc_norm\": 0.29432624113475175,\n \"acc_norm_stderr\": 0.027187127011503793\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.28096479791395046,\n \"acc_stderr\": 0.011479684550077692,\n \"acc_norm\": 0.28096479791395046,\n \"acc_norm_stderr\": 0.011479684550077692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.20220588235294118,\n \"acc_stderr\": 0.024398192986654924,\n \"acc_norm\": 0.20220588235294118,\n \"acc_norm_stderr\": 0.024398192986654924\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.2875816993464052,\n \"acc_stderr\": 0.018311653053648222,\n \"acc_norm\": 0.2875816993464052,\n \"acc_norm_stderr\": 0.018311653053648222\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.2909090909090909,\n \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.2909090909090909,\n \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.22040816326530613,\n \"acc_stderr\": 0.026537045312145287,\n \"acc_norm\": 0.22040816326530613,\n \"acc_norm_stderr\": 0.026537045312145287\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.32338308457711445,\n \"acc_stderr\": 0.033076159479790326,\n \"acc_norm\": 0.32338308457711445,\n \"acc_norm_stderr\": 0.033076159479790326\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.3072289156626506,\n \"acc_stderr\": 0.035915667978246635,\n \"acc_norm\": 0.3072289156626506,\n \"acc_norm_stderr\": 0.035915667978246635\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.32748538011695905,\n \"acc_stderr\": 0.035993357714560276,\n \"acc_norm\": 0.32748538011695905,\n \"acc_norm_stderr\": 0.035993357714560276\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2729498164014688,\n \"mc1_stderr\": 0.015594753632006535,\n \"mc2\": 0.41227748774876055,\n \"mc2_stderr\": 0.014572961912704371\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.6503551696921863,\n \"acc_stderr\": 0.013402073680850515\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.02122820318423048,\n \"acc_stderr\": 0.003970449129848635\n }\n}\n```", "repo_url": "https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|gsm8k\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hellaswag\|10_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T18-49-52.400292.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T18-49-52.400292.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": 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["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T18-49-52.400292.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T18_49_52.400292", "path": ["results_2023-12-09T18-49-52.400292.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T18-49-52.400292.parquet"]}]}]}	2023-12-09T18:53:35+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T18:49:52.400292(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:49:52.400292(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T18:49:52.400292(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T18:49:52.400292(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
7f5c8e586c00ebaabb396ba68b269ef15d068188	# Dataset Card for "cds_both_balanced_512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	lhallee/cds_both_balanced_512	[ "region:us" ]	2023-12-09T19:04:19+00:00	{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "species", "dtype": "string"}, {"name": "CDS", "dtype": "string"}, {"name": "AA", "dtype": "string"}, {"name": "Label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1905721929, "num_examples": 3245094}], "download_size": 1707079967, "dataset_size": 1905721929}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-09T19:05:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "cds_both_balanced_512" More Information needed	[ "# Dataset Card for \"cds_both_balanced_512\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"cds_both_balanced_512\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"cds_both_balanced_512\"\n\nMore Information needed" ]
5f8d715eb506f24e3ed53db9ec8ed3c7fe52840e	# Dataset Card for "ccds_human_512.csv" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	lhallee/ccds_human_512	[ "region:us" ]	2023-12-09T19:07:57+00:00	{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "species", "dtype": "string"}, {"name": "CDS", "dtype": "string"}, {"name": "AA", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13364217, "num_examples": 20882}], "download_size": 12357139, "dataset_size": 13364217}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-09T19:07:58+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ccds_human_512.csv" More Information needed	[ "# Dataset Card for \"ccds_human_512.csv\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ccds_human_512.csv\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ccds_human_512.csv\"\n\nMore Information needed" ]
cb5a6e0799e65d81629735883a95363fe0cd168c	# Dataset Card for "ccds_mouse_512.csv" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	lhallee/ccds_mouse_512	[ "region:us" ]	2023-12-09T19:08:05+00:00	{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "species", "dtype": "string"}, {"name": "CDS", "dtype": "string"}, {"name": "AA", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10746756, "num_examples": 16628}], "download_size": 9922040, "dataset_size": 10746756}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-09T19:08:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ccds_mouse_512.csv" More Information needed	[ "# Dataset Card for \"ccds_mouse_512.csv\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ccds_mouse_512.csv\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ccds_mouse_512.csv\"\n\nMore Information needed" ]
3090a3b94ae0825ae5cb9c3baca9d21638ebb931	![img](https://i.imgur.com/LjJAUln.png) Inspired by the [trismegistus-project](https://huggingface.co/datasets/teknium/trismegistus-project) by teknium, I decided to build a high-quality dataset composed of some of the most important works for Western esoteric studies. The dataset is currently compose of 20 carefully processed books of multiple authors in the field. The dataset lacks works on practical magic; this should be fixed in the coming versions of this dataset. Pax!	alexandreteles/hermes-toth	[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:en", "license:agpl-3.0", "spirituality", "occultism", "esoterism", "region:us" ]	2023-12-09T19:26:47+00:00	{"language": ["en"], "license": "agpl-3.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "Hermes Toth", "tags": ["spirituality", "occultism", "esoterism"]}	2024-02-10T03:59:41+00:00	[]	[ "en" ]	TAGS #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-agpl-3.0 #spirituality #occultism #esoterism #region-us	!img Inspired by the trismegistus-project by teknium, I decided to build a high-quality dataset composed of some of the most important works for Western esoteric studies. The dataset is currently compose of 20 carefully processed books of multiple authors in the field. The dataset lacks works on practical magic; this should be fixed in the coming versions of this dataset. Pax!	[]	[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-agpl-3.0 #spirituality #occultism #esoterism #region-us \n" ]	[ 53 ]	[ "passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-agpl-3.0 #spirituality #occultism #esoterism #region-us \n" ]
794434f35af33be65f4e36f695347a817848b861	# Dataset Card for Evaluation run of Zardos/Kant-Test-0.1-Mistral-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/Zardos/Kant-Test-0.1-Mistral-7B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Zardos/Kant-Test-0.1-Mistral-7B](https://huggingface.co/Zardos/Kant-Test-0.1-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Zardos__Kant-Test-0.1-Mistral-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T11:05:46.345175](https://huggingface.co/datasets/open-llm-leaderboard/details_Zardos__Kant-Test-0.1-Mistral-7B/blob/main/results_2023-12-10T11-05-46.345175.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6253667303213345, "acc_stderr": 0.03253315196101968, "acc_norm": 0.6318205791064505, "acc_norm_stderr": 0.033203023073407084, "mc1": 0.3402692778457772, "mc1_stderr": 0.016586304901762557, "mc2": 0.4940424629624919, "mc2_stderr": 0.014891468326851799 }, "harness\|arc:challenge\|25": { "acc": 0.5844709897610921, "acc_stderr": 0.014401366641216388, "acc_norm": 0.6177474402730375, "acc_norm_stderr": 0.014200454049979275 }, "harness\|hellaswag\|10": { "acc": 0.6352320254929297, "acc_stderr": 0.004803812631994957, "acc_norm": 0.828918542123083, "acc_norm_stderr": 0.0037581050431501257 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6513157894736842, "acc_stderr": 0.038781398887976104, "acc_norm": 0.6513157894736842, "acc_norm_stderr": 0.038781398887976104 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6641509433962264, "acc_stderr": 0.02906722014664483, "acc_norm": 0.6641509433962264, "acc_norm_stderr": 0.02906722014664483 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5895953757225434, "acc_stderr": 0.03750757044895536, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.03750757044895536 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105652, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105652 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944433, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944433 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7483870967741936, "acc_stderr": 0.024685979286239963, "acc_norm": 0.7483870967741936, "acc_norm_stderr": 0.024685979286239963 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.02463978909770944, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.02463978909770944 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6256410256410256, "acc_stderr": 0.0245375915728305, "acc_norm": 0.6256410256410256, "acc_norm_stderr": 0.0245375915728305 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616258, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616258 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6302521008403361, "acc_stderr": 0.03135709599613591, "acc_norm": 0.6302521008403361, "acc_norm_stderr": 0.03135709599613591 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8036697247706422, "acc_stderr": 0.01703071933915435, "acc_norm": 0.8036697247706422, "acc_norm_stderr": 0.01703071933915435 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.49074074074074076, "acc_stderr": 0.034093869469927006, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7745098039215687, "acc_stderr": 0.02933116229425174, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.02933116229425174 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7914110429447853, "acc_stderr": 0.031921934489347235, "acc_norm": 0.7914110429447853, "acc_norm_stderr": 0.031921934489347235 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077812, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077812 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.80970625798212, "acc_stderr": 0.01403694585038139, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.01403694585038139 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.024818350129436593, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.024818350129436593 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3642458100558659, "acc_stderr": 0.016094338768474596, "acc_norm": 0.3642458100558659, "acc_norm_stderr": 0.016094338768474596 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.025261691219729484, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.025261691219729484 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818774, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818774 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7191358024691358, "acc_stderr": 0.025006469755799208, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.025006469755799208 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4511082138200782, "acc_stderr": 0.012709037347346233, "acc_norm": 0.4511082138200782, "acc_norm_stderr": 0.012709037347346233 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6507352941176471, "acc_stderr": 0.028959755196824873, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.028959755196824873 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.019070985589687492, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.019070985589687492 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3402692778457772, "mc1_stderr": 0.016586304901762557, "mc2": 0.4940424629624919, "mc2_stderr": 0.014891468326851799 }, "harness\|winogrande\|5": { "acc": 0.7853196527229677, "acc_stderr": 0.011539912734345396 }, "harness\|gsm8k\|5": { "acc": 0.3115996967399545, "acc_stderr": 0.012757375376754941 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Zardos__Kant-Test-0.1-Mistral-7B	[ "region:us" ]	2023-12-09T19:37:18+00:00	{"pretty_name": "Evaluation run of Zardos/Kant-Test-0.1-Mistral-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Zardos/Kant-Test-0.1-Mistral-7B](https://huggingface.co/Zardos/Kant-Test-0.1-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Zardos__Kant-Test-0.1-Mistral-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T11:05:46.345175](https://huggingface.co/datasets/open-llm-leaderboard/details_Zardos__Kant-Test-0.1-Mistral-7B/blob/main/results_2023-12-10T11-05-46.345175.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6253667303213345,\n \"acc_stderr\": 0.03253315196101968,\n \"acc_norm\": 0.6318205791064505,\n \"acc_norm_stderr\": 0.033203023073407084,\n \"mc1\": 0.3402692778457772,\n \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.4940424629624919,\n \"mc2_stderr\": 0.014891468326851799\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5844709897610921,\n \"acc_stderr\": 0.014401366641216388,\n \"acc_norm\": 0.6177474402730375,\n \"acc_norm_stderr\": 0.014200454049979275\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6352320254929297,\n \"acc_stderr\": 0.004803812631994957,\n \"acc_norm\": 0.828918542123083,\n \"acc_norm_stderr\": 0.0037581050431501257\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.038781398887976104,\n \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.038781398887976104\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6641509433962264,\n \"acc_stderr\": 0.02906722014664483,\n \"acc_norm\": 0.6641509433962264,\n \"acc_norm_stderr\": 0.02906722014664483\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895536\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105652,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105652\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944433,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944433\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7483870967741936,\n \"acc_stderr\": 0.024685979286239963,\n \"acc_norm\": 0.7483870967741936,\n \"acc_norm_stderr\": 0.024685979286239963\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.0245375915728305,\n \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.0245375915728305\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616258,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616258\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6302521008403361,\n \"acc_stderr\": 0.03135709599613591,\n \"acc_norm\": 0.6302521008403361,\n \"acc_norm_stderr\": 0.03135709599613591\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8036697247706422,\n \"acc_stderr\": 0.01703071933915435,\n \"acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.01703071933915435\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.02933116229425174,\n \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02933116229425174\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6547085201793722,\n \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.49107142857142855,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.022509033937077812,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.022509033937077812\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.80970625798212,\n \"acc_stderr\": 0.01403694585038139,\n \"acc_norm\": 0.80970625798212,\n \"acc_norm_stderr\": 0.01403694585038139\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3642458100558659,\n \"acc_stderr\": 0.016094338768474596,\n \"acc_norm\": 0.3642458100558659,\n \"acc_norm_stderr\": 0.016094338768474596\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729484,\n \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729484\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7041800643086816,\n \"acc_stderr\": 0.025922371788818774,\n \"acc_norm\": 0.7041800643086816,\n \"acc_norm_stderr\": 0.025922371788818774\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4511082138200782,\n \"acc_stderr\": 0.012709037347346233,\n \"acc_norm\": 0.4511082138200782,\n \"acc_norm_stderr\": 0.012709037347346233\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824873,\n \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824873\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687492,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687492\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3402692778457772,\n \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.4940424629624919,\n \"mc2_stderr\": 0.014891468326851799\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7853196527229677,\n \"acc_stderr\": 0.011539912734345396\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3115996967399545,\n \"acc_stderr\": 0.012757375376754941\n }\n}\n```", "repo_url": "https://huggingface.co/Zardos/Kant-Test-0.1-Mistral-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|gsm8k\|5_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|gsm8k\|5_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|gsm8k\|5_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|hellaswag\|10_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|hellaswag\|10_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|hellaswag\|10_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T19-34-29.855469.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T19-34-29.855469.parquet", 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["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["/details_harness\|winogrande\|5_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T11-05-46.345175.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T19_34_29.855469", "path": ["results_2023-12-09T19-34-29.855469.parquet"]}, {"split": "2023_12_09T19_45_27.448654", "path": ["results_2023-12-09T19-45-27.448654.parquet"]}, {"split": "2023_12_10T11_05_46.345175", "path": ["results_2023-12-10T11-05-46.345175.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T11-05-46.345175.parquet"]}]}]}	2023-12-10T11:09:25+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Zardos/Kant-Test-0.1-Mistral-7B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Zardos/Kant-Test-0.1-Mistral-7B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T11:05:46.345175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Zardos/Kant-Test-0.1-Mistral-7B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Zardos/Kant-Test-0.1-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:05:46.345175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Zardos/Kant-Test-0.1-Mistral-7B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Zardos/Kant-Test-0.1-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:05:46.345175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Zardos/Kant-Test-0.1-Mistral-7B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Zardos/Kant-Test-0.1-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T11:05:46.345175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
cab7e6da7727c300be5214f623f0101310a5ba20	# Dataset Card for Evaluation run of Sao10K/Venomia-m7 ## Dataset Description - Homepage: - Repository: https://huggingface.co/Sao10K/Venomia-m7 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Sao10K/Venomia-m7](https://huggingface.co/Sao10K/Venomia-m7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Sao10K__Venomia-m7", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T19:38:57.975905](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Venomia-m7/blob/main/results_2023-12-09T19-38-57.975905.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5994561896653553, "acc_stderr": 0.032914574924459074, "acc_norm": 0.6051903322222639, "acc_norm_stderr": 0.03358493802168653, "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.49078721070216347, "mc2_stderr": 0.015495976475887885 }, "harness\|arc:challenge\|25": { "acc": 0.5870307167235495, "acc_stderr": 0.014388344935398326, "acc_norm": 0.6313993174061433, "acc_norm_stderr": 0.014097810678042194 }, "harness\|hellaswag\|10": { "acc": 0.6635132443736308, "acc_stderr": 0.004715419139697519, "acc_norm": 0.8399721171081458, "acc_norm_stderr": 0.0036588262081016106 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6381578947368421, "acc_stderr": 0.039105257528497236, "acc_norm": 0.6381578947368421, "acc_norm_stderr": 0.039105257528497236 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6415094339622641, "acc_stderr": 0.02951470358398177, "acc_norm": 0.6415094339622641, "acc_norm_stderr": 0.02951470358398177 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6875, "acc_stderr": 0.038760854559127644, "acc_norm": 0.6875, "acc_norm_stderr": 0.038760854559127644 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3431372549019608, "acc_stderr": 0.047240073523838876, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.047240073523838876 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932262, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932262 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5276595744680851, "acc_stderr": 0.03263597118409769, "acc_norm": 0.5276595744680851, "acc_norm_stderr": 0.03263597118409769 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.39473684210526316, "acc_stderr": 0.045981880578165414, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.045981880578165414 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.496551724137931, "acc_stderr": 0.041665675771015785, "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.02510742548113728, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.02510742548113728 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3412698412698413, "acc_stderr": 0.04240799327574925, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.04240799327574925 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6870967741935484, "acc_stderr": 0.02637756702864586, "acc_norm": 0.6870967741935484, "acc_norm_stderr": 0.02637756702864586 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4630541871921182, "acc_stderr": 0.035083705204426656, "acc_norm": 0.4630541871921182, "acc_norm_stderr": 0.035083705204426656 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7525252525252525, "acc_stderr": 0.03074630074212451, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.03074630074212451 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6218487394957983, "acc_stderr": 0.031499305777849054, "acc_norm": 0.6218487394957983, "acc_norm_stderr": 0.031499305777849054 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7834862385321101, "acc_stderr": 0.017658710594443135, "acc_norm": 0.7834862385321101, "acc_norm_stderr": 0.017658710594443135 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.39351851851851855, "acc_stderr": 0.03331747876370312, "acc_norm": 0.39351851851851855, "acc_norm_stderr": 0.03331747876370312 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639318, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639318 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306085, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306085 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7107438016528925, "acc_stderr": 0.04139112727635463, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.04139112727635463 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7129629629629629, "acc_stderr": 0.043733130409147614, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.043733130409147614 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615623, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615623 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489294, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489294 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8045977011494253, "acc_stderr": 0.014179171373424383, "acc_norm": 0.8045977011494253, "acc_norm_stderr": 0.014179171373424383 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6734104046242775, "acc_stderr": 0.025248264774242832, "acc_norm": 0.6734104046242775, "acc_norm_stderr": 0.025248264774242832 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.30614525139664805, "acc_stderr": 0.01541449448790323, "acc_norm": 0.30614525139664805, "acc_norm_stderr": 0.01541449448790323 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6862745098039216, "acc_stderr": 0.026568921015457152, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.026568921015457152 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6591639871382636, "acc_stderr": 0.026920841260776162, "acc_norm": 0.6591639871382636, "acc_norm_stderr": 0.026920841260776162 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.025407197798890162, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.025407197798890162 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.44680851063829785, "acc_stderr": 0.029658235097666907, "acc_norm": 0.44680851063829785, "acc_norm_stderr": 0.029658235097666907 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.42959582790091266, "acc_stderr": 0.01264300462379021, "acc_norm": 0.42959582790091266, "acc_norm_stderr": 0.01264300462379021 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6286764705882353, "acc_stderr": 0.02934980313976587, "acc_norm": 0.6286764705882353, "acc_norm_stderr": 0.02934980313976587 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6274509803921569, "acc_stderr": 0.019559646809215923, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.019559646809215923 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.04607582090719976, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.04607582090719976 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065674, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065674 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233264, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233264 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.49078721070216347, "mc2_stderr": 0.015495976475887885 }, "harness\|winogrande\|5": { "acc": 0.7576953433307024, "acc_stderr": 0.012042352526174782 }, "harness\|gsm8k\|5": { "acc": 0.3237300985595148, "acc_stderr": 0.01288824739737114 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Sao10K__Venomia-m7	[ "region:us" ]	2023-12-09T19:41:51+00:00	{"pretty_name": "Evaluation run of Sao10K/Venomia-m7", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/Venomia-m7](https://huggingface.co/Sao10K/Venomia-m7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Venomia-m7\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T19:38:57.975905](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Venomia-m7/blob/main/results_2023-12-09T19-38-57.975905.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5994561896653553,\n \"acc_stderr\": 0.032914574924459074,\n \"acc_norm\": 0.6051903322222639,\n \"acc_norm_stderr\": 0.03358493802168653,\n \"mc1\": 0.34149326805385555,\n \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.49078721070216347,\n \"mc2_stderr\": 0.015495976475887885\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5870307167235495,\n \"acc_stderr\": 0.014388344935398326,\n \"acc_norm\": 0.6313993174061433,\n \"acc_norm_stderr\": 0.014097810678042194\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6635132443736308,\n \"acc_stderr\": 0.004715419139697519,\n \"acc_norm\": 0.8399721171081458,\n \"acc_norm_stderr\": 0.0036588262081016106\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.039105257528497236,\n \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.039105257528497236\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6415094339622641,\n \"acc_stderr\": 0.02951470358398177,\n \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.02951470358398177\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.047240073523838876,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.047240073523838876\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5276595744680851,\n \"acc_stderr\": 0.03263597118409769,\n \"acc_norm\": 0.5276595744680851,\n \"acc_norm_stderr\": 0.03263597118409769\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.02510742548113728,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.02510742548113728\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3412698412698413,\n \"acc_stderr\": 0.04240799327574925,\n \"acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.04240799327574925\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6870967741935484,\n \"acc_stderr\": 0.02637756702864586,\n \"acc_norm\": 0.6870967741935484,\n \"acc_norm_stderr\": 0.02637756702864586\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7525252525252525,\n \"acc_stderr\": 0.03074630074212451,\n \"acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.03074630074212451\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6025641025641025,\n \"acc_stderr\": 0.024811920017903836,\n \"acc_norm\": 0.6025641025641025,\n \"acc_norm_stderr\": 0.024811920017903836\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.031499305777849054,\n \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.031499305777849054\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.7834862385321101,\n \"acc_stderr\": 0.017658710594443135,\n \"acc_norm\": 0.7834862385321101,\n \"acc_norm_stderr\": 0.017658710594443135\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.39351851851851855,\n \"acc_stderr\": 0.03331747876370312,\n \"acc_norm\": 0.39351851851851855,\n \"acc_norm_stderr\": 0.03331747876370312\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6547085201793722,\n \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306085,\n \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306085\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.38392857142857145,\n \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8461538461538461,\n \"acc_stderr\": 0.023636873317489294,\n \"acc_norm\": 0.8461538461538461,\n \"acc_norm_stderr\": 0.023636873317489294\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8045977011494253,\n \"acc_stderr\": 0.014179171373424383,\n \"acc_norm\": 0.8045977011494253,\n \"acc_norm_stderr\": 0.014179171373424383\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.025248264774242832,\n \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.025248264774242832\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.30614525139664805,\n \"acc_stderr\": 0.01541449448790323,\n \"acc_norm\": 0.30614525139664805,\n \"acc_norm_stderr\": 0.01541449448790323\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.026568921015457152,\n \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.026568921015457152\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6591639871382636,\n \"acc_stderr\": 0.026920841260776162,\n \"acc_norm\": 0.6591639871382636,\n \"acc_norm_stderr\": 0.026920841260776162\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.025407197798890162,\n \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.025407197798890162\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.42959582790091266,\n \"acc_stderr\": 0.01264300462379021,\n \"acc_norm\": 0.42959582790091266,\n \"acc_norm_stderr\": 0.01264300462379021\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.02934980313976587,\n \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.02934980313976587\n },\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-38-57.975905.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-38-57.975905.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T19_38_57.975905", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-38-57.975905.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-38-57.975905.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T19_38_57.975905", "path": ["results_2023-12-09T19-38-57.975905.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T19-38-57.975905.parquet"]}]}]}	2023-12-09T19:42:33+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Sao10K/Venomia-m7 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Sao10K/Venomia-m7 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T19:38:57.975905(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Sao10K/Venomia-m7", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Venomia-m7 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:38:57.975905(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Sao10K/Venomia-m7", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Venomia-m7 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:38:57.975905(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 18, 31, 167, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Sao10K/Venomia-m7## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Venomia-m7 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T19:38:57.975905(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
2739c3884b8251ea1832b46d1319e1034d1c93e8	# Dataset Card for Evaluation run of ajibawa-2023/Code-13B ## Dataset Description - Homepage: - Repository: https://huggingface.co/ajibawa-2023/Code-13B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [ajibawa-2023/Code-13B](https://huggingface.co/ajibawa-2023/Code-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__Code-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T19:40:16.694610](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-13B/blob/main/results_2023-12-09T19-40-16.694610.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5315302469691541, "acc_stderr": 0.0338171547995471, "acc_norm": 0.5374650243523146, "acc_norm_stderr": 0.034550805778528454, "mc1": 0.2962056303549572, "mc1_stderr": 0.01598359510181139, "mc2": 0.4246156253859874, "mc2_stderr": 0.01586771249517698 }, "harness\|arc:challenge\|25": { "acc": 0.5511945392491467, "acc_stderr": 0.014534599585097665, "acc_norm": 0.5733788395904437, "acc_norm_stderr": 0.014453185592920293 }, "harness\|hellaswag\|10": { "acc": 0.6420035849432384, "acc_stderr": 0.004784312972495391, "acc_norm": 0.8328022306313483, "acc_norm_stderr": 0.003723897305645496 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421296, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421296 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5197368421052632, "acc_stderr": 0.040657710025626036, "acc_norm": 0.5197368421052632, "acc_norm_stderr": 0.040657710025626036 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5773584905660377, "acc_stderr": 0.03040233144576954, "acc_norm": 0.5773584905660377, "acc_norm_stderr": 0.03040233144576954 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5902777777777778, "acc_stderr": 0.04112490974670788, "acc_norm": 0.5902777777777778, "acc_norm_stderr": 0.04112490974670788 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4797687861271676, "acc_stderr": 0.03809342081273957, "acc_norm": 0.4797687861271676, "acc_norm_stderr": 0.03809342081273957 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.04158307533083286, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.04158307533083286 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108102, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108102 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3508771929824561, "acc_stderr": 0.044895393502706986, "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.044895393502706986 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.42758620689655175, "acc_stderr": 0.04122737111370331, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.04122737111370331 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3201058201058201, "acc_stderr": 0.024026846392873506, "acc_norm": 0.3201058201058201, "acc_norm_stderr": 0.024026846392873506 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6290322580645161, "acc_stderr": 0.027480541887953593, "acc_norm": 0.6290322580645161, "acc_norm_stderr": 0.027480541887953593 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4433497536945813, "acc_stderr": 0.03495334582162933, "acc_norm": 0.4433497536945813, "acc_norm_stderr": 0.03495334582162933 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6424242424242425, "acc_stderr": 0.037425970438065864, "acc_norm": 0.6424242424242425, "acc_norm_stderr": 0.037425970438065864 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6565656565656566, "acc_stderr": 0.03383201223244441, "acc_norm": 0.6565656565656566, "acc_norm_stderr": 0.03383201223244441 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7875647668393783, "acc_stderr": 0.02951928261681723, "acc_norm": 0.7875647668393783, "acc_norm_stderr": 0.02951928261681723 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4717948717948718, "acc_stderr": 0.025310639254933882, "acc_norm": 0.4717948717948718, "acc_norm_stderr": 0.025310639254933882 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3148148148148148, "acc_stderr": 0.02831753349606648, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.02831753349606648 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5378151260504201, "acc_stderr": 0.032385469487589795, "acc_norm": 0.5378151260504201, "acc_norm_stderr": 0.032385469487589795 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943342, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943342 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6844036697247706, "acc_stderr": 0.019926117513869666, "acc_norm": 0.6844036697247706, "acc_norm_stderr": 0.019926117513869666 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3611111111111111, "acc_stderr": 0.03275773486100999, "acc_norm": 0.3611111111111111, "acc_norm_stderr": 0.03275773486100999 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588663, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588663 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.70042194092827, "acc_stderr": 0.02981802474975309, "acc_norm": 0.70042194092827, "acc_norm_stderr": 0.02981802474975309 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6367713004484304, "acc_stderr": 0.03227790442850499, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.03227790442850499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5801526717557252, "acc_stderr": 0.043285772152629715, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.043285772152629715 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.628099173553719, "acc_stderr": 0.044120158066245044, "acc_norm": 0.628099173553719, "acc_norm_stderr": 0.044120158066245044 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.04453197507374983, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.04453197507374983 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6441717791411042, "acc_stderr": 0.03761521380046734, "acc_norm": 0.6441717791411042, "acc_norm_stderr": 0.03761521380046734 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.30357142857142855, "acc_stderr": 0.04364226155841044, "acc_norm": 0.30357142857142855, "acc_norm_stderr": 0.04364226155841044 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.041858325989283136, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.041858325989283136 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.027236013946196697, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.027236013946196697 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7215836526181354, "acc_stderr": 0.016028295188992476, "acc_norm": 0.7215836526181354, "acc_norm_stderr": 0.016028295188992476 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6271676300578035, "acc_stderr": 0.02603389061357628, "acc_norm": 0.6271676300578035, "acc_norm_stderr": 0.02603389061357628 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2916201117318436, "acc_stderr": 0.01520103251252044, "acc_norm": 0.2916201117318436, "acc_norm_stderr": 0.01520103251252044 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6078431372549019, "acc_stderr": 0.027956046165424516, "acc_norm": 0.6078431372549019, "acc_norm_stderr": 0.027956046165424516 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6077170418006431, "acc_stderr": 0.027731258647011998, "acc_norm": 0.6077170418006431, "acc_norm_stderr": 0.027731258647011998 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5771604938271605, "acc_stderr": 0.027487472980871588, "acc_norm": 0.5771604938271605, "acc_norm_stderr": 0.027487472980871588 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4326241134751773, "acc_stderr": 0.029555454236778862, "acc_norm": 0.4326241134751773, "acc_norm_stderr": 0.029555454236778862 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.408735332464146, "acc_stderr": 0.012555701346703385, "acc_norm": 0.408735332464146, "acc_norm_stderr": 0.012555701346703385 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4963235294117647, "acc_stderr": 0.030372015885428195, "acc_norm": 0.4963235294117647, "acc_norm_stderr": 0.030372015885428195 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5179738562091504, "acc_stderr": 0.020214761037872404, "acc_norm": 0.5179738562091504, "acc_norm_stderr": 0.020214761037872404 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5545454545454546, "acc_stderr": 0.047605488214603246, "acc_norm": 0.5545454545454546, "acc_norm_stderr": 0.047605488214603246 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6244897959183674, "acc_stderr": 0.031001209039894843, "acc_norm": 0.6244897959183674, "acc_norm_stderr": 0.031001209039894843 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7213930348258707, "acc_stderr": 0.031700561834973086, "acc_norm": 0.7213930348258707, "acc_norm_stderr": 0.031700561834973086 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7602339181286549, "acc_stderr": 0.03274485211946956, "acc_norm": 0.7602339181286549, "acc_norm_stderr": 0.03274485211946956 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2962056303549572, "mc1_stderr": 0.01598359510181139, "mc2": 0.4246156253859874, "mc2_stderr": 0.01586771249517698 }, "harness\|winogrande\|5": { "acc": 0.7355958958168903, "acc_stderr": 0.012394724896983799 }, "harness\|gsm8k\|5": { "acc": 0.19029567854435178, "acc_stderr": 0.010812347283182974 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_ajibawa-2023__Code-13B	[ "region:us" ]	2023-12-09T19:43:11+00:00	{"pretty_name": "Evaluation run of ajibawa-2023/Code-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ajibawa-2023/Code-13B](https://huggingface.co/ajibawa-2023/Code-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__Code-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T19:40:16.694610](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-13B/blob/main/results_2023-12-09T19-40-16.694610.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5315302469691541,\n \"acc_stderr\": 0.0338171547995471,\n \"acc_norm\": 0.5374650243523146,\n \"acc_norm_stderr\": 0.034550805778528454,\n \"mc1\": 0.2962056303549572,\n \"mc1_stderr\": 0.01598359510181139,\n \"mc2\": 0.4246156253859874,\n \"mc2_stderr\": 0.01586771249517698\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5511945392491467,\n \"acc_stderr\": 0.014534599585097665,\n \"acc_norm\": 0.5733788395904437,\n \"acc_norm_stderr\": 0.014453185592920293\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6420035849432384,\n \"acc_stderr\": 0.004784312972495391,\n \"acc_norm\": 0.8328022306313483,\n \"acc_norm_stderr\": 0.003723897305645496\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421296,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421296\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.040657710025626036,\n \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.040657710025626036\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.5773584905660377,\n \"acc_stderr\": 0.03040233144576954,\n \"acc_norm\": 0.5773584905660377,\n \"acc_norm_stderr\": 0.03040233144576954\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5902777777777778,\n \"acc_stderr\": 0.04112490974670788,\n \"acc_norm\": 0.5902777777777778,\n \"acc_norm_stderr\": 0.04112490974670788\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4797687861271676,\n \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108102,\n \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108102\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3508771929824561,\n \"acc_stderr\": 0.044895393502706986,\n \"acc_norm\": 0.3508771929824561,\n \"acc_norm_stderr\": 0.044895393502706986\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370331,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370331\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3201058201058201,\n \"acc_stderr\": 0.024026846392873506,\n \"acc_norm\": 0.3201058201058201,\n \"acc_norm_stderr\": 0.024026846392873506\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6290322580645161,\n \"acc_stderr\": 0.027480541887953593,\n \"acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.027480541887953593\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.03495334582162933,\n \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.03495334582162933\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.037425970438065864,\n \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.037425970438065864\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.6565656565656566,\n \"acc_stderr\": 0.03383201223244441,\n \"acc_norm\": 0.6565656565656566,\n \"acc_norm_stderr\": 0.03383201223244441\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7875647668393783,\n \"acc_stderr\": 0.02951928261681723,\n \"acc_norm\": 0.7875647668393783,\n \"acc_norm_stderr\": 0.02951928261681723\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.4717948717948718,\n \"acc_stderr\": 0.025310639254933882,\n \"acc_norm\": 0.4717948717948718,\n \"acc_norm_stderr\": 0.025310639254933882\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606648,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606648\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5378151260504201,\n \"acc_stderr\": 0.032385469487589795,\n \"acc_norm\": 0.5378151260504201,\n \"acc_norm_stderr\": 0.032385469487589795\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943342,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943342\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.6844036697247706,\n \"acc_stderr\": 0.019926117513869666,\n \"acc_norm\": 0.6844036697247706,\n \"acc_norm_stderr\": 0.019926117513869666\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.3611111111111111,\n \"acc_stderr\": 0.03275773486100999,\n \"acc_norm\": 0.3611111111111111,\n \"acc_norm_stderr\": 0.03275773486100999\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588663,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588663\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.70042194092827,\n \"acc_stderr\": 0.02981802474975309,\n \"acc_norm\": 0.70042194092827,\n \"acc_norm_stderr\": 0.02981802474975309\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6367713004484304,\n \"acc_stderr\": 0.03227790442850499,\n \"acc_norm\": 0.6367713004484304,\n \"acc_norm_stderr\": 0.03227790442850499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.043285772152629715,\n \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.043285772152629715\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.628099173553719,\n \"acc_stderr\": 0.044120158066245044,\n \"acc_norm\": 0.628099173553719,\n \"acc_norm_stderr\": 0.044120158066245044\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.04453197507374983,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.04453197507374983\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.6441717791411042,\n \"acc_stderr\": 0.03761521380046734,\n \"acc_norm\": 0.6441717791411042,\n \"acc_norm_stderr\": 0.03761521380046734\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.30357142857142855,\n \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.041858325989283136,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.041858325989283136\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.027236013946196697,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.027236013946196697\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7215836526181354,\n \"acc_stderr\": 0.016028295188992476,\n \"acc_norm\": 0.7215836526181354,\n \"acc_norm_stderr\": 0.016028295188992476\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.6271676300578035,\n \"acc_stderr\": 0.02603389061357628,\n \"acc_norm\": 0.6271676300578035,\n \"acc_norm_stderr\": 0.02603389061357628\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2916201117318436,\n \"acc_stderr\": 0.01520103251252044,\n \"acc_norm\": 0.2916201117318436,\n \"acc_norm_stderr\": 0.01520103251252044\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.027956046165424516,\n \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.027956046165424516\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6077170418006431,\n \"acc_stderr\": 0.027731258647011998,\n \"acc_norm\": 0.6077170418006431,\n \"acc_norm_stderr\": 0.027731258647011998\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5771604938271605,\n \"acc_stderr\": 0.027487472980871588,\n \"acc_norm\": 0.5771604938271605,\n \"acc_norm_stderr\": 0.027487472980871588\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4326241134751773,\n \"acc_stderr\": 0.029555454236778862,\n \"acc_norm\": 0.4326241134751773,\n \"acc_norm_stderr\": 0.029555454236778862\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.408735332464146,\n \"acc_stderr\": 0.012555701346703385,\n \"acc_norm\": 0.408735332464146,\n \"acc_norm_stderr\": 0.012555701346703385\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.4963235294117647,\n \"acc_stderr\": 0.030372015885428195,\n \"acc_norm\": 0.4963235294117647,\n \"acc_norm_stderr\": 0.030372015885428195\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.5179738562091504,\n \"acc_stderr\": 0.020214761037872404,\n \"acc_norm\": 0.5179738562091504,\n \"acc_norm_stderr\": 0.020214761037872404\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5545454545454546,\n \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.5545454545454546,\n \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.6244897959183674,\n \"acc_stderr\": 0.031001209039894843,\n \"acc_norm\": 0.6244897959183674,\n \"acc_norm_stderr\": 0.031001209039894843\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7213930348258707,\n \"acc_stderr\": 0.031700561834973086,\n \"acc_norm\": 0.7213930348258707,\n \"acc_norm_stderr\": 0.031700561834973086\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4397590361445783,\n \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.7602339181286549,\n \"acc_stderr\": 0.03274485211946956,\n \"acc_norm\": 0.7602339181286549,\n \"acc_norm_stderr\": 0.03274485211946956\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2962056303549572,\n \"mc1_stderr\": 0.01598359510181139,\n \"mc2\": 0.4246156253859874,\n \"mc2_stderr\": 0.01586771249517698\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7355958958168903,\n \"acc_stderr\": 0.012394724896983799\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.19029567854435178,\n \"acc_stderr\": 0.010812347283182974\n }\n}\n```", "repo_url": "https://huggingface.co/ajibawa-2023/Code-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|gsm8k\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hellaswag\|10_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T19-40-16.694610.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T19-40-16.694610.parquet", 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-40-16.694610.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T19_40_16.694610", "path": ["results_2023-12-09T19-40-16.694610.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T19-40-16.694610.parquet"]}]}]}	2023-12-09T19:43:53+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of ajibawa-2023/Code-13B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model ajibawa-2023/Code-13B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T19:40:16.694610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of ajibawa-2023/Code-13B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Code-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:40:16.694610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ajibawa-2023/Code-13B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Code-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:40:16.694610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 18, 31, 167, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ajibawa-2023/Code-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Code-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T19:40:16.694610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
fcbbfad17efcfece0821d550ea5f348ba1a233c7	# Dataset Card for Evaluation run of PulsarAI/Neural-una-cybertron-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/Neural-una-cybertron-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PulsarAI/Neural-una-cybertron-7b](https://huggingface.co/PulsarAI/Neural-una-cybertron-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PulsarAI__Neural-una-cybertron-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T19:49:04.690282](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Neural-una-cybertron-7b/blob/main/results_2023-12-09T19-49-04.690282.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6303659109315263, "acc_stderr": 0.032701507219088696, "acc_norm": 0.6326609738082676, "acc_norm_stderr": 0.033364878181962175, "mc1": 0.49938800489596086, "mc1_stderr": 0.01750348793889251, "mc2": 0.6498823682901811, "mc2_stderr": 0.01528184743332698 }, "harness\|arc:challenge\|25": { "acc": 0.6604095563139932, "acc_stderr": 0.013839039762820164, "acc_norm": 0.6902730375426621, "acc_norm_stderr": 0.013512058415238363 }, "harness\|hellaswag\|10": { "acc": 0.6704839673371839, "acc_stderr": 0.004690768393854475, "acc_norm": 0.8450507866958773, "acc_norm_stderr": 0.0036111673029597625 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7302631578947368, "acc_stderr": 0.03611780560284898, "acc_norm": 0.7302631578947368, "acc_norm_stderr": 0.03611780560284898 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6875, "acc_stderr": 0.038760854559127644, "acc_norm": 0.6875, "acc_norm_stderr": 0.038760854559127644 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155247, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155247 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7612903225806451, "acc_stderr": 0.02425107126220884, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.02425107126220884 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.029620227874790492, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.029620227874790492 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8497409326424871, "acc_stderr": 0.025787723180723875, "acc_norm": 0.8497409326424871, "acc_norm_stderr": 0.025787723180723875 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635474, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635474 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114993, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114993 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887034, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887034 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8311926605504587, "acc_stderr": 0.016060056268530343, "acc_norm": 0.8311926605504587, "acc_norm_stderr": 0.016060056268530343 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588667, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588667 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847835, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847835 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7520661157024794, "acc_stderr": 0.03941897526516302, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516302 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7177914110429447, "acc_stderr": 0.03536117886664742, "acc_norm": 0.7177914110429447, "acc_norm_stderr": 0.03536117886664742 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489277, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489277 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.80970625798212, "acc_stderr": 0.014036945850381398, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381398 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6965317919075145, "acc_stderr": 0.024752411960917205, "acc_norm": 0.6965317919075145, "acc_norm_stderr": 0.024752411960917205 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3787709497206704, "acc_stderr": 0.016223533510365113, "acc_norm": 0.3787709497206704, "acc_norm_stderr": 0.016223533510365113 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6601307189542484, "acc_stderr": 0.027121956071388856, "acc_norm": 0.6601307189542484, "acc_norm_stderr": 0.027121956071388856 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765137, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765137 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.475177304964539, "acc_stderr": 0.02979071924382972, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.02979071924382972 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.44784876140808344, "acc_stderr": 0.01270058240476822, "acc_norm": 0.44784876140808344, "acc_norm_stderr": 0.01270058240476822 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.0290294228156814, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.0290294228156814 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6552287581699346, "acc_stderr": 0.019228322018696647, "acc_norm": 0.6552287581699346, "acc_norm_stderr": 0.019228322018696647 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065677, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623325, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623325 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7894736842105263, "acc_stderr": 0.0312678171466318, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.0312678171466318 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.49938800489596086, "mc1_stderr": 0.01750348793889251, "mc2": 0.6498823682901811, "mc2_stderr": 0.01528184743332698 }, "harness\|winogrande\|5": { "acc": 0.8066298342541437, "acc_stderr": 0.011099796645920524 }, "harness\|gsm8k\|5": { "acc": 0.5231235784685367, "acc_stderr": 0.013757748544245336 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_PulsarAI__Neural-una-cybertron-7b	[ "region:us" ]	2023-12-09T19:51:54+00:00	{"pretty_name": "Evaluation run of PulsarAI/Neural-una-cybertron-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [PulsarAI/Neural-una-cybertron-7b](https://huggingface.co/PulsarAI/Neural-una-cybertron-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__Neural-una-cybertron-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T19:49:04.690282](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Neural-una-cybertron-7b/blob/main/results_2023-12-09T19-49-04.690282.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6303659109315263,\n \"acc_stderr\": 0.032701507219088696,\n \"acc_norm\": 0.6326609738082676,\n \"acc_norm_stderr\": 0.033364878181962175,\n \"mc1\": 0.49938800489596086,\n \"mc1_stderr\": 0.01750348793889251,\n \"mc2\": 0.6498823682901811,\n \"mc2_stderr\": 0.01528184743332698\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6604095563139932,\n \"acc_stderr\": 0.013839039762820164,\n \"acc_norm\": 0.6902730375426621,\n \"acc_norm_stderr\": 0.013512058415238363\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6704839673371839,\n \"acc_stderr\": 0.004690768393854475,\n \"acc_norm\": 0.8450507866958773,\n \"acc_norm_stderr\": 0.0036111673029597625\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155247,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155247\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7612903225806451,\n \"acc_stderr\": 0.02425107126220884,\n \"acc_norm\": 0.7612903225806451,\n \"acc_norm_stderr\": 0.02425107126220884\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790492,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790492\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8497409326424871,\n \"acc_stderr\": 0.025787723180723875,\n \"acc_norm\": 0.8497409326424871,\n \"acc_norm_stderr\": 0.025787723180723875\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635474,\n \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635474\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114993,\n \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 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"latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T19-49-04.690282.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T19_49_04.690282", "path": ["results_2023-12-09T19-49-04.690282.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T19-49-04.690282.parquet"]}]}]}	2023-12-09T19:52:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of PulsarAI/Neural-una-cybertron-7b ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model PulsarAI/Neural-una-cybertron-7b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T19:49:04.690282(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of PulsarAI/Neural-una-cybertron-7b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/Neural-una-cybertron-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:49:04.690282(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of PulsarAI/Neural-una-cybertron-7b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/Neural-una-cybertron-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:49:04.690282(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PulsarAI/Neural-una-cybertron-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/Neural-una-cybertron-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T19:49:04.690282(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
fb64048b6224d11ca19043e17b1d7e55fa52c18b	# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.4-preview2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.4-preview2](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.4-preview2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T19:57:57.872670](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview2/blob/main/results_2023-12-09T19-57-57.872670.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5440235971329553, "acc_stderr": 0.03410726380039453, "acc_norm": 0.5490928177495088, "acc_norm_stderr": 0.03483965758622219, "mc1": 0.37821297429620565, "mc1_stderr": 0.016976335907546866, "mc2": 0.5379290576758808, "mc2_stderr": 0.01514579551273296 }, "harness\|arc:challenge\|25": { "acc": 0.5093856655290102, "acc_stderr": 0.014608816322065, "acc_norm": 0.5298634812286689, "acc_norm_stderr": 0.014585305840007105 }, "harness\|hellaswag\|10": { "acc": 0.5582553276239793, "acc_stderr": 0.004955798214513426, "acc_norm": 0.7453694483170683, "acc_norm_stderr": 0.004347629889040944 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4222222222222222, "acc_stderr": 0.042667634040995814, "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.042667634040995814 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5526315789473685, "acc_stderr": 0.040463368839782514, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.040463368839782514 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6, "acc_stderr": 0.03015113445777629, "acc_norm": 0.6, "acc_norm_stderr": 0.03015113445777629 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.04122728707651282, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.04122728707651282 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5144508670520231, "acc_stderr": 0.03810871630454764, "acc_norm": 0.5144508670520231, "acc_norm_stderr": 0.03810871630454764 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.451063829787234, "acc_stderr": 0.03252909619613197, "acc_norm": 0.451063829787234, "acc_norm_stderr": 0.03252909619613197 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.38596491228070173, "acc_stderr": 0.045796394220704334, "acc_norm": 0.38596491228070173, "acc_norm_stderr": 0.045796394220704334 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.041546596717075474, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.041546596717075474 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.0248708152510571, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.0248708152510571 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6419354838709678, "acc_stderr": 0.02727389059430064, "acc_norm": 0.6419354838709678, "acc_norm_stderr": 0.02727389059430064 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.37438423645320196, "acc_stderr": 0.03405155380561952, "acc_norm": 0.37438423645320196, "acc_norm_stderr": 0.03405155380561952 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.036810508691615486, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.036810508691615486 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03173071239071724, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03173071239071724 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7150259067357513, "acc_stderr": 0.03257714077709662, "acc_norm": 0.7150259067357513, "acc_norm_stderr": 0.03257714077709662 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.517948717948718, "acc_stderr": 0.025334667080954915, "acc_norm": 0.517948717948718, "acc_norm_stderr": 0.025334667080954915 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085626, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085626 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5462184873949579, "acc_stderr": 0.032339434681820885, "acc_norm": 0.5462184873949579, "acc_norm_stderr": 0.032339434681820885 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7137614678899082, "acc_stderr": 0.01937943662891999, "acc_norm": 0.7137614678899082, "acc_norm_stderr": 0.01937943662891999 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4398148148148148, "acc_stderr": 0.03385177976044811, "acc_norm": 0.4398148148148148, "acc_norm_stderr": 0.03385177976044811 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.03242661719827218, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.03242661719827218 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.70042194092827, "acc_stderr": 0.029818024749753088, "acc_norm": 0.70042194092827, "acc_norm_stderr": 0.029818024749753088 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928275, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928275 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6694214876033058, "acc_stderr": 0.04294340845212093, "acc_norm": 0.6694214876033058, "acc_norm_stderr": 0.04294340845212093 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6759259259259259, "acc_stderr": 0.045245960070300476, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.045245960070300476 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6441717791411042, "acc_stderr": 0.03761521380046734, "acc_norm": 0.6441717791411042, "acc_norm_stderr": 0.03761521380046734 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973647, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973647 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.046561471100123514, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.046561471100123514 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7420178799489144, "acc_stderr": 0.01564583018834895, "acc_norm": 0.7420178799489144, "acc_norm_stderr": 0.01564583018834895 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5751445086705202, "acc_stderr": 0.026613350840261743, "acc_norm": 0.5751445086705202, "acc_norm_stderr": 0.026613350840261743 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.20446927374301677, "acc_stderr": 0.013488813404711903, "acc_norm": 0.20446927374301677, "acc_norm_stderr": 0.013488813404711903 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6176470588235294, "acc_stderr": 0.02782610930728369, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.02782610930728369 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6045016077170418, "acc_stderr": 0.02777091853142784, "acc_norm": 0.6045016077170418, "acc_norm_stderr": 0.02777091853142784 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5987654320987654, "acc_stderr": 0.0272725828498398, "acc_norm": 0.5987654320987654, "acc_norm_stderr": 0.0272725828498398 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3723404255319149, "acc_stderr": 0.028838921471251458, "acc_norm": 0.3723404255319149, "acc_norm_stderr": 0.028838921471251458 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.39374185136897, "acc_stderr": 0.012478532272564447, "acc_norm": 0.39374185136897, "acc_norm_stderr": 0.012478532272564447 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5772058823529411, "acc_stderr": 0.03000856284500348, "acc_norm": 0.5772058823529411, "acc_norm_stderr": 0.03000856284500348 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5147058823529411, "acc_stderr": 0.020219083895133924, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.020219083895133924 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.673469387755102, "acc_stderr": 0.03002105623844031, "acc_norm": 0.673469387755102, "acc_norm_stderr": 0.03002105623844031 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.736318407960199, "acc_stderr": 0.031157150869355586, "acc_norm": 0.736318407960199, "acc_norm_stderr": 0.031157150869355586 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.45180722891566266, "acc_stderr": 0.03874371556587953, "acc_norm": 0.45180722891566266, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7251461988304093, "acc_stderr": 0.03424042924691583, "acc_norm": 0.7251461988304093, "acc_norm_stderr": 0.03424042924691583 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.37821297429620565, "mc1_stderr": 0.016976335907546866, "mc2": 0.5379290576758808, "mc2_stderr": 0.01514579551273296 }, "harness\|winogrande\|5": { "acc": 0.739542225730071, "acc_stderr": 0.012334833671998285 }, "harness\|gsm8k\|5": { "acc": 0.25701288855193327, "acc_stderr": 0.012036781757428675 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview2	[ "region:us" ]	2023-12-09T20:00:47+00:00	{"pretty_name": "Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview2", "dataset_summary": "Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.4-preview2](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.4-preview2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T19:57:57.872670](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview2/blob/main/results_2023-12-09T19-57-57.872670.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5440235971329553,\n \"acc_stderr\": 0.03410726380039453,\n \"acc_norm\": 0.5490928177495088,\n \"acc_norm_stderr\": 0.03483965758622219,\n \"mc1\": 0.37821297429620565,\n \"mc1_stderr\": 0.016976335907546866,\n \"mc2\": 0.5379290576758808,\n \"mc2_stderr\": 0.01514579551273296\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5093856655290102,\n \"acc_stderr\": 0.014608816322065,\n \"acc_norm\": 0.5298634812286689,\n \"acc_norm_stderr\": 0.014585305840007105\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5582553276239793,\n \"acc_stderr\": 0.004955798214513426,\n \"acc_norm\": 0.7453694483170683,\n \"acc_norm_stderr\": 0.004347629889040944\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4222222222222222,\n \"acc_stderr\": 0.042667634040995814,\n \"acc_norm\": 0.4222222222222222,\n \"acc_norm_stderr\": 0.042667634040995814\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5526315789473685,\n \"acc_stderr\": 0.040463368839782514,\n \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.040463368839782514\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03015113445777629,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03015113445777629\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5833333333333334,\n \"acc_stderr\": 0.04122728707651282,\n \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.04122728707651282\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5144508670520231,\n \"acc_stderr\": 0.03810871630454764,\n \"acc_norm\": 0.5144508670520231,\n \"acc_norm_stderr\": 0.03810871630454764\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.451063829787234,\n \"acc_stderr\": 0.03252909619613197,\n \"acc_norm\": 0.451063829787234,\n \"acc_norm_stderr\": 0.03252909619613197\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.38596491228070173,\n \"acc_stderr\": 0.045796394220704334,\n \"acc_norm\": 0.38596491228070173,\n \"acc_norm_stderr\": 0.045796394220704334\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.041546596717075474,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.041546596717075474\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.37037037037037035,\n \"acc_stderr\": 0.0248708152510571,\n \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.0248708152510571\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6419354838709678,\n \"acc_stderr\": 0.02727389059430064,\n \"acc_norm\": 0.6419354838709678,\n \"acc_norm_stderr\": 0.02727389059430064\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.37438423645320196,\n \"acc_stderr\": 0.03405155380561952,\n \"acc_norm\": 0.37438423645320196,\n \"acc_norm_stderr\": 0.03405155380561952\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.036810508691615486,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.036810508691615486\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7150259067357513,\n \"acc_stderr\": 0.03257714077709662,\n \"acc_norm\": 0.7150259067357513,\n \"acc_norm_stderr\": 0.03257714077709662\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.517948717948718,\n \"acc_stderr\": 0.025334667080954915,\n \"acc_norm\": 0.517948717948718,\n \"acc_norm_stderr\": 0.025334667080954915\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5462184873949579,\n \"acc_stderr\": 0.032339434681820885,\n \"acc_norm\": 0.5462184873949579,\n \"acc_norm_stderr\": 0.032339434681820885\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.7137614678899082,\n \"acc_stderr\": 0.01937943662891999,\n \"acc_norm\": 0.7137614678899082,\n \"acc_norm_stderr\": 0.01937943662891999\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4398148148148148,\n \"acc_stderr\": 0.03385177976044811,\n \"acc_norm\": 0.4398148148148148,\n \"acc_norm_stderr\": 0.03385177976044811\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.03242661719827218,\n \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.03242661719827218\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.70042194092827,\n \"acc_stderr\": 0.029818024749753088,\n \"acc_norm\": 0.70042194092827,\n \"acc_norm_stderr\": 0.029818024749753088\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6322869955156951,\n \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.6322869955156951,\n \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212093,\n \"acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212093\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.045245960070300476,\n \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.045245960070300476\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.6441717791411042,\n \"acc_stderr\": 0.03761521380046734,\n \"acc_norm\": 0.6441717791411042,\n \"acc_norm_stderr\": 0.03761521380046734\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4017857142857143,\n \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.4017857142857143,\n \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.046561471100123514,\n \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.046561471100123514\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8504273504273504,\n \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7420178799489144,\n \"acc_stderr\": 0.01564583018834895,\n \"acc_norm\": 0.7420178799489144,\n \"acc_norm_stderr\": 0.01564583018834895\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.5751445086705202,\n \"acc_stderr\": 0.026613350840261743,\n \"acc_norm\": 0.5751445086705202,\n \"acc_norm_stderr\": 0.026613350840261743\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.20446927374301677,\n \"acc_stderr\": 0.013488813404711903,\n \"acc_norm\": 0.20446927374301677,\n \"acc_norm_stderr\": 0.013488813404711903\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.02782610930728369,\n \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.02782610930728369\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6045016077170418,\n \"acc_stderr\": 0.02777091853142784,\n \"acc_norm\": 0.6045016077170418,\n \"acc_norm_stderr\": 0.02777091853142784\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5987654320987654,\n \"acc_stderr\": 0.0272725828498398,\n \"acc_norm\": 0.5987654320987654,\n \"acc_norm_stderr\": 0.0272725828498398\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.3723404255319149,\n \"acc_stderr\": 0.028838921471251458,\n \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.028838921471251458\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.39374185136897,\n \"acc_stderr\": 0.012478532272564447,\n \"acc_norm\": 0.39374185136897,\n \"acc_norm_stderr\": 0.012478532272564447\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.5772058823529411,\n \"acc_stderr\": 0.03000856284500348,\n \"acc_norm\": 0.5772058823529411,\n \"acc_norm_stderr\": 0.03000856284500348\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.020219083895133924,\n \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.020219083895133924\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.673469387755102,\n \"acc_stderr\": 0.03002105623844031,\n \"acc_norm\": 0.673469387755102,\n \"acc_norm_stderr\": 0.03002105623844031\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.736318407960199,\n \"acc_stderr\": 0.031157150869355586,\n \"acc_norm\": 0.736318407960199,\n \"acc_norm_stderr\": 0.031157150869355586\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n 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run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T19:57:57.872670(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:57:57.872670(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T19:57:57.872670(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T19:57:57.872670(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
6e339fb6ca3d695277824b1b4a7e6ac20edc31a3	# Dataset Card for Evaluation run of ContextualAI/archangel_sft-kto_llama13b ## Dataset Description - Homepage: - Repository: https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [ContextualAI/archangel_sft-kto_llama13b](https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:01:05.918025](https://huggingface.co/datasets/open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b/blob/main/results_2023-12-09T20-01-05.918025.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.4808497396801513, "acc_stderr": 0.0342816178342491, "acc_norm": 0.48534799426464065, "acc_norm_stderr": 0.03504863417527385, "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015023, "mc2": 0.39418229629364515, "mc2_stderr": 0.013748123967336172 }, "harness\|arc:challenge\|25": { "acc": 0.5264505119453925, "acc_stderr": 0.01459093135812017, "acc_norm": 0.5614334470989761, "acc_norm_stderr": 0.014500682618212864 }, "harness\|hellaswag\|10": { "acc": 0.6093407687711612, "acc_stderr": 0.004869010152280754, "acc_norm": 0.8080063732324239, "acc_norm_stderr": 0.003930631369978262 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847415, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847415 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.46710526315789475, "acc_stderr": 0.04060127035236395, "acc_norm": 0.46710526315789475, "acc_norm_stderr": 0.04060127035236395 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4641509433962264, "acc_stderr": 0.030693675018458003, "acc_norm": 0.4641509433962264, "acc_norm_stderr": 0.030693675018458003 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4861111111111111, "acc_stderr": 0.04179596617581, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.04179596617581 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.41040462427745666, "acc_stderr": 0.037507570448955356, "acc_norm": 0.41040462427745666, "acc_norm_stderr": 0.037507570448955356 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179963, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179963 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.39574468085106385, "acc_stderr": 0.03196758697835361, "acc_norm": 0.39574468085106385, "acc_norm_stderr": 0.03196758697835361 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2719298245614035, "acc_stderr": 0.04185774424022056, "acc_norm": 0.2719298245614035, "acc_norm_stderr": 0.04185774424022056 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.43448275862068964, "acc_stderr": 0.041307408795554966, "acc_norm": 0.43448275862068964, "acc_norm_stderr": 0.041307408795554966 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.02264421261552521, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.02264421261552521 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.042163702135578345, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.042163702135578345 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5225806451612903, "acc_stderr": 0.028414985019707868, "acc_norm": 0.5225806451612903, "acc_norm_stderr": 0.028414985019707868 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.28078817733990147, "acc_stderr": 0.0316185633535861, "acc_norm": 0.28078817733990147, "acc_norm_stderr": 0.0316185633535861 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6121212121212121, "acc_stderr": 0.038049136539710114, "acc_norm": 0.6121212121212121, "acc_norm_stderr": 0.038049136539710114 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5454545454545454, "acc_stderr": 0.03547601494006937, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.03547601494006937 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836183, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836183 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.025294608023986472, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.025294608023986472 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712173, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712173 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.4579831932773109, "acc_stderr": 0.03236361111951941, "acc_norm": 0.4579831932773109, "acc_norm_stderr": 0.03236361111951941 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943342, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943342 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.618348623853211, "acc_stderr": 0.020828148517022582, "acc_norm": 0.618348623853211, "acc_norm_stderr": 0.020828148517022582 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.2916666666666667, "acc_stderr": 0.03099866630456052, "acc_norm": 0.2916666666666667, "acc_norm_stderr": 0.03099866630456052 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239171, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239171 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6919831223628692, "acc_stderr": 0.0300523893356057, "acc_norm": 0.6919831223628692, "acc_norm_stderr": 0.0300523893356057 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5291479820627802, "acc_stderr": 0.03350073248773403, "acc_norm": 0.5291479820627802, "acc_norm_stderr": 0.03350073248773403 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5801526717557252, "acc_stderr": 0.04328577215262971, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.04328577215262971 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.043913262867240704, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.043913262867240704 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.04830366024635331, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.04830366024635331 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5214723926380368, "acc_stderr": 0.03924746876751129, "acc_norm": 0.5214723926380368, "acc_norm_stderr": 0.03924746876751129 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.30357142857142855, "acc_stderr": 0.04364226155841044, "acc_norm": 0.30357142857142855, "acc_norm_stderr": 0.04364226155841044 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.0465614711001235, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.0465614711001235 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7307692307692307, "acc_stderr": 0.029058588303748842, "acc_norm": 0.7307692307692307, "acc_norm_stderr": 0.029058588303748842 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.55, "acc_stderr": 0.04999999999999999, "acc_norm": 0.55, "acc_norm_stderr": 0.04999999999999999 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6615581098339719, "acc_stderr": 0.016920869586210675, "acc_norm": 0.6615581098339719, "acc_norm_stderr": 0.016920869586210675 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5144508670520231, "acc_stderr": 0.02690784985628254, "acc_norm": 0.5144508670520231, "acc_norm_stderr": 0.02690784985628254 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2916201117318436, "acc_stderr": 0.015201032512520436, "acc_norm": 0.2916201117318436, "acc_norm_stderr": 0.015201032512520436 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5130718954248366, "acc_stderr": 0.028620130800700246, "acc_norm": 0.5130718954248366, "acc_norm_stderr": 0.028620130800700246 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5498392282958199, "acc_stderr": 0.028256660723360173, "acc_norm": 0.5498392282958199, "acc_norm_stderr": 0.028256660723360173 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5154320987654321, "acc_stderr": 0.02780749004427619, "acc_norm": 0.5154320987654321, "acc_norm_stderr": 0.02780749004427619 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.34397163120567376, "acc_stderr": 0.028338017428611324, "acc_norm": 0.34397163120567376, "acc_norm_stderr": 0.028338017428611324 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.37614080834419816, "acc_stderr": 0.012372214430599814, "acc_norm": 0.37614080834419816, "acc_norm_stderr": 0.012372214430599814 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904611, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904611 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4820261437908497, "acc_stderr": 0.020214761037872404, "acc_norm": 0.4820261437908497, "acc_norm_stderr": 0.020214761037872404 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6, "acc_stderr": 0.0469237132203465, "acc_norm": 0.6, "acc_norm_stderr": 0.0469237132203465 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5387755102040817, "acc_stderr": 0.031912820526692774, "acc_norm": 0.5387755102040817, "acc_norm_stderr": 0.031912820526692774 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6069651741293532, "acc_stderr": 0.0345368246603156, "acc_norm": 0.6069651741293532, "acc_norm_stderr": 0.0345368246603156 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.695906432748538, "acc_stderr": 0.0352821125824523, "acc_norm": 0.695906432748538, "acc_norm_stderr": 0.0352821125824523 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015023, "mc2": 0.39418229629364515, "mc2_stderr": 0.013748123967336172 }, "harness\|winogrande\|5": { "acc": 0.7616416732438832, "acc_stderr": 0.011974948667702311 }, "harness\|gsm8k\|5": { "acc": 0.1683093252463988, "acc_stderr": 0.010305695358125522 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b	[ "region:us" ]	2023-12-09T20:03:19+00:00	{"pretty_name": "Evaluation run of ContextualAI/archangel_sft-kto_llama13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [ContextualAI/archangel_sft-kto_llama13b](https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:01:05.918025](https://huggingface.co/datasets/open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b/blob/main/results_2023-12-09T20-01-05.918025.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4808497396801513,\n \"acc_stderr\": 0.0342816178342491,\n \"acc_norm\": 0.48534799426464065,\n \"acc_norm_stderr\": 0.03504863417527385,\n \"mc1\": 0.26193390452876375,\n \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.39418229629364515,\n \"mc2_stderr\": 0.013748123967336172\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5264505119453925,\n \"acc_stderr\": 0.01459093135812017,\n \"acc_norm\": 0.5614334470989761,\n \"acc_norm_stderr\": 0.014500682618212864\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6093407687711612,\n \"acc_stderr\": 0.004869010152280754,\n \"acc_norm\": 0.8080063732324239,\n \"acc_norm_stderr\": 0.003930631369978262\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.46710526315789475,\n \"acc_stderr\": 0.04060127035236395,\n \"acc_norm\": 0.46710526315789475,\n \"acc_norm_stderr\": 0.04060127035236395\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.4641509433962264,\n \"acc_stderr\": 0.030693675018458003,\n \"acc_norm\": 0.4641509433962264,\n \"acc_norm_stderr\": 0.030693675018458003\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4861111111111111,\n \"acc_stderr\": 0.04179596617581,\n \"acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.04179596617581\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.41040462427745666,\n \"acc_stderr\": 0.037507570448955356,\n \"acc_norm\": 0.41040462427745666,\n \"acc_norm_stderr\": 0.037507570448955356\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179963,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179963\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.39574468085106385,\n \"acc_stderr\": 0.03196758697835361,\n \"acc_norm\": 0.39574468085106385,\n \"acc_norm_stderr\": 0.03196758697835361\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2719298245614035,\n \"acc_stderr\": 0.04185774424022056,\n \"acc_norm\": 0.2719298245614035,\n \"acc_norm_stderr\": 0.04185774424022056\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.041307408795554966,\n \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.041307408795554966\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.02264421261552521,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.02264421261552521\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.042163702135578345,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.042163702135578345\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5225806451612903,\n \"acc_stderr\": 0.028414985019707868,\n \"acc_norm\": 0.5225806451612903,\n \"acc_norm_stderr\": 0.028414985019707868\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.28078817733990147,\n \"acc_stderr\": 0.0316185633535861,\n \"acc_norm\": 0.28078817733990147,\n \"acc_norm_stderr\": 0.0316185633535861\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6121212121212121,\n \"acc_stderr\": 0.038049136539710114,\n \"acc_norm\": 0.6121212121212121,\n \"acc_norm_stderr\": 0.038049136539710114\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.5454545454545454,\n \"acc_stderr\": 0.03547601494006937,\n \"acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.03547601494006937\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836183,\n \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836183\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.025294608023986472,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.025294608023986472\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712173,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712173\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.4579831932773109,\n \"acc_stderr\": 0.03236361111951941,\n \"acc_norm\": 0.4579831932773109,\n \"acc_norm_stderr\": 0.03236361111951941\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943342,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943342\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.618348623853211,\n \"acc_stderr\": 0.020828148517022582,\n \"acc_norm\": 0.618348623853211,\n \"acc_norm_stderr\": 0.020828148517022582\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.2916666666666667,\n \"acc_stderr\": 0.03099866630456052,\n \"acc_norm\": 0.2916666666666667,\n \"acc_norm_stderr\": 0.03099866630456052\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239171,\n \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239171\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.6919831223628692,\n \"acc_stderr\": 0.0300523893356057,\n \"acc_norm\": 0.6919831223628692,\n \"acc_norm_stderr\": 0.0300523893356057\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5291479820627802,\n \"acc_stderr\": 0.03350073248773403,\n \"acc_norm\": 0.5291479820627802,\n \"acc_norm_stderr\": 0.03350073248773403\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.04328577215262971,\n \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.04328577215262971\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.5214723926380368,\n \"acc_stderr\": 0.03924746876751129,\n \"acc_norm\": 0.5214723926380368,\n \"acc_norm_stderr\": 0.03924746876751129\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.30357142857142855,\n \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7307692307692307,\n \"acc_stderr\": 0.029058588303748842,\n \"acc_norm\": 0.7307692307692307,\n \"acc_norm_stderr\": 0.029058588303748842\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6615581098339719,\n \"acc_stderr\": 0.016920869586210675,\n \"acc_norm\": 0.6615581098339719,\n \"acc_norm_stderr\": 0.016920869586210675\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.5144508670520231,\n \"acc_stderr\": 0.02690784985628254,\n \"acc_norm\": 0.5144508670520231,\n \"acc_norm_stderr\": 0.02690784985628254\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2916201117318436,\n \"acc_stderr\": 0.015201032512520436,\n \"acc_norm\": 0.2916201117318436,\n \"acc_norm_stderr\": 0.015201032512520436\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.5130718954248366,\n \"acc_stderr\": 0.028620130800700246,\n \"acc_norm\": 0.5130718954248366,\n \"acc_norm_stderr\": 0.028620130800700246\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5498392282958199,\n \"acc_stderr\": 0.028256660723360173,\n \"acc_norm\": 0.5498392282958199,\n \"acc_norm_stderr\": 0.028256660723360173\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5154320987654321,\n \"acc_stderr\": 0.02780749004427619,\n \"acc_norm\": 0.5154320987654321,\n \"acc_norm_stderr\": 0.02780749004427619\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611324,\n \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611324\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.37614080834419816,\n \"acc_stderr\": 0.012372214430599814,\n \"acc_norm\": 0.37614080834419816,\n \"acc_norm_stderr\": 0.012372214430599814\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904611,\n \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904611\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.4820261437908497,\n \"acc_stderr\": 0.020214761037872404,\n \"acc_norm\": 0.4820261437908497,\n \"acc_norm_stderr\": 0.020214761037872404\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.5387755102040817,\n \"acc_stderr\": 0.031912820526692774,\n \"acc_norm\": 0.5387755102040817,\n \"acc_norm_stderr\": 0.031912820526692774\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6069651741293532,\n \"acc_stderr\": 0.0345368246603156,\n \"acc_norm\": 0.6069651741293532,\n \"acc_norm_stderr\": 0.0345368246603156\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4457831325301205,\n \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.0352821125824523,\n \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.0352821125824523\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.26193390452876375,\n \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.39418229629364515,\n \"mc2_stderr\": 0.013748123967336172\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702311\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.1683093252463988,\n \"acc_stderr\": 0.010305695358125522\n }\n}\n```", "repo_url": "https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|gsm8k\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hellaswag\|10_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-01-05.918025.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-01-05.918025.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-01-05.918025.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_01_05.918025", "path": ["results_2023-12-09T20-01-05.918025.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T20-01-05.918025.parquet"]}]}]}	2023-12-09T20:04:03+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of ContextualAI/archangel_sft-kto_llama13b ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model ContextualAI/archangel_sft-kto_llama13b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:01:05.918025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of ContextualAI/archangel_sft-kto_llama13b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model ContextualAI/archangel_sft-kto_llama13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:01:05.918025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ContextualAI/archangel_sft-kto_llama13b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model ContextualAI/archangel_sft-kto_llama13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:01:05.918025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 26, 31, 175, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ContextualAI/archangel_sft-kto_llama13b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ContextualAI/archangel_sft-kto_llama13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:01:05.918025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
de3a20f4e5d7e8668614618e1f8cde8e14065648	# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview ## Dataset Description - Homepage: - Repository: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.5-preview](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:01:18.948310](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview/blob/main/results_2023-12-09T20-01-18.948310.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5441057040654342, "acc_stderr": 0.03404499199717172, "acc_norm": 0.5501066597591592, "acc_norm_stderr": 0.034782781683925894, "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5516274394366725, "mc2_stderr": 0.01504190113817455 }, "harness\|arc:challenge\|25": { "acc": 0.4931740614334471, "acc_stderr": 0.014610029151379813, "acc_norm": 0.5273037542662116, "acc_norm_stderr": 0.014589589101985994 }, "harness\|hellaswag\|10": { "acc": 0.5624377614021111, "acc_stderr": 0.004950723480149757, "acc_norm": 0.7650866361282613, "acc_norm_stderr": 0.004230782375004432 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45185185185185184, "acc_stderr": 0.04299268905480864, "acc_norm": 0.45185185185185184, "acc_norm_stderr": 0.04299268905480864 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5723684210526315, "acc_stderr": 0.04026097083296562, "acc_norm": 0.5723684210526315, "acc_norm_stderr": 0.04026097083296562 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5886792452830188, "acc_stderr": 0.030285009259009794, "acc_norm": 0.5886792452830188, "acc_norm_stderr": 0.030285009259009794 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5902777777777778, "acc_stderr": 0.04112490974670787, "acc_norm": 0.5902777777777778, "acc_norm_stderr": 0.04112490974670787 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5086705202312138, "acc_stderr": 0.0381189098894041, "acc_norm": 0.5086705202312138, "acc_norm_stderr": 0.0381189098894041 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.27450980392156865, "acc_stderr": 0.04440521906179327, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.04440521906179327 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482758, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482758 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.02467786284133278, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.02467786284133278 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795132, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795132 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6290322580645161, "acc_stderr": 0.027480541887953593, "acc_norm": 0.6290322580645161, "acc_norm_stderr": 0.027480541887953593 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4187192118226601, "acc_stderr": 0.03471192860518468, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.03471192860518468 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6606060606060606, "acc_stderr": 0.03697442205031595, "acc_norm": 0.6606060606060606, "acc_norm_stderr": 0.03697442205031595 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7171717171717171, "acc_stderr": 0.032087795587867514, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.032087795587867514 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7046632124352331, "acc_stderr": 0.032922966391551414, "acc_norm": 0.7046632124352331, "acc_norm_stderr": 0.032922966391551414 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.49743589743589745, "acc_stderr": 0.025350672979412195, "acc_norm": 0.49743589743589745, "acc_norm_stderr": 0.025350672979412195 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2814814814814815, "acc_stderr": 0.02742001935094527, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.02742001935094527 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5210084033613446, "acc_stderr": 0.032449808499900284, "acc_norm": 0.5210084033613446, "acc_norm_stderr": 0.032449808499900284 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7064220183486238, "acc_stderr": 0.019525151122639667, "acc_norm": 0.7064220183486238, "acc_norm_stderr": 0.019525151122639667 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4351851851851852, "acc_stderr": 0.03381200005643525, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.03381200005643525 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7009803921568627, "acc_stderr": 0.03213325717373617, "acc_norm": 0.7009803921568627, "acc_norm_stderr": 0.03213325717373617 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6835443037974683, "acc_stderr": 0.030274974880218977, "acc_norm": 0.6835443037974683, "acc_norm_stderr": 0.030274974880218977 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6278026905829597, "acc_stderr": 0.03244305283008731, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.03244305283008731 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6564885496183206, "acc_stderr": 0.041649760719448786, "acc_norm": 0.6564885496183206, "acc_norm_stderr": 0.041649760719448786 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6611570247933884, "acc_stderr": 0.04320767807536671, "acc_norm": 0.6611570247933884, "acc_norm_stderr": 0.04320767807536671 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497752, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497752 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935574, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935574 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.046561471100123514, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.046561471100123514 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077785, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077785 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7420178799489144, "acc_stderr": 0.01564583018834895, "acc_norm": 0.7420178799489144, "acc_norm_stderr": 0.01564583018834895 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.026296227915613674, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.026296227915613674 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3094972067039106, "acc_stderr": 0.015461169002371544, "acc_norm": 0.3094972067039106, "acc_norm_stderr": 0.015461169002371544 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6045751633986928, "acc_stderr": 0.027996723180631438, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.027996723180631438 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6237942122186495, "acc_stderr": 0.027513925683549434, "acc_norm": 0.6237942122186495, "acc_norm_stderr": 0.027513925683549434 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6049382716049383, "acc_stderr": 0.02720111766692565, "acc_norm": 0.6049382716049383, "acc_norm_stderr": 0.02720111766692565 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3829787234042553, "acc_stderr": 0.028999080904806185, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.028999080904806185 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.394393741851369, "acc_stderr": 0.012482141665631184, "acc_norm": 0.394393741851369, "acc_norm_stderr": 0.012482141665631184 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5183823529411765, "acc_stderr": 0.030352303395351964, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.030352303395351964 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5196078431372549, "acc_stderr": 0.020212274976302954, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.020212274976302954 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6454545454545455, "acc_stderr": 0.04582004841505415, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.04582004841505415 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6326530612244898, "acc_stderr": 0.03086214492108756, "acc_norm": 0.6326530612244898, "acc_norm_stderr": 0.03086214492108756 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7611940298507462, "acc_stderr": 0.03014777593540922, "acc_norm": 0.7611940298507462, "acc_norm_stderr": 0.03014777593540922 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.43373493975903615, "acc_stderr": 0.038581589406855174, "acc_norm": 0.43373493975903615, "acc_norm_stderr": 0.038581589406855174 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7192982456140351, "acc_stderr": 0.034462962170884265, "acc_norm": 0.7192982456140351, "acc_norm_stderr": 0.034462962170884265 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5516274394366725, "mc2_stderr": 0.01504190113817455 }, "harness\|winogrande\|5": { "acc": 0.7434885556432518, "acc_stderr": 0.012273648008759987 }, "harness\|gsm8k\|5": { "acc": 0.22744503411675512, "acc_stderr": 0.011546363312548092 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview	[ "region:us" ]	2023-12-09T20:04:10+00:00	{"pretty_name": "Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview", "dataset_summary": "Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.5-preview](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:01:18.948310](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview/blob/main/results_2023-12-09T20-01-18.948310.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5441057040654342,\n \"acc_stderr\": 0.03404499199717172,\n \"acc_norm\": 0.5501066597591592,\n \"acc_norm_stderr\": 0.034782781683925894,\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5516274394366725,\n \"mc2_stderr\": 0.01504190113817455\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.4931740614334471,\n \"acc_stderr\": 0.014610029151379813,\n \"acc_norm\": 0.5273037542662116,\n \"acc_norm_stderr\": 0.014589589101985994\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5624377614021111,\n \"acc_stderr\": 0.004950723480149757,\n \"acc_norm\": 0.7650866361282613,\n \"acc_norm_stderr\": 0.004230782375004432\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296562,\n \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296562\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.5886792452830188,\n \"acc_stderr\": 0.030285009259009794,\n \"acc_norm\": 0.5886792452830188,\n \"acc_norm_stderr\": 0.030285009259009794\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5902777777777778,\n \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.5902777777777778,\n \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5086705202312138,\n \"acc_stderr\": 0.0381189098894041,\n \"acc_norm\": 0.5086705202312138,\n \"acc_norm_stderr\": 0.0381189098894041\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.04440521906179327,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.04440521906179327\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.37719298245614036,\n \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.02467786284133278,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.02467786284133278\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6290322580645161,\n \"acc_stderr\": 0.027480541887953593,\n \"acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.027480541887953593\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4187192118226601,\n \"acc_stderr\": 0.03471192860518468,\n \"acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.03471192860518468\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.03697442205031595,\n \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.03697442205031595\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7171717171717171,\n \"acc_stderr\": 0.032087795587867514,\n \"acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.032087795587867514\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7046632124352331,\n \"acc_stderr\": 0.032922966391551414,\n \"acc_norm\": 0.7046632124352331,\n \"acc_norm_stderr\": 0.032922966391551414\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.49743589743589745,\n \"acc_stderr\": 0.025350672979412195,\n \"acc_norm\": 0.49743589743589745,\n \"acc_norm_stderr\": 0.025350672979412195\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094527,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-01-18.948310.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_01_18.948310", "path": ["results_2023-12-09T20-01-18.948310.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T20-01-18.948310.parquet"]}]}]}	2023-12-09T20:04:54+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:01:18.948310(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:01:18.948310(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:01:18.948310(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 29, 31, 178, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:01:18.948310(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
68d1e81c8b91161637c52f0bbc03896de150707d	# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.5-preview2](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:04:44.969487](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview2/blob/main/results_2023-12-09T20-04-44.969487.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5160127414618038, "acc_stderr": 0.034322292179271935, "acc_norm": 0.5205499288114833, "acc_norm_stderr": 0.03505429574375341, "mc1": 0.37454100367197063, "mc1_stderr": 0.016943535128405324, "mc2": 0.5547100211730163, "mc2_stderr": 0.014944795662781923 }, "harness\|arc:challenge\|25": { "acc": 0.48293515358361777, "acc_stderr": 0.014602878388536593, "acc_norm": 0.5221843003412969, "acc_norm_stderr": 0.014597001927076138 }, "harness\|hellaswag\|10": { "acc": 0.5521808404700259, "acc_stderr": 0.004962534264751918, "acc_norm": 0.7554272057359092, "acc_norm_stderr": 0.004289551633772026 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04292596718256981, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04292596718256981 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5131578947368421, "acc_stderr": 0.04067533136309173, "acc_norm": 0.5131578947368421, "acc_norm_stderr": 0.04067533136309173 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5547169811320755, "acc_stderr": 0.030588052974270655, "acc_norm": 0.5547169811320755, "acc_norm_stderr": 0.030588052974270655 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5763888888888888, "acc_stderr": 0.04132125019723369, "acc_norm": 0.5763888888888888, "acc_norm_stderr": 0.04132125019723369 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5375722543352601, "acc_stderr": 0.0380168510452446, "acc_norm": 0.5375722543352601, "acc_norm_stderr": 0.0380168510452446 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.28431372549019607, "acc_stderr": 0.04488482852329017, "acc_norm": 0.28431372549019607, "acc_norm_stderr": 0.04488482852329017 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4127659574468085, "acc_stderr": 0.03218471141400351, "acc_norm": 0.4127659574468085, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3508771929824561, "acc_stderr": 0.044895393502707, "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.044895393502707 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.04164188720169377, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.04164188720169377 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.328042328042328, "acc_stderr": 0.024180497164376907, "acc_norm": 0.328042328042328, "acc_norm_stderr": 0.024180497164376907 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.040406101782088394, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.040406101782088394 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5967741935483871, "acc_stderr": 0.027906150826041136, "acc_norm": 0.5967741935483871, "acc_norm_stderr": 0.027906150826041136 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4187192118226601, "acc_stderr": 0.03471192860518468, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.03471192860518468 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6060606060606061, "acc_stderr": 0.03815494308688929, "acc_norm": 0.6060606060606061, "acc_norm_stderr": 0.03815494308688929 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6868686868686869, "acc_stderr": 0.033042050878136525, "acc_norm": 0.6868686868686869, "acc_norm_stderr": 0.033042050878136525 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.694300518134715, "acc_stderr": 0.033248379397581594, "acc_norm": 0.694300518134715, "acc_norm_stderr": 0.033248379397581594 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.47692307692307695, "acc_stderr": 0.025323990861736118, "acc_norm": 0.47692307692307695, "acc_norm_stderr": 0.025323990861736118 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.27037037037037037, "acc_stderr": 0.027080372815145668, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.027080372815145668 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5210084033613446, "acc_stderr": 0.032449808499900284, "acc_norm": 0.5210084033613446, "acc_norm_stderr": 0.032449808499900284 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6972477064220184, "acc_stderr": 0.01969871143475634, "acc_norm": 0.6972477064220184, "acc_norm_stderr": 0.01969871143475634 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502326, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502326 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.032962451101722294, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.032962451101722294 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6708860759493671, "acc_stderr": 0.03058732629470237, "acc_norm": 0.6708860759493671, "acc_norm_stderr": 0.03058732629470237 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5829596412556054, "acc_stderr": 0.03309266936071721, "acc_norm": 0.5829596412556054, "acc_norm_stderr": 0.03309266936071721 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5343511450381679, "acc_stderr": 0.043749285605997376, "acc_norm": 0.5343511450381679, "acc_norm_stderr": 0.043749285605997376 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6446280991735537, "acc_stderr": 0.0436923632657398, "acc_norm": 0.6446280991735537, "acc_norm_stderr": 0.0436923632657398 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497752, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497752 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935573, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935573 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3392857142857143, "acc_stderr": 0.04493949068613539, "acc_norm": 0.3392857142857143, "acc_norm_stderr": 0.04493949068613539 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6407766990291263, "acc_stderr": 0.047504583990416946, "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.047504583990416946 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8162393162393162, "acc_stderr": 0.025372139671722933, "acc_norm": 0.8162393162393162, "acc_norm_stderr": 0.025372139671722933 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6909323116219668, "acc_stderr": 0.0165249889197022, "acc_norm": 0.6909323116219668, "acc_norm_stderr": 0.0165249889197022 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5635838150289018, "acc_stderr": 0.026700545424943677, "acc_norm": 0.5635838150289018, "acc_norm_stderr": 0.026700545424943677 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2636871508379888, "acc_stderr": 0.014736926383761959, "acc_norm": 0.2636871508379888, "acc_norm_stderr": 0.014736926383761959 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.02818059632825929, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.02818059632825929 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5852090032154341, "acc_stderr": 0.027982680459759567, "acc_norm": 0.5852090032154341, "acc_norm_stderr": 0.027982680459759567 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5648148148148148, "acc_stderr": 0.027586006221607715, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.027586006221607715 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35815602836879434, "acc_stderr": 0.02860208586275942, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.02860208586275942 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.34485006518904826, "acc_stderr": 0.012139881006287052, "acc_norm": 0.34485006518904826, "acc_norm_stderr": 0.012139881006287052 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5404411764705882, "acc_stderr": 0.03027332507734575, "acc_norm": 0.5404411764705882, "acc_norm_stderr": 0.03027332507734575 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5081699346405228, "acc_stderr": 0.02022513434305726, "acc_norm": 0.5081699346405228, "acc_norm_stderr": 0.02022513434305726 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5959183673469388, "acc_stderr": 0.03141470802586589, "acc_norm": 0.5959183673469388, "acc_norm_stderr": 0.03141470802586589 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7164179104477612, "acc_stderr": 0.031871875379197966, "acc_norm": 0.7164179104477612, "acc_norm_stderr": 0.031871875379197966 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.463855421686747, "acc_stderr": 0.03882310850890593, "acc_norm": 0.463855421686747, "acc_norm_stderr": 0.03882310850890593 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.03565079670708311, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.03565079670708311 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.37454100367197063, "mc1_stderr": 0.016943535128405324, "mc2": 0.5547100211730163, "mc2_stderr": 0.014944795662781923 }, "harness\|winogrande\|5": { "acc": 0.7308602999210734, "acc_stderr": 0.012464911951268738 }, "harness\|gsm8k\|5": { "acc": 0.2759666413949962, "acc_stderr": 0.012312603010427355 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview2	[ "region:us" ]	2023-12-09T20:07:36+00:00	{"pretty_name": "Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview2", "dataset_summary": "Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.5-preview2](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:04:44.969487](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview2/blob/main/results_2023-12-09T20-04-44.969487.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5160127414618038,\n \"acc_stderr\": 0.034322292179271935,\n \"acc_norm\": 0.5205499288114833,\n \"acc_norm_stderr\": 0.03505429574375341,\n \"mc1\": 0.37454100367197063,\n \"mc1_stderr\": 0.016943535128405324,\n \"mc2\": 0.5547100211730163,\n \"mc2_stderr\": 0.014944795662781923\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.48293515358361777,\n \"acc_stderr\": 0.014602878388536593,\n \"acc_norm\": 0.5221843003412969,\n \"acc_norm_stderr\": 0.014597001927076138\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5521808404700259,\n \"acc_stderr\": 0.004962534264751918,\n \"acc_norm\": 0.7554272057359092,\n \"acc_norm_stderr\": 0.004289551633772026\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309173,\n \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309173\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.5547169811320755,\n \"acc_stderr\": 0.030588052974270655,\n \"acc_norm\": 0.5547169811320755,\n \"acc_norm_stderr\": 0.030588052974270655\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5763888888888888,\n \"acc_stderr\": 0.04132125019723369,\n \"acc_norm\": 0.5763888888888888,\n \"acc_norm_stderr\": 0.04132125019723369\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5375722543352601,\n \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.5375722543352601,\n \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4127659574468085,\n \"acc_stderr\": 0.03218471141400351,\n \"acc_norm\": 0.4127659574468085,\n \"acc_norm_stderr\": 0.03218471141400351\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3508771929824561,\n \"acc_stderr\": 0.044895393502707,\n \"acc_norm\": 0.3508771929824561,\n \"acc_norm_stderr\": 0.044895393502707\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.04164188720169377,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.04164188720169377\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.328042328042328,\n \"acc_stderr\": 0.024180497164376907,\n \"acc_norm\": 0.328042328042328,\n \"acc_norm_stderr\": 0.024180497164376907\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.040406101782088394,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.040406101782088394\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5967741935483871,\n \"acc_stderr\": 0.027906150826041136,\n \"acc_norm\": 0.5967741935483871,\n \"acc_norm_stderr\": 0.027906150826041136\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4187192118226601,\n \"acc_stderr\": 0.03471192860518468,\n \"acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.03471192860518468\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.03815494308688929,\n \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.03815494308688929\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.6868686868686869,\n \"acc_stderr\": 0.033042050878136525,\n \"acc_norm\": 0.6868686868686869,\n \"acc_norm_stderr\": 0.033042050878136525\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.694300518134715,\n \"acc_stderr\": 0.033248379397581594,\n \"acc_norm\": 0.694300518134715,\n \"acc_norm_stderr\": 0.033248379397581594\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.47692307692307695,\n \"acc_stderr\": 0.025323990861736118,\n \"acc_norm\": 0.47692307692307695,\n \"acc_norm_stderr\": 0.025323990861736118\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.27037037037037037,\n \"acc_stderr\": 0.027080372815145668,\n \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.027080372815145668\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5210084033613446,\n \"acc_stderr\": 0.032449808499900284,\n \"acc_norm\": 0.5210084033613446,\n \"acc_norm_stderr\": 0.032449808499900284\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.6972477064220184,\n \"acc_stderr\": 0.01969871143475634,\n \"acc_norm\": 0.6972477064220184,\n \"acc_norm_stderr\": 0.01969871143475634\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502326,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502326\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.032962451101722294,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.032962451101722294\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.6708860759493671,\n \"acc_stderr\": 0.03058732629470237,\n \"acc_norm\": 0.6708860759493671,\n \"acc_norm_stderr\": 0.03058732629470237\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5829596412556054,\n \"acc_stderr\": 0.03309266936071721,\n \"acc_norm\": 0.5829596412556054,\n \"acc_norm_stderr\": 0.03309266936071721\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.5343511450381679,\n \"acc_stderr\": 0.043749285605997376,\n \"acc_norm\": 0.5343511450381679,\n \"acc_norm_stderr\": 0.043749285605997376\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.6446280991735537,\n \"acc_stderr\": 0.0436923632657398,\n \"acc_norm\": 0.6446280991735537,\n \"acc_norm_stderr\": 0.0436923632657398\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04557239513497752,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04557239513497752\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.6625766871165644,\n \"acc_stderr\": 0.03714908409935573,\n \"acc_norm\": 0.6625766871165644,\n \"acc_norm_stderr\": 0.03714908409935573\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3392857142857143,\n \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.6407766990291263,\n \"acc_stderr\": 0.047504583990416946,\n \"acc_norm\": 0.6407766990291263,\n \"acc_norm_stderr\": 0.047504583990416946\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8162393162393162,\n \"acc_stderr\": 0.025372139671722933,\n \"acc_norm\": 0.8162393162393162,\n \"acc_norm_stderr\": 0.025372139671722933\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6909323116219668,\n \"acc_stderr\": 0.0165249889197022,\n \"acc_norm\": 0.6909323116219668,\n \"acc_norm_stderr\": 0.0165249889197022\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.5635838150289018,\n \"acc_stderr\": 0.026700545424943677,\n \"acc_norm\": 0.5635838150289018,\n \"acc_norm_stderr\": 0.026700545424943677\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2636871508379888,\n \"acc_stderr\": 0.014736926383761959,\n \"acc_norm\": 0.2636871508379888,\n \"acc_norm_stderr\": 0.014736926383761959\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.02818059632825929,\n \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.02818059632825929\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5852090032154341,\n \"acc_stderr\": 0.027982680459759567,\n \"acc_norm\": 0.5852090032154341,\n \"acc_norm_stderr\": 0.027982680459759567\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5648148148148148,\n \"acc_stderr\": 0.027586006221607715,\n \"acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.027586006221607715\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.35815602836879434,\n \"acc_stderr\": 0.02860208586275942,\n \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.02860208586275942\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.34485006518904826,\n \"acc_stderr\": 0.012139881006287052,\n \"acc_norm\": 0.34485006518904826,\n \"acc_norm_stderr\": 0.012139881006287052\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.5404411764705882,\n \"acc_stderr\": 0.03027332507734575,\n \"acc_norm\": 0.5404411764705882,\n \"acc_norm_stderr\": 0.03027332507734575\n },\n 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run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:04:44.969487(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:04:44.969487(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:04:44.969487(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.5-preview2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:04:44.969487(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
34cdc8108b5c75bb7444ad559e22821ea05454cf	# Dataset Card for Evaluation run of uukuguy/speechless-code-mistral-7b-v2.0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [uukuguy/speechless-code-mistral-7b-v2.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v2.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T13:58:45.141578](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v2.0/blob/main/results_2024-01-04T13-58-45.141578.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5140785549022919, "acc_stderr": 0.034312631751023934, "acc_norm": 0.517073732009842, "acc_norm_stderr": 0.03502546155916221, "mc1": 0.35128518971848227, "mc1_stderr": 0.0167113581635444, "mc2": 0.5205221363822641, "mc2_stderr": 0.01546112612953185 }, "harness\|arc:challenge\|25": { "acc": 0.49146757679180886, "acc_stderr": 0.014609263165632182, "acc_norm": 0.523037542662116, "acc_norm_stderr": 0.01459587320535827 }, "harness\|hellaswag\|10": { "acc": 0.569308902609042, "acc_stderr": 0.004941609820763585, "acc_norm": 0.7561242780322645, "acc_norm_stderr": 0.004285410130466108 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5460526315789473, "acc_stderr": 0.04051646342874141, "acc_norm": 0.5460526315789473, "acc_norm_stderr": 0.04051646342874141 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5509433962264151, "acc_stderr": 0.030612730713641095, "acc_norm": 0.5509433962264151, "acc_norm_stderr": 0.030612730713641095 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.04999999999999999, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999999 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.49710982658959535, "acc_stderr": 0.03812400565974833, "acc_norm": 0.49710982658959535, "acc_norm_stderr": 0.03812400565974833 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.043898699568087785, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.043898699568087785 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.46382978723404256, "acc_stderr": 0.032600385118357715, "acc_norm": 0.46382978723404256, "acc_norm_stderr": 0.032600385118357715 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3306878306878307, "acc_stderr": 0.024229965298425075, "acc_norm": 0.3306878306878307, "acc_norm_stderr": 0.024229965298425075 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3492063492063492, "acc_stderr": 0.042639068927951336, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.042639068927951336 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5838709677419355, "acc_stderr": 0.028040981380761536, "acc_norm": 0.5838709677419355, "acc_norm_stderr": 0.028040981380761536 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.41379310344827586, "acc_stderr": 0.03465304488406796, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.03465304488406796 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6424242424242425, "acc_stderr": 0.03742597043806586, "acc_norm": 0.6424242424242425, "acc_norm_stderr": 0.03742597043806586 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6565656565656566, "acc_stderr": 0.03383201223244442, "acc_norm": 0.6565656565656566, "acc_norm_stderr": 0.03383201223244442 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7046632124352331, "acc_stderr": 0.0329229663915514, "acc_norm": 0.7046632124352331, "acc_norm_stderr": 0.0329229663915514 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.45384615384615384, "acc_stderr": 0.025242770987126174, "acc_norm": 0.45384615384615384, "acc_norm_stderr": 0.025242770987126174 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085626, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085626 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5126050420168067, "acc_stderr": 0.03246816765752174, "acc_norm": 0.5126050420168067, "acc_norm_stderr": 0.03246816765752174 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.037579499229433426, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.037579499229433426 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6605504587155964, "acc_stderr": 0.02030210934266235, "acc_norm": 0.6605504587155964, "acc_norm_stderr": 0.02030210934266235 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.36574074074074076, "acc_stderr": 0.03284738857647206, "acc_norm": 0.36574074074074076, "acc_norm_stderr": 0.03284738857647206 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6519607843137255, "acc_stderr": 0.03343311240488418, "acc_norm": 0.6519607843137255, "acc_norm_stderr": 0.03343311240488418 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6708860759493671, "acc_stderr": 0.03058732629470236, "acc_norm": 0.6708860759493671, "acc_norm_stderr": 0.03058732629470236 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6143497757847534, "acc_stderr": 0.03266842214289201, "acc_norm": 0.6143497757847534, "acc_norm_stderr": 0.03266842214289201 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5801526717557252, "acc_stderr": 0.043285772152629715, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.043285772152629715 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6694214876033058, "acc_stderr": 0.04294340845212093, "acc_norm": 0.6694214876033058, "acc_norm_stderr": 0.04294340845212093 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6759259259259259, "acc_stderr": 0.04524596007030048, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.04524596007030048 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6134969325153374, "acc_stderr": 0.03825825548848607, "acc_norm": 0.6134969325153374, "acc_norm_stderr": 0.03825825548848607 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6601941747572816, "acc_stderr": 0.046897659372781335, "acc_norm": 0.6601941747572816, "acc_norm_stderr": 0.046897659372781335 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.782051282051282, "acc_stderr": 0.027046857630716677, "acc_norm": 0.782051282051282, "acc_norm_stderr": 0.027046857630716677 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.665389527458493, "acc_stderr": 0.016873468641592157, "acc_norm": 0.665389527458493, "acc_norm_stderr": 0.016873468641592157 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5606936416184971, "acc_stderr": 0.026720034380514998, "acc_norm": 0.5606936416184971, "acc_norm_stderr": 0.026720034380514998 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2770949720670391, "acc_stderr": 0.014968772435812143, "acc_norm": 0.2770949720670391, "acc_norm_stderr": 0.014968772435812143 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5163398692810458, "acc_stderr": 0.02861462475280544, "acc_norm": 0.5163398692810458, "acc_norm_stderr": 0.02861462475280544 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5852090032154341, "acc_stderr": 0.02798268045975956, "acc_norm": 0.5852090032154341, "acc_norm_stderr": 0.02798268045975956 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5524691358024691, "acc_stderr": 0.027667138569422704, "acc_norm": 0.5524691358024691, "acc_norm_stderr": 0.027667138569422704 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.32978723404255317, "acc_stderr": 0.0280459469420424, "acc_norm": 0.32978723404255317, "acc_norm_stderr": 0.0280459469420424 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3898305084745763, "acc_stderr": 0.012456386619082604, "acc_norm": 0.3898305084745763, "acc_norm_stderr": 0.012456386619082604 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.39338235294117646, "acc_stderr": 0.029674288281311183, "acc_norm": 0.39338235294117646, "acc_norm_stderr": 0.029674288281311183 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5016339869281046, "acc_stderr": 0.020227726838150117, "acc_norm": 0.5016339869281046, "acc_norm_stderr": 0.020227726838150117 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5346938775510204, "acc_stderr": 0.03193207024425314, "acc_norm": 0.5346938775510204, "acc_norm_stderr": 0.03193207024425314 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.746268656716418, "acc_stderr": 0.03076944496729602, "acc_norm": 0.746268656716418, "acc_norm_stderr": 0.03076944496729602 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6608187134502924, "acc_stderr": 0.03631053496488905, "acc_norm": 0.6608187134502924, "acc_norm_stderr": 0.03631053496488905 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35128518971848227, "mc1_stderr": 0.0167113581635444, "mc2": 0.5205221363822641, "mc2_stderr": 0.01546112612953185 }, "harness\|winogrande\|5": { "acc": 0.7134964483030781, "acc_stderr": 0.01270703013996038 }, "harness\|gsm8k\|5": { "acc": 0.35633055344958303, "acc_stderr": 0.01319168503135746 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v2.0	[ "region:us" ]	2023-12-09T20:11:15+00:00	{"pretty_name": "Evaluation run of uukuguy/speechless-code-mistral-7b-v2.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [uukuguy/speechless-code-mistral-7b-v2.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v2.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-04T13:58:45.141578](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v2.0/blob/main/results_2024-01-04T13-58-45.141578.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5140785549022919,\n \"acc_stderr\": 0.034312631751023934,\n \"acc_norm\": 0.517073732009842,\n \"acc_norm_stderr\": 0.03502546155916221,\n \"mc1\": 0.35128518971848227,\n \"mc1_stderr\": 0.0167113581635444,\n \"mc2\": 0.5205221363822641,\n \"mc2_stderr\": 0.01546112612953185\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.49146757679180886,\n \"acc_stderr\": 0.014609263165632182,\n \"acc_norm\": 0.523037542662116,\n \"acc_norm_stderr\": 0.01459587320535827\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.569308902609042,\n \"acc_stderr\": 0.004941609820763585,\n \"acc_norm\": 0.7561242780322645,\n \"acc_norm_stderr\": 0.004285410130466108\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5460526315789473,\n \"acc_stderr\": 0.04051646342874141,\n \"acc_norm\": 0.5460526315789473,\n \"acc_norm_stderr\": 0.04051646342874141\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.5509433962264151,\n \"acc_stderr\": 0.030612730713641095,\n \"acc_norm\": 0.5509433962264151,\n \"acc_norm_stderr\": 0.030612730713641095\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.03812400565974833,\n \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.03812400565974833\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.043898699568087785,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.043898699568087785\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.46382978723404256,\n \"acc_stderr\": 0.032600385118357715,\n \"acc_norm\": 0.46382978723404256,\n \"acc_norm_stderr\": 0.032600385118357715\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.40350877192982454,\n \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3306878306878307,\n \"acc_stderr\": 0.024229965298425075,\n \"acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.024229965298425075\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 0.042639068927951336,\n \"acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.042639068927951336\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5838709677419355,\n \"acc_stderr\": 0.028040981380761536,\n \"acc_norm\": 0.5838709677419355,\n \"acc_norm_stderr\": 0.028040981380761536\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406796,\n \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406796\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.03742597043806586,\n \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.03742597043806586\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.6565656565656566,\n \"acc_stderr\": 0.03383201223244442,\n \"acc_norm\": 0.6565656565656566,\n \"acc_norm_stderr\": 0.03383201223244442\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7046632124352331,\n \"acc_stderr\": 0.0329229663915514,\n \"acc_norm\": 0.7046632124352331,\n \"acc_norm_stderr\": 0.0329229663915514\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.45384615384615384,\n \"acc_stderr\": 0.025242770987126174,\n \"acc_norm\": 0.45384615384615384,\n \"acc_norm_stderr\": 0.025242770987126174\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5126050420168067,\n \"acc_stderr\": 0.03246816765752174,\n \"acc_norm\": 0.5126050420168067,\n \"acc_norm_stderr\": 0.03246816765752174\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.037579499229433426,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.037579499229433426\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.6605504587155964,\n \"acc_stderr\": 0.02030210934266235,\n \"acc_norm\": 0.6605504587155964,\n \"acc_norm_stderr\": 0.02030210934266235\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.36574074074074076,\n \"acc_stderr\": 0.03284738857647206,\n \"acc_norm\": 0.36574074074074076,\n \"acc_norm_stderr\": 0.03284738857647206\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.6519607843137255,\n \"acc_stderr\": 0.03343311240488418,\n \"acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.03343311240488418\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.6708860759493671,\n \"acc_stderr\": 0.03058732629470236,\n \"acc_norm\": 0.6708860759493671,\n \"acc_norm_stderr\": 0.03058732629470236\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6143497757847534,\n \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.043285772152629715,\n \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.043285772152629715\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212093,\n \"acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212093\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.04524596007030048,\n \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.04524596007030048\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.6134969325153374,\n \"acc_stderr\": 0.03825825548848607,\n \"acc_norm\": 0.6134969325153374,\n \"acc_norm_stderr\": 0.03825825548848607\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.046897659372781335,\n \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.046897659372781335\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.782051282051282,\n \"acc_stderr\": 0.027046857630716677,\n \"acc_norm\": 0.782051282051282,\n \"acc_norm_stderr\": 0.027046857630716677\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.665389527458493,\n \"acc_stderr\": 0.016873468641592157,\n \"acc_norm\": 0.665389527458493,\n \"acc_norm_stderr\": 0.016873468641592157\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.5606936416184971,\n \"acc_stderr\": 0.026720034380514998,\n \"acc_norm\": 0.5606936416184971,\n \"acc_norm_stderr\": 0.026720034380514998\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2770949720670391,\n \"acc_stderr\": 0.014968772435812143,\n \"acc_norm\": 0.2770949720670391,\n \"acc_norm_stderr\": 0.014968772435812143\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.5163398692810458,\n \"acc_stderr\": 0.02861462475280544,\n \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.02861462475280544\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5852090032154341,\n \"acc_stderr\": 0.02798268045975956,\n \"acc_norm\": 0.5852090032154341,\n \"acc_norm_stderr\": 0.02798268045975956\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5524691358024691,\n \"acc_stderr\": 0.027667138569422704,\n \"acc_norm\": 0.5524691358024691,\n \"acc_norm_stderr\": 0.027667138569422704\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.32978723404255317,\n \"acc_stderr\": 0.0280459469420424,\n \"acc_norm\": 0.32978723404255317,\n \"acc_norm_stderr\": 0.0280459469420424\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3898305084745763,\n \"acc_stderr\": 0.012456386619082604,\n \"acc_norm\": 0.3898305084745763,\n \"acc_norm_stderr\": 0.012456386619082604\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.39338235294117646,\n \"acc_stderr\": 0.029674288281311183,\n \"acc_norm\": 0.39338235294117646,\n \"acc_norm_stderr\": 0.029674288281311183\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.5016339869281046,\n \"acc_stderr\": 0.020227726838150117,\n \"acc_norm\": 0.5016339869281046,\n \"acc_norm_stderr\": 0.020227726838150117\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.5346938775510204,\n \"acc_stderr\": 0.03193207024425314,\n \"acc_norm\": 0.5346938775510204,\n \"acc_norm_stderr\": 0.03193207024425314\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.746268656716418,\n \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.746268656716418,\n \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4397590361445783,\n \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.6608187134502924,\n \"acc_stderr\": 0.03631053496488905,\n \"acc_norm\": 0.6608187134502924,\n \"acc_norm_stderr\": 0.03631053496488905\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.35128518971848227,\n \"mc1_stderr\": 0.0167113581635444,\n \"mc2\": 0.5205221363822641,\n \"mc2_stderr\": 0.01546112612953185\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7134964483030781,\n \"acc_stderr\": 0.01270703013996038\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.35633055344958303,\n \"acc_stderr\": 0.01319168503135746\n }\n}\n```", "repo_url": "https://huggingface.co/uukuguy/speechless-code-mistral-7b-v2.0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|arc:challenge\|25_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|gsm8k\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|gsm8k\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hellaswag\|10_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hellaswag\|10_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-08-24.695971.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-08-24.695971.parquet", 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["/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-sociology\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-virology\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|truthfulqa:mc\|0_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["/details_harness\|winogrande\|5_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2024-01-04T13-58-45.141578.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_08_24.695971", "path": ["results_2023-12-09T20-08-24.695971.parquet"]}, {"split": "2024_01_04T13_58_45.141578", "path": ["results_2024-01-04T13-58-45.141578.parquet"]}, {"split": "latest", "path": ["results_2024-01-04T13-58-45.141578.parquet"]}]}]}	2024-01-04T14:01:29+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of uukuguy/speechless-code-mistral-7b-v2.0 Dataset automatically created during the evaluation run of model uukuguy/speechless-code-mistral-7b-v2.0 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-01-04T13:58:45.141578(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of uukuguy/speechless-code-mistral-7b-v2.0\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-code-mistral-7b-v2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-04T13:58:45.141578(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of uukuguy/speechless-code-mistral-7b-v2.0\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-code-mistral-7b-v2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-04T13:58:45.141578(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 197, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of uukuguy/speechless-code-mistral-7b-v2.0\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-code-mistral-7b-v2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-04T13:58:45.141578(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
b81e9d8be8a84fcb9d610f58ae622ff84c3c9870	# Dataset Card for Evaluation run of Locutusque/Orca-2-13B-no_robots ## Dataset Description - Homepage: - Repository: https://huggingface.co/Locutusque/Orca-2-13B-no_robots - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Locutusque/Orca-2-13B-no_robots](https://huggingface.co/Locutusque/Orca-2-13B-no_robots) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Locutusque__Orca-2-13B-no_robots", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:22:23.375628](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Orca-2-13B-no_robots/blob/main/results_2023-12-09T20-22-23.375628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6001251554964193, "acc_stderr": 0.032923087084061796, "acc_norm": 0.605904200867543, "acc_norm_stderr": 0.03362523660489667, "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5117394616239719, "mc2_stderr": 0.014749980218549294 }, "harness\|arc:challenge\|25": { "acc": 0.5622866894197952, "acc_stderr": 0.014497573881108288, "acc_norm": 0.591296928327645, "acc_norm_stderr": 0.014365750345427006 }, "harness\|hellaswag\|10": { "acc": 0.6075482971519618, "acc_stderr": 0.004872984492967997, "acc_norm": 0.7956582354112727, "acc_norm_stderr": 0.004023957334461984 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7302631578947368, "acc_stderr": 0.03611780560284898, "acc_norm": 0.7302631578947368, "acc_norm_stderr": 0.03611780560284898 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319875, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319875 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6805555555555556, "acc_stderr": 0.038990736873573344, "acc_norm": 0.6805555555555556, "acc_norm_stderr": 0.038990736873573344 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5664739884393064, "acc_stderr": 0.03778621079092056, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.03778621079092056 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3137254901960784, "acc_stderr": 0.04617034827006718, "acc_norm": 0.3137254901960784, "acc_norm_stderr": 0.04617034827006718 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5361702127659574, "acc_stderr": 0.03260038511835771, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.03260038511835771 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.30701754385964913, "acc_stderr": 0.0433913832257986, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.0433913832257986 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482758, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482758 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.36507936507936506, "acc_stderr": 0.02479606060269995, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.02479606060269995 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04216370213557835, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04216370213557835 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7225806451612903, "acc_stderr": 0.025470196835900055, "acc_norm": 0.7225806451612903, "acc_norm_stderr": 0.025470196835900055 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.47783251231527096, "acc_stderr": 0.035145285621750094, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.035145285621750094 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03477691162163659, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03477691162163659 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7424242424242424, "acc_stderr": 0.03115626951964683, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.03115626951964683 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8601036269430051, "acc_stderr": 0.02503387058301518, "acc_norm": 0.8601036269430051, "acc_norm_stderr": 0.02503387058301518 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5743589743589743, "acc_stderr": 0.02506909438729654, "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.02506909438729654 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.634453781512605, "acc_stderr": 0.03128217706368461, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.03128217706368461 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8073394495412844, "acc_stderr": 0.016909276884936073, "acc_norm": 0.8073394495412844, "acc_norm_stderr": 0.016909276884936073 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7990196078431373, "acc_stderr": 0.02812597226565437, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.02812597226565437 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503217, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503217 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6591928251121076, "acc_stderr": 0.031811497470553604, "acc_norm": 0.6591928251121076, "acc_norm_stderr": 0.031811497470553604 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7099236641221374, "acc_stderr": 0.03980066246467765, "acc_norm": 0.7099236641221374, "acc_norm_stderr": 0.03980066246467765 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6993865030674846, "acc_stderr": 0.03602511318806771, "acc_norm": 0.6993865030674846, "acc_norm_stderr": 0.03602511318806771 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04547960999764377, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04547960999764377 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384493, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384493 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489274, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489274 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7828863346104725, "acc_stderr": 0.01474312539482329, "acc_norm": 0.7828863346104725, "acc_norm_stderr": 0.01474312539482329 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.025416003773165545, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.025416003773165545 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.311731843575419, "acc_stderr": 0.015491756531894635, "acc_norm": 0.311731843575419, "acc_norm_stderr": 0.015491756531894635 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6699346405228758, "acc_stderr": 0.026925654653615693, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.026925654653615693 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7160493827160493, "acc_stderr": 0.02508947852376513, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.02508947852376513 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.44680851063829785, "acc_stderr": 0.029658235097666904, "acc_norm": 0.44680851063829785, "acc_norm_stderr": 0.029658235097666904 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.43089960886571055, "acc_stderr": 0.012647695889547235, "acc_norm": 0.43089960886571055, "acc_norm_stderr": 0.012647695889547235 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5808823529411765, "acc_stderr": 0.029972807170464622, "acc_norm": 0.5808823529411765, "acc_norm_stderr": 0.029972807170464622 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6176470588235294, "acc_stderr": 0.019659922493623354, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.019659922493623354 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.04607582090719976, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.04607582090719976 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.736318407960199, "acc_stderr": 0.03115715086935557, "acc_norm": 0.736318407960199, "acc_norm_stderr": 0.03115715086935557 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5117394616239719, "mc2_stderr": 0.014749980218549294 }, "harness\|winogrande\|5": { "acc": 0.8034727703235991, "acc_stderr": 0.011168120593569576 }, "harness\|gsm8k\|5": { "acc": 0.27293404094010615, "acc_stderr": 0.012270381151108754 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Locutusque__Orca-2-13B-no_robots	[ "region:us" ]	2023-12-09T20:25:19+00:00	{"pretty_name": "Evaluation run of Locutusque/Orca-2-13B-no_robots", "dataset_summary": "Dataset automatically created during the evaluation run of model [Locutusque/Orca-2-13B-no_robots](https://huggingface.co/Locutusque/Orca-2-13B-no_robots) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Locutusque__Orca-2-13B-no_robots\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:22:23.375628](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Orca-2-13B-no_robots/blob/main/results_2023-12-09T20-22-23.375628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6001251554964193,\n \"acc_stderr\": 0.032923087084061796,\n \"acc_norm\": 0.605904200867543,\n \"acc_norm_stderr\": 0.03362523660489667,\n \"mc1\": 0.35495716034271724,\n \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5117394616239719,\n \"mc2_stderr\": 0.014749980218549294\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5622866894197952,\n \"acc_stderr\": 0.014497573881108288,\n \"acc_norm\": 0.591296928327645,\n \"acc_norm_stderr\": 0.014365750345427006\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6075482971519618,\n \"acc_stderr\": 0.004872984492967997,\n \"acc_norm\": 0.7956582354112727,\n \"acc_norm_stderr\": 0.004023957334461984\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6805555555555556,\n \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.6805555555555556,\n \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006718,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006718\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.03260038511835771,\n \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.03260038511835771\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.30701754385964913,\n \"acc_stderr\": 0.0433913832257986,\n \"acc_norm\": 0.30701754385964913,\n \"acc_norm_stderr\": 0.0433913832257986\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.02479606060269995,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.02479606060269995\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7225806451612903,\n \"acc_stderr\": 0.025470196835900055,\n \"acc_norm\": 0.7225806451612903,\n \"acc_norm_stderr\": 0.025470196835900055\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.035145285621750094,\n \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.035145285621750094\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.03477691162163659,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03477691162163659\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7424242424242424,\n \"acc_stderr\": 0.03115626951964683,\n \"acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.03115626951964683\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.02503387058301518,\n \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.02503387058301518\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.02506909438729654,\n \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.02506909438729654\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.03128217706368461,\n \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.03128217706368461\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936073,\n \"acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936073\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 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"latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-22-23.375628.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_22_23.375628", "path": ["results_2023-12-09T20-22-23.375628.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T20-22-23.375628.parquet"]}]}]}	2023-12-09T20:26:02+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Locutusque/Orca-2-13B-no_robots ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Locutusque/Orca-2-13B-no_robots on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:22:23.375628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Locutusque/Orca-2-13B-no_robots", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Locutusque/Orca-2-13B-no_robots on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:22:23.375628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Locutusque/Orca-2-13B-no_robots", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Locutusque/Orca-2-13B-no_robots on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:22:23.375628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Locutusque/Orca-2-13B-no_robots## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Locutusque/Orca-2-13B-no_robots on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:22:23.375628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
43c9a98b357e57b323fba8f482033331749b6acd	# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.4-preview3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.4-preview3](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.4-preview3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:24:42.121892](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview3/blob/main/results_2023-12-09T20-24-42.121892.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5277560649597496, "acc_stderr": 0.03425240844552933, "acc_norm": 0.532688176887894, "acc_norm_stderr": 0.034990875171934714, "mc1": 0.35862913096695226, "mc1_stderr": 0.016789289499502025, "mc2": 0.5235126569364149, "mc2_stderr": 0.015157264857162787 }, "harness\|arc:challenge\|25": { "acc": 0.49146757679180886, "acc_stderr": 0.014609263165632182, "acc_norm": 0.5127986348122867, "acc_norm_stderr": 0.014606603181012541 }, "harness\|hellaswag\|10": { "acc": 0.5513841864170484, "acc_stderr": 0.00496336208527556, "acc_norm": 0.7482573192591118, "acc_norm_stderr": 0.004331271717773856 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.43703703703703706, "acc_stderr": 0.04284958639753399, "acc_norm": 0.43703703703703706, "acc_norm_stderr": 0.04284958639753399 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.506578947368421, "acc_stderr": 0.040685900502249704, "acc_norm": 0.506578947368421, "acc_norm_stderr": 0.040685900502249704 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319875, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319875 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5433526011560693, "acc_stderr": 0.03798106566014498, "acc_norm": 0.5433526011560693, "acc_norm_stderr": 0.03798106566014498 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.28431372549019607, "acc_stderr": 0.04488482852329017, "acc_norm": 0.28431372549019607, "acc_norm_stderr": 0.04488482852329017 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4340425531914894, "acc_stderr": 0.032400380867927465, "acc_norm": 0.4340425531914894, "acc_norm_stderr": 0.032400380867927465 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.32456140350877194, "acc_stderr": 0.04404556157374767, "acc_norm": 0.32456140350877194, "acc_norm_stderr": 0.04404556157374767 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.45517241379310347, "acc_stderr": 0.04149886942192117, "acc_norm": 0.45517241379310347, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.043435254289490965, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.043435254289490965 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6548387096774193, "acc_stderr": 0.027045746573534327, "acc_norm": 0.6548387096774193, "acc_norm_stderr": 0.027045746573534327 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3842364532019704, "acc_stderr": 0.03422398565657551, "acc_norm": 0.3842364532019704, "acc_norm_stderr": 0.03422398565657551 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6303030303030303, "acc_stderr": 0.037694303145125674, "acc_norm": 0.6303030303030303, "acc_norm_stderr": 0.037694303145125674 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7070707070707071, "acc_stderr": 0.03242497958178815, "acc_norm": 0.7070707070707071, "acc_norm_stderr": 0.03242497958178815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7150259067357513, "acc_stderr": 0.03257714077709662, "acc_norm": 0.7150259067357513, "acc_norm_stderr": 0.03257714077709662 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5153846153846153, "acc_stderr": 0.025339003010106515, "acc_norm": 0.5153846153846153, "acc_norm_stderr": 0.025339003010106515 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.02857834836547307, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547307 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5630252100840336, "acc_stderr": 0.03221943636566196, "acc_norm": 0.5630252100840336, "acc_norm_stderr": 0.03221943636566196 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7192660550458716, "acc_stderr": 0.019266055045871616, "acc_norm": 0.7192660550458716, "acc_norm_stderr": 0.019266055045871616 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4861111111111111, "acc_stderr": 0.03408655867977749, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.03408655867977749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6519607843137255, "acc_stderr": 0.03343311240488418, "acc_norm": 0.6519607843137255, "acc_norm_stderr": 0.03343311240488418 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03068582059661079, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03068582059661079 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5829596412556054, "acc_stderr": 0.03309266936071721, "acc_norm": 0.5829596412556054, "acc_norm_stderr": 0.03309266936071721 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6335877862595419, "acc_stderr": 0.04225875451969638, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.04225875451969638 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6446280991735537, "acc_stderr": 0.0436923632657398, "acc_norm": 0.6446280991735537, "acc_norm_stderr": 0.0436923632657398 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6388888888888888, "acc_stderr": 0.04643454608906276, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.04643454608906276 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.588957055214724, "acc_stderr": 0.038656978537853624, "acc_norm": 0.588957055214724, "acc_norm_stderr": 0.038656978537853624 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.36607142857142855, "acc_stderr": 0.045723723587374296, "acc_norm": 0.36607142857142855, "acc_norm_stderr": 0.045723723587374296 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7184466019417476, "acc_stderr": 0.044532548363264673, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.044532548363264673 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8376068376068376, "acc_stderr": 0.02416161812798774, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.02416161812798774 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7254150702426565, "acc_stderr": 0.015959829933084032, "acc_norm": 0.7254150702426565, "acc_norm_stderr": 0.015959829933084032 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.569364161849711, "acc_stderr": 0.026658800273672376, "acc_norm": 0.569364161849711, "acc_norm_stderr": 0.026658800273672376 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3229050279329609, "acc_stderr": 0.01563844038024149, "acc_norm": 0.3229050279329609, "acc_norm_stderr": 0.01563844038024149 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6045751633986928, "acc_stderr": 0.02799672318063145, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.02799672318063145 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6012861736334405, "acc_stderr": 0.027809322585774503, "acc_norm": 0.6012861736334405, "acc_norm_stderr": 0.027809322585774503 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5154320987654321, "acc_stderr": 0.02780749004427619, "acc_norm": 0.5154320987654321, "acc_norm_stderr": 0.02780749004427619 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.34397163120567376, "acc_stderr": 0.028338017428611324, "acc_norm": 0.34397163120567376, "acc_norm_stderr": 0.028338017428611324 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3650586701434159, "acc_stderr": 0.012296373743443478, "acc_norm": 0.3650586701434159, "acc_norm_stderr": 0.012296373743443478 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5661764705882353, "acc_stderr": 0.03010563657001663, "acc_norm": 0.5661764705882353, "acc_norm_stderr": 0.03010563657001663 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.020196594933541194, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.020196594933541194 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661895, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661895 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5918367346938775, "acc_stderr": 0.03146465712827423, "acc_norm": 0.5918367346938775, "acc_norm_stderr": 0.03146465712827423 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6915422885572139, "acc_stderr": 0.032658195885126966, "acc_norm": 0.6915422885572139, "acc_norm_stderr": 0.032658195885126966 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6783625730994152, "acc_stderr": 0.03582529442573122, "acc_norm": 0.6783625730994152, "acc_norm_stderr": 0.03582529442573122 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35862913096695226, "mc1_stderr": 0.016789289499502025, "mc2": 0.5235126569364149, "mc2_stderr": 0.015157264857162787 }, "harness\|winogrande\|5": { "acc": 0.7348066298342542, "acc_stderr": 0.01240654946619286 }, "harness\|gsm8k\|5": { "acc": 0.24791508718726307, "acc_stderr": 0.011893980214826171 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview3	[ "region:us" ]	2023-12-09T20:27:33+00:00	{"pretty_name": "Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview3", "dataset_summary": "Dataset automatically created during the evaluation run of model [WebraftAI/synapsellm-7b-mistral-v0.4-preview3](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.4-preview3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:24:42.121892](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.4-preview3/blob/main/results_2023-12-09T20-24-42.121892.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5277560649597496,\n \"acc_stderr\": 0.03425240844552933,\n \"acc_norm\": 0.532688176887894,\n \"acc_norm_stderr\": 0.034990875171934714,\n \"mc1\": 0.35862913096695226,\n \"mc1_stderr\": 0.016789289499502025,\n \"mc2\": 0.5235126569364149,\n \"mc2_stderr\": 0.015157264857162787\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.49146757679180886,\n \"acc_stderr\": 0.014609263165632182,\n \"acc_norm\": 0.5127986348122867,\n \"acc_norm_stderr\": 0.014606603181012541\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5513841864170484,\n \"acc_stderr\": 0.00496336208527556,\n \"acc_norm\": 0.7482573192591118,\n \"acc_norm_stderr\": 0.004331271717773856\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.04284958639753399,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 0.04284958639753399\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.506578947368421,\n \"acc_stderr\": 0.040685900502249704,\n \"acc_norm\": 0.506578947368421,\n \"acc_norm_stderr\": 0.040685900502249704\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5433526011560693,\n \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.5433526011560693,\n \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4340425531914894,\n \"acc_stderr\": 0.032400380867927465,\n \"acc_norm\": 0.4340425531914894,\n \"acc_norm_stderr\": 0.032400380867927465\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.32456140350877194,\n \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6548387096774193,\n \"acc_stderr\": 0.027045746573534327,\n \"acc_norm\": 0.6548387096774193,\n \"acc_norm_stderr\": 0.027045746573534327\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.3842364532019704,\n \"acc_stderr\": 0.03422398565657551,\n \"acc_norm\": 0.3842364532019704,\n \"acc_norm_stderr\": 0.03422398565657551\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6303030303030303,\n \"acc_stderr\": 0.037694303145125674,\n \"acc_norm\": 0.6303030303030303,\n \"acc_norm_stderr\": 0.037694303145125674\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7070707070707071,\n \"acc_stderr\": 0.03242497958178815,\n \"acc_norm\": 0.7070707070707071,\n \"acc_norm_stderr\": 0.03242497958178815\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7150259067357513,\n \"acc_stderr\": 0.03257714077709662,\n \"acc_norm\": 0.7150259067357513,\n \"acc_norm_stderr\": 0.03257714077709662\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5153846153846153,\n \"acc_stderr\": 0.025339003010106515,\n \"acc_norm\": 0.5153846153846153,\n \"acc_norm_stderr\": 0.025339003010106515\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547307,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547307\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5630252100840336,\n \"acc_stderr\": 0.03221943636566196,\n \"acc_norm\": 0.5630252100840336,\n \"acc_norm_stderr\": 0.03221943636566196\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.7192660550458716,\n \"acc_stderr\": 0.019266055045871616,\n \"acc_norm\": 0.7192660550458716,\n \"acc_norm_stderr\": 0.019266055045871616\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977749,\n \"acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977749\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.6519607843137255,\n \"acc_stderr\": 0.03343311240488418,\n \"acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.03343311240488418\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03068582059661079,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03068582059661079\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5829596412556054,\n \"acc_stderr\": 0.03309266936071721,\n \"acc_norm\": 0.5829596412556054,\n \"acc_norm_stderr\": 0.03309266936071721\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969638,\n \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969638\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.6446280991735537,\n \"acc_stderr\": 0.0436923632657398,\n \"acc_norm\": 0.6446280991735537,\n \"acc_norm_stderr\": 0.0436923632657398\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.04643454608906276,\n \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.04643454608906276\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.588957055214724,\n \"acc_stderr\": 0.038656978537853624,\n \"acc_norm\": 0.588957055214724,\n \"acc_norm_stderr\": 0.038656978537853624\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.36607142857142855,\n \"acc_stderr\": 0.045723723587374296,\n \"acc_norm\": 0.36607142857142855,\n \"acc_norm_stderr\": 0.045723723587374296\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.044532548363264673,\n \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.044532548363264673\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8376068376068376,\n \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7254150702426565,\n \"acc_stderr\": 0.015959829933084032,\n \"acc_norm\": 0.7254150702426565,\n \"acc_norm_stderr\": 0.015959829933084032\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.569364161849711,\n \"acc_stderr\": 0.026658800273672376,\n \"acc_norm\": 0.569364161849711,\n \"acc_norm_stderr\": 0.026658800273672376\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3229050279329609,\n \"acc_stderr\": 0.01563844038024149,\n \"acc_norm\": 0.3229050279329609,\n \"acc_norm_stderr\": 0.01563844038024149\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.02799672318063145,\n \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.02799672318063145\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6012861736334405,\n \"acc_stderr\": 0.027809322585774503,\n \"acc_norm\": 0.6012861736334405,\n \"acc_norm_stderr\": 0.027809322585774503\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5154320987654321,\n \"acc_stderr\": 0.02780749004427619,\n \"acc_norm\": 0.5154320987654321,\n \"acc_norm_stderr\": 0.02780749004427619\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611324,\n \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611324\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3650586701434159,\n \"acc_stderr\": 0.012296373743443478,\n \"acc_norm\": 0.3650586701434159,\n \"acc_norm_stderr\": 0.012296373743443478\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.5661764705882353,\n \"acc_stderr\": 0.03010563657001663,\n \"acc_norm\": 0.5661764705882353,\n \"acc_norm_stderr\": 0.03010563657001663\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.020196594933541194,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.020196594933541194\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5909090909090909,\n \"acc_stderr\": 0.04709306978661895,\n \"acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.04709306978661895\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.5918367346938775,\n \"acc_stderr\": 0.03146465712827423,\n \"acc_norm\": 0.5918367346938775,\n \"acc_norm_stderr\": 0.03146465712827423\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6915422885572139,\n \"acc_stderr\": 0.032658195885126966,\n \"acc_norm\": 0.6915422885572139,\n \"acc_norm_stderr\": 0.032658195885126966\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-24-42.121892.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-24-42.121892.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_24_42.121892", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-24-42.121892.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-24-42.121892.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_24_42.121892", "path": ["results_2023-12-09T20-24-42.121892.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T20-24-42.121892.parquet"]}]}]}	2023-12-09T20:28:19+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview3 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview3 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:24:42.121892(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:24:42.121892(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:24:42.121892(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.4-preview3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model WebraftAI/synapsellm-7b-mistral-v0.4-preview3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:24:42.121892(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
35001355c7883c5e9d6a1c12669c090621a06327	# Dataset Card for "rapidapi-example-responses-tokenized-bart" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses-tokenized-bart	[ "region:us" ]	2023-12-09T20:28:57+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167674923.4914025, "num_examples": 45170}, {"name": "test", "num_bytes": 18630959.5085975, "num_examples": 5019}], "download_size": 65550667, "dataset_size": 186305883.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}]}	2023-12-10T00:01:14+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses-tokenized-bart" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses-tokenized-bart\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses-tokenized-bart\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses-tokenized-bart\"\n\nMore Information needed" ]
3667e6867cd69095884caa6277e0833c052ea6d6	# Dataset Card for Evaluation run of Intel/neural-chat-7b-v3-3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/Intel/neural-chat-7b-v3-3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Intel__neural-chat-7b-v3-3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:33:34.862293](https://huggingface.co/datasets/open-llm-leaderboard/details_Intel__neural-chat-7b-v3-3/blob/main/results_2023-12-09T20-33-34.862293.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.633718840288445, "acc_stderr": 0.03262856399270551, "acc_norm": 0.6351165946232198, "acc_norm_stderr": 0.03329008839330021, "mc1": 0.4700122399020808, "mc1_stderr": 0.017471992091697534, "mc2": 0.6301479198844473, "mc2_stderr": 0.015176409746133967 }, "harness\|arc:challenge\|25": { "acc": 0.6373720136518771, "acc_stderr": 0.014049106564955007, "acc_norm": 0.6689419795221843, "acc_norm_stderr": 0.013752062419817837 }, "harness\|hellaswag\|10": { "acc": 0.6617207727544314, "acc_stderr": 0.004721571443354415, "acc_norm": 0.8526190001991635, "acc_norm_stderr": 0.0035376085010691773 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.0420392104015628, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.0420392104015628 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6578947368421053, "acc_stderr": 0.03860731599316092, "acc_norm": 0.6578947368421053, "acc_norm_stderr": 0.03860731599316092 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6679245283018868, "acc_stderr": 0.02898545565233439, "acc_norm": 0.6679245283018868, "acc_norm_stderr": 0.02898545565233439 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958546, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958546 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.032500536843658404, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.032500536843658404 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594963, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594963 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3835978835978836, "acc_stderr": 0.025043757318520193, "acc_norm": 0.3835978835978836, "acc_norm_stderr": 0.025043757318520193 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7483870967741936, "acc_stderr": 0.02468597928623996, "acc_norm": 0.7483870967741936, "acc_norm_stderr": 0.02468597928623996 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.458128078817734, "acc_stderr": 0.03505630140785741, "acc_norm": 0.458128078817734, "acc_norm_stderr": 0.03505630140785741 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.033175059300091826, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.033175059300091826 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.02912652283458682, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.02912652283458682 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8704663212435233, "acc_stderr": 0.02423353229775873, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.02423353229775873 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6333333333333333, "acc_stderr": 0.02443301646605246, "acc_norm": 0.6333333333333333, "acc_norm_stderr": 0.02443301646605246 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251972, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251972 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8348623853211009, "acc_stderr": 0.015919557829976044, "acc_norm": 0.8348623853211009, "acc_norm_stderr": 0.015919557829976044 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.04330043749650742, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650742 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615624, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615624 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.039166677628225836, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.039166677628225836 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8199233716475096, "acc_stderr": 0.013740797258579825, "acc_norm": 0.8199233716475096, "acc_norm_stderr": 0.013740797258579825 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7109826589595376, "acc_stderr": 0.02440517393578323, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.02440517393578323 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4, "acc_stderr": 0.016384638410380823, "acc_norm": 0.4, "acc_norm_stderr": 0.016384638410380823 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460845, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460845 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.45390070921985815, "acc_stderr": 0.02970045324729146, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.02970045324729146 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.43546284224250326, "acc_stderr": 0.01266341210124834, "acc_norm": 0.43546284224250326, "acc_norm_stderr": 0.01266341210124834 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.028582709753898445, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.028582709753898445 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6584967320261438, "acc_stderr": 0.019184639328092487, "acc_norm": 0.6584967320261438, "acc_norm_stderr": 0.019184639328092487 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4700122399020808, "mc1_stderr": 0.017471992091697534, "mc2": 0.6301479198844473, "mc2_stderr": 0.015176409746133967 }, "harness\|winogrande\|5": { "acc": 0.7963693764798737, "acc_stderr": 0.011317798781626913 }, "harness\|gsm8k\|5": { "acc": 0.6110689916603488, "acc_stderr": 0.013428382481274231 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Intel__neural-chat-7b-v3-3	[ "region:us" ]	2023-12-09T20:36:24+00:00	{"pretty_name": "Evaluation run of Intel/neural-chat-7b-v3-3", "dataset_summary": "Dataset automatically created during the evaluation run of model [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Intel__neural-chat-7b-v3-3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:33:34.862293](https://huggingface.co/datasets/open-llm-leaderboard/details_Intel__neural-chat-7b-v3-3/blob/main/results_2023-12-09T20-33-34.862293.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.633718840288445,\n \"acc_stderr\": 0.03262856399270551,\n \"acc_norm\": 0.6351165946232198,\n \"acc_norm_stderr\": 0.03329008839330021,\n \"mc1\": 0.4700122399020808,\n \"mc1_stderr\": 0.017471992091697534,\n \"mc2\": 0.6301479198844473,\n \"mc2_stderr\": 0.015176409746133967\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6373720136518771,\n \"acc_stderr\": 0.014049106564955007,\n \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.013752062419817837\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6617207727544314,\n \"acc_stderr\": 0.004721571443354415,\n \"acc_norm\": 0.8526190001991635,\n \"acc_norm_stderr\": 0.0035376085010691773\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.0420392104015628,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.0420392104015628\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.02898545565233439,\n \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.02898545565233439\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7291666666666666,\n \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.03643037168958546,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.03643037168958546\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.032500536843658404,\n \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.032500536843658404\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4298245614035088,\n \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520193,\n \"acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520193\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7483870967741936,\n \"acc_stderr\": 0.02468597928623996,\n \"acc_norm\": 0.7483870967741936,\n \"acc_norm_stderr\": 0.02468597928623996\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.033175059300091826,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.033175059300091826\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.02912652283458682,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.02912652283458682\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.02423353229775873,\n \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.02423353229775873\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8348623853211009,\n \"acc_stderr\": 0.015919557829976044,\n \"acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.015919557829976044\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.04330043749650742,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.04330043749650742\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.039166677628225836,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.039166677628225836\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8199233716475096,\n \"acc_stderr\": 0.013740797258579825,\n \"acc_norm\": 0.8199233716475096,\n \"acc_norm_stderr\": 0.013740797258579825\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.016384638410380823,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.016384638410380823\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6784565916398714,\n \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.45390070921985815,\n \"acc_stderr\": 0.02970045324729146,\n \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.02970045324729146\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.43546284224250326,\n \"acc_stderr\": 0.01266341210124834,\n \"acc_norm\": 0.43546284224250326,\n \"acc_norm_stderr\": 0.01266341210124834\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898445,\n \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898445\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6454545454545455,\n \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8109452736318408,\n \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4700122399020808,\n \"mc1_stderr\": 0.017471992091697534,\n \"mc2\": 0.6301479198844473,\n \"mc2_stderr\": 0.015176409746133967\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626913\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6110689916603488,\n \"acc_stderr\": 0.013428382481274231\n }\n}\n```", "repo_url": "https://huggingface.co/Intel/neural-chat-7b-v3-3", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|gsm8k\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hellaswag\|10_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-33-34.862293.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-33-34.862293.parquet", 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"path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-33-34.862293.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_33_34.862293", "path": ["results_2023-12-09T20-33-34.862293.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T20-33-34.862293.parquet"]}]}]}	2023-12-09T20:37:07+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Intel/neural-chat-7b-v3-3 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Intel/neural-chat-7b-v3-3 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:33:34.862293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Intel/neural-chat-7b-v3-3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Intel/neural-chat-7b-v3-3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:33:34.862293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Intel/neural-chat-7b-v3-3", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Intel/neural-chat-7b-v3-3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:33:34.862293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 20, 31, 169, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Intel/neural-chat-7b-v3-3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Intel/neural-chat-7b-v3-3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:33:34.862293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
fdf62b3bc22e4a052afe26888152dff144acfa42	# Dataset Card for Evaluation run of CausalLM/72B-preview ## Dataset Description - Homepage: - Repository: https://huggingface.co/CausalLM/72B-preview - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [CausalLM/72B-preview](https://huggingface.co/CausalLM/72B-preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CausalLM__72B-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T21:42:26.382618](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__72B-preview/blob/main/results_2023-12-09T21-42-26.382618.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7667362936260237, "acc_stderr": 0.027929321227362417, "acc_norm": 0.7704368351697709, "acc_norm_stderr": 0.028461947646281283, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5257567284522894, "mc2_stderr": 0.014743557767765337 }, "harness\|arc:challenge\|25": { "acc": 0.606655290102389, "acc_stderr": 0.014275101465693024, "acc_norm": 0.6518771331058021, "acc_norm_stderr": 0.013921008595179347 }, "harness\|hellaswag\|10": { "acc": 0.6468830910177256, "acc_stderr": 0.004769618829196502, "acc_norm": 0.8323043218482374, "acc_norm_stderr": 0.0037283229688748914 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.9144736842105263, "acc_stderr": 0.02275867713088861, "acc_norm": 0.9144736842105263, "acc_norm_stderr": 0.02275867713088861 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8301886792452831, "acc_stderr": 0.023108393799841326, "acc_norm": 0.8301886792452831, "acc_norm_stderr": 0.023108393799841326 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7803468208092486, "acc_stderr": 0.031568093627031744, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.031568093627031744 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5392156862745098, "acc_stderr": 0.04959859966384181, "acc_norm": 0.5392156862745098, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.8, "acc_stderr": 0.026148818018424502, "acc_norm": 0.8, "acc_norm_stderr": 0.026148818018424502 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594963, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594963 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6798941798941799, "acc_stderr": 0.024026846392873506, "acc_norm": 0.6798941798941799, "acc_norm_stderr": 0.024026846392873506 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8903225806451613, "acc_stderr": 0.017776778700485173, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.017776778700485173 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6600985221674877, "acc_stderr": 0.033327690684107895, "acc_norm": 0.6600985221674877, "acc_norm_stderr": 0.033327690684107895 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.0270459488258654, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.0270459488258654 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9444444444444444, "acc_stderr": 0.0163199507007674, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.0163199507007674 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9896373056994818, "acc_stderr": 0.007308424386792194, "acc_norm": 0.9896373056994818, "acc_norm_stderr": 0.007308424386792194 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8076923076923077, "acc_stderr": 0.019982347208637282, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.019982347208637282 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.5296296296296297, "acc_stderr": 0.030431963547936584, "acc_norm": 0.5296296296296297, "acc_norm_stderr": 0.030431963547936584 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8319327731092437, "acc_stderr": 0.024289102115692275, "acc_norm": 0.8319327731092437, "acc_norm_stderr": 0.024289102115692275 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.543046357615894, "acc_stderr": 0.040673251742474416, "acc_norm": 0.543046357615894, "acc_norm_stderr": 0.040673251742474416 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815481, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815481 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6759259259259259, "acc_stderr": 0.03191923445686186, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.03191923445686186 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.01886951464665892, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.01886951464665892 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8945147679324894, "acc_stderr": 0.019995560723758535, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.019995560723758535 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8116591928251121, "acc_stderr": 0.026241132996407252, "acc_norm": 0.8116591928251121, "acc_norm_stderr": 0.026241132996407252 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8778625954198473, "acc_stderr": 0.02871877688934232, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.02871877688934232 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8677685950413223, "acc_stderr": 0.0309227883204458, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.0309227883204458 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243630999, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243630999 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6785714285714286, "acc_stderr": 0.04432804055291518, "acc_norm": 0.6785714285714286, "acc_norm_stderr": 0.04432804055291518 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8640776699029126, "acc_stderr": 0.03393295729761011, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.03393295729761011 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253878, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253878 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.88, "acc_stderr": 0.032659863237109066, "acc_norm": 0.88, "acc_norm_stderr": 0.032659863237109066 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9195402298850575, "acc_stderr": 0.009726831316141866, "acc_norm": 0.9195402298850575, "acc_norm_stderr": 0.009726831316141866 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.846820809248555, "acc_stderr": 0.019390370108969934, "acc_norm": 0.846820809248555, "acc_norm_stderr": 0.019390370108969934 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.5642458100558659, "acc_stderr": 0.016583881958602397, "acc_norm": 0.5642458100558659, "acc_norm_stderr": 0.016583881958602397 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043714, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043714 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8456591639871383, "acc_stderr": 0.02051905034208471, "acc_norm": 0.8456591639871383, "acc_norm_stderr": 0.02051905034208471 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8827160493827161, "acc_stderr": 0.017903112615281123, "acc_norm": 0.8827160493827161, "acc_norm_stderr": 0.017903112615281123 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6276595744680851, "acc_stderr": 0.028838921471251455, "acc_norm": 0.6276595744680851, "acc_norm_stderr": 0.028838921471251455 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6258148631029987, "acc_stderr": 0.012359335618172063, "acc_norm": 0.6258148631029987, "acc_norm_stderr": 0.012359335618172063 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8272058823529411, "acc_stderr": 0.02296606758558181, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.02296606758558181 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8202614379084967, "acc_stderr": 0.01553374508338279, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.01553374508338279 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7545454545454545, "acc_stderr": 0.04122066502878285, "acc_norm": 0.7545454545454545, "acc_norm_stderr": 0.04122066502878285 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7959183673469388, "acc_stderr": 0.0258012834750905, "acc_norm": 0.7959183673469388, "acc_norm_stderr": 0.0258012834750905 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824667, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824667 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.96, "acc_stderr": 0.01969463855669321, "acc_norm": 0.96, "acc_norm_stderr": 0.01969463855669321 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8830409356725146, "acc_stderr": 0.02464806896136616, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.02464806896136616 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5257567284522894, "mc2_stderr": 0.014743557767765337 }, "harness\|winogrande\|5": { "acc": 0.824782951854775, "acc_stderr": 0.010684179227706167 }, "harness\|gsm8k\|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970311 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_CausalLM__72B-preview	[ "region:us" ]	2023-12-09T20:40:27+00:00	{"pretty_name": "Evaluation run of CausalLM/72B-preview", "dataset_summary": "Dataset automatically created during the evaluation run of model [CausalLM/72B-preview](https://huggingface.co/CausalLM/72B-preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CausalLM__72B-preview\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T21:42:26.382618](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__72B-preview/blob/main/results_2023-12-09T21-42-26.382618.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7667362936260237,\n \"acc_stderr\": 0.027929321227362417,\n \"acc_norm\": 0.7704368351697709,\n \"acc_norm_stderr\": 0.028461947646281283,\n \"mc1\": 0.3671970624235006,\n \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5257567284522894,\n \"mc2_stderr\": 0.014743557767765337\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.606655290102389,\n \"acc_stderr\": 0.014275101465693024,\n \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.013921008595179347\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6468830910177256,\n \"acc_stderr\": 0.004769618829196502,\n \"acc_norm\": 0.8323043218482374,\n \"acc_norm_stderr\": 0.0037283229688748914\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.9144736842105263,\n \"acc_stderr\": 0.02275867713088861,\n \"acc_norm\": 0.9144736842105263,\n \"acc_norm_stderr\": 0.02275867713088861\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8301886792452831,\n \"acc_stderr\": 0.023108393799841326,\n \"acc_norm\": 0.8301886792452831,\n \"acc_norm_stderr\": 0.023108393799841326\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8958333333333334,\n \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7803468208092486,\n \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.7803468208092486,\n \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.04959859966384181,\n \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.04959859966384181\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.026148818018424502,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.026148818018424502\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5701754385964912,\n \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.5701754385964912,\n \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6798941798941799,\n \"acc_stderr\": 0.024026846392873506,\n \"acc_norm\": 0.6798941798941799,\n \"acc_norm_stderr\": 0.024026846392873506\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8903225806451613,\n \"acc_stderr\": 0.017776778700485173,\n \"acc_norm\": 0.8903225806451613,\n \"acc_norm_stderr\": 0.017776778700485173\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6600985221674877,\n \"acc_stderr\": 0.033327690684107895,\n \"acc_norm\": 0.6600985221674877,\n \"acc_norm_stderr\": 0.033327690684107895\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.0270459488258654,\n \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.0270459488258654\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9444444444444444,\n \"acc_stderr\": 0.0163199507007674,\n \"acc_norm\": 0.9444444444444444,\n \"acc_norm_stderr\": 0.0163199507007674\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9896373056994818,\n \"acc_stderr\": 0.007308424386792194,\n \"acc_norm\": 0.9896373056994818,\n \"acc_norm_stderr\": 0.007308424386792194\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.8076923076923077,\n \"acc_stderr\": 0.019982347208637282,\n \"acc_norm\": 0.8076923076923077,\n \"acc_norm_stderr\": 0.019982347208637282\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.5296296296296297,\n \"acc_stderr\": 0.030431963547936584,\n \"acc_norm\": 0.5296296296296297,\n \"acc_norm_stderr\": 0.030431963547936584\n },\n 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},\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.02464806896136616,\n \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.02464806896136616\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3671970624235006,\n \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5257567284522894,\n \"mc2_stderr\": 0.014743557767765337\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706167\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7210007581501138,\n \"acc_stderr\": 0.012354115779970311\n }\n}\n```", "repo_url": "https://huggingface.co/CausalLM/72B-preview", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": 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["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T21-42-26.382618.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T20_37_44.242475", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-37-44.242475.parquet"]}, {"split": "2023_12_09T21_42_26.382618", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T21-42-26.382618.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T20_37_44.242475", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-37-44.242475.parquet"]}, {"split": "2023_12_09T21_42_26.382618", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T21-42-26.382618.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T20_37_44.242475", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-37-44.242475.parquet"]}, {"split": "2023_12_09T21_42_26.382618", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T21-42-26.382618.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T20_37_44.242475", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-37-44.242475.parquet"]}, {"split": "2023_12_09T21_42_26.382618", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T21-42-26.382618.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_37_44.242475", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-37-44.242475.parquet"]}, {"split": "2023_12_09T21_42_26.382618", "path": ["/details_harness\|winogrande\|5_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2023-12-09T21-42-26.382618.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_37_44.242475", "path": ["results_2023-12-09T20-37-44.242475.parquet"]}, {"split": "2023_12_09T21_42_26.382618", "path": ["results_2023-12-09T21-42-26.382618.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T21-42-26.382618.parquet"]}]}]}	2023-12-09T21:45:51+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of CausalLM/72B-preview ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model CausalLM/72B-preview on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T21:42:26.382618(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of CausalLM/72B-preview", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model CausalLM/72B-preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T21:42:26.382618(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of CausalLM/72B-preview", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model CausalLM/72B-preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T21:42:26.382618(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 18, 31, 167, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of CausalLM/72B-preview## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model CausalLM/72B-preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T21:42:26.382618(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
9fecc6de4c1592b470c542489fcc561280d2f462	# Dataset Card for Evaluation run of perlthoughts/Falkor-16b ## Dataset Description - Homepage: - Repository: https://huggingface.co/perlthoughts/Falkor-16b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [perlthoughts/Falkor-16b](https://huggingface.co/perlthoughts/Falkor-16b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_perlthoughts__Falkor-16b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:44:01.806324](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Falkor-16b/blob/main/results_2023-12-09T20-44-01.806324.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6322464801756708, "acc_stderr": 0.032618125802324496, "acc_norm": 0.6394191887381151, "acc_norm_stderr": 0.03329436215245147, "mc1": 0.4847001223990208, "mc1_stderr": 0.017495304473187902, "mc2": 0.627668658731456, "mc2_stderr": 0.015393187257856768 }, "harness\|arc:challenge\|25": { "acc": 0.6322525597269625, "acc_stderr": 0.014090995618168482, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892973 }, "harness\|hellaswag\|10": { "acc": 0.6330412268472416, "acc_stderr": 0.0048099011512348355, "acc_norm": 0.826229834694284, "acc_norm_stderr": 0.00378137335887 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6644736842105263, "acc_stderr": 0.038424985593952694, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.038424985593952694 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6, "acc_stderr": 0.03202563076101735, "acc_norm": 0.6, "acc_norm_stderr": 0.03202563076101735 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.025305906241590632, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.025305906241590632 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.02315787934908353, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.02315787934908353 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009181, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009181 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386417, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386417 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812143, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812143 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.02385479568097112, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.02385479568097112 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7058823529411765, "acc_stderr": 0.029597329730978082, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.029597329730978082 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.37748344370860926, "acc_stderr": 0.0395802723112157, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.0395802723112157 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009244, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009244 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.033622774366080424, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.033622774366080424 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6681614349775785, "acc_stderr": 0.03160295143776679, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.03160295143776679 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406974, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406974 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8045977011494253, "acc_stderr": 0.014179171373424384, "acc_norm": 0.8045977011494253, "acc_norm_stderr": 0.014179171373424384 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6734104046242775, "acc_stderr": 0.02524826477424284, "acc_norm": 0.6734104046242775, "acc_norm_stderr": 0.02524826477424284 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.40782122905027934, "acc_stderr": 0.016435865260914742, "acc_norm": 0.40782122905027934, "acc_norm_stderr": 0.016435865260914742 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6945337620578779, "acc_stderr": 0.026160584450140453, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.026160584450140453 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.691358024691358, "acc_stderr": 0.02570264026060374, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.02570264026060374 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4485006518904824, "acc_stderr": 0.01270231749055981, "acc_norm": 0.4485006518904824, "acc_norm_stderr": 0.01270231749055981 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6405228758169934, "acc_stderr": 0.01941253924203216, "acc_norm": 0.6405228758169934, "acc_norm_stderr": 0.01941253924203216 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065674, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065674 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536934, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4847001223990208, "mc1_stderr": 0.017495304473187902, "mc2": 0.627668658731456, "mc2_stderr": 0.015393187257856768 }, "harness\|winogrande\|5": { "acc": 0.7790055248618785, "acc_stderr": 0.011661223637643417 }, "harness\|gsm8k\|5": { "acc": 0.28278999241849884, "acc_stderr": 0.012405020417873619 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_perlthoughts__Falkor-16b	[ "region:us" ]	2023-12-09T20:46:54+00:00	{"pretty_name": "Evaluation run of perlthoughts/Falkor-16b", "dataset_summary": "Dataset automatically created during the evaluation run of model [perlthoughts/Falkor-16b](https://huggingface.co/perlthoughts/Falkor-16b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_perlthoughts__Falkor-16b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T20:44:01.806324](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Falkor-16b/blob/main/results_2023-12-09T20-44-01.806324.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6322464801756708,\n \"acc_stderr\": 0.032618125802324496,\n \"acc_norm\": 0.6394191887381151,\n \"acc_norm_stderr\": 0.03329436215245147,\n \"mc1\": 0.4847001223990208,\n \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.627668658731456,\n \"mc2_stderr\": 0.015393187257856768\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6322525597269625,\n \"acc_stderr\": 0.014090995618168482,\n \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892973\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6330412268472416,\n \"acc_stderr\": 0.0048099011512348355,\n \"acc_norm\": 0.826229834694284,\n \"acc_norm_stderr\": 0.00378137335887\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03202563076101735,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03202563076101735\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.025305906241590632,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.025305906241590632\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7903225806451613,\n \"acc_stderr\": 0.02315787934908353,\n \"acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.02315787934908353\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812143,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812143\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097112,\n \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097112\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.029597329730978082,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.029597329730978082\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.03160295143776679,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.03160295143776679\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8045977011494253,\n \"acc_stderr\": 0.014179171373424384,\n \"acc_norm\": 0.8045977011494253,\n \"acc_norm_stderr\": 0.014179171373424384\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.02524826477424284,\n \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.02524826477424284\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.40782122905027934,\n \"acc_stderr\": 0.016435865260914742,\n \"acc_norm\": 0.40782122905027934,\n \"acc_norm_stderr\": 0.016435865260914742\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6945337620578779,\n \"acc_stderr\": 0.026160584450140453,\n \"acc_norm\": 0.6945337620578779,\n \"acc_norm_stderr\": 0.026160584450140453\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.02570264026060374,\n \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.02570264026060374\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4485006518904824,\n \"acc_stderr\": 0.01270231749055981,\n \"acc_norm\": 0.4485006518904824,\n \"acc_norm_stderr\": 0.01270231749055981\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6405228758169934,\n \"acc_stderr\": 0.01941253924203216,\n \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.01941253924203216\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065674,\n \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065674\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4847001223990208,\n \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.627668658731456,\n \"mc2_stderr\": 0.015393187257856768\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7790055248618785,\n \"acc_stderr\": 0.011661223637643417\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.28278999241849884,\n \"acc_stderr\": 0.012405020417873619\n }\n}\n```", "repo_url": "https://huggingface.co/perlthoughts/Falkor-16b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", 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["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-44-01.806324.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T20-44-01.806324.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T20_44_01.806324", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-44-01.806324.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T20-44-01.806324.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T20_44_01.806324", "path": ["results_2023-12-09T20-44-01.806324.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T20-44-01.806324.parquet"]}]}]}	2023-12-09T20:47:42+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of perlthoughts/Falkor-16b ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model perlthoughts/Falkor-16b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T20:44:01.806324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of perlthoughts/Falkor-16b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model perlthoughts/Falkor-16b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:44:01.806324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of perlthoughts/Falkor-16b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model perlthoughts/Falkor-16b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T20:44:01.806324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 19, 31, 168, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of perlthoughts/Falkor-16b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model perlthoughts/Falkor-16b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T20:44:01.806324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
4577fb5335874140cde1953076ad08b526f51dfb	139 Code Conversations generated from LeetCode Questions including official answers. Totals to 968 total messages from USER and SYSTEM. Includes: - Generation of the solutions - Conversions into other programming languages - Adjustments to the Code - Generating tests Conversations generated with GPT4/GPT4-Turbo	SebastianBodza/LeetCode_Conversations	[ "region:us" ]	2023-12-09T21:03:33+00:00	{}	2023-12-09T21:09:56+00:00	[]	[]	TAGS #region-us	139 Code Conversations generated from LeetCode Questions including official answers. Totals to 968 total messages from USER and SYSTEM. Includes: - Generation of the solutions - Conversions into other programming languages - Adjustments to the Code - Generating tests Conversations generated with GPT4/GPT4-Turbo	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
d4ca8eaee10a2568c1afd74eef0755c104f930ad	# Dataset Card for "mtdg-eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	nayohan/mtdg-eval	[ "region:us" ]	2023-12-09T21:32:47+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 60809, "num_examples": 100}], "download_size": 39291, "dataset_size": 60809}}	2023-12-09T22:27:03+00:00	[]	[]	TAGS #region-us	# Dataset Card for "mtdg-eval" More Information needed	[ "# Dataset Card for \"mtdg-eval\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"mtdg-eval\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"mtdg-eval\"\n\nMore Information needed" ]
f0b94c91184646d6c171d7180711aedf65039690	# Dataset Card for "msdg-eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	nayohan/msdg-eval	[ "region:us" ]	2023-12-09T21:32:50+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 406016, "num_examples": 100}], "download_size": 218562, "dataset_size": 406016}}	2023-12-09T22:27:08+00:00	[]	[]	TAGS #region-us	# Dataset Card for "msdg-eval" More Information needed	[ "# Dataset Card for \"msdg-eval\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"msdg-eval\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"msdg-eval\"\n\nMore Information needed" ]
2d7c3c2da4a465ee0d659e65a94f03ce4174f34f	# Dataset Card for Evaluation run of AA051610/AZG ## Dataset Description - Homepage: - Repository: https://huggingface.co/AA051610/AZG - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [AA051610/AZG](https://huggingface.co/AA051610/AZG) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AA051610__AZG", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T22:11:18.691101](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__AZG/blob/main/results_2023-12-09T22-11-18.691101.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6997224830510826, "acc_stderr": 0.03037702672483155, "acc_norm": 0.7036495225534056, "acc_norm_stderr": 0.030966589072480434, "mc1": 0.38310893512851896, "mc1_stderr": 0.017018461679389855, "mc2": 0.538427969031974, "mc2_stderr": 0.015499026242399048 }, "harness\|arc:challenge\|25": { "acc": 0.5981228668941979, "acc_stderr": 0.014327268614578274, "acc_norm": 0.628839590443686, "acc_norm_stderr": 0.014117971901142822 }, "harness\|hellaswag\|10": { "acc": 0.619398526190002, "acc_stderr": 0.0048454245247640405, "acc_norm": 0.8201553475403306, "acc_norm_stderr": 0.003832731017592104 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8289473684210527, "acc_stderr": 0.030643607071677088, "acc_norm": 0.8289473684210527, "acc_norm_stderr": 0.030643607071677088 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7358490566037735, "acc_stderr": 0.027134291628741706, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.027134291628741706 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7986111111111112, "acc_stderr": 0.033536474697138406, "acc_norm": 0.7986111111111112, "acc_norm_stderr": 0.033536474697138406 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7063829787234043, "acc_stderr": 0.029771642712491227, "acc_norm": 0.7063829787234043, "acc_norm_stderr": 0.029771642712491227 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.543859649122807, "acc_stderr": 0.046854730419077895, "acc_norm": 0.543859649122807, "acc_norm_stderr": 0.046854730419077895 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7103448275862069, "acc_stderr": 0.03780019230438015, "acc_norm": 0.7103448275862069, "acc_norm_stderr": 0.03780019230438015 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.5423280423280423, "acc_stderr": 0.02565886886205833, "acc_norm": 0.5423280423280423, "acc_norm_stderr": 0.02565886886205833 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8387096774193549, "acc_stderr": 0.020923327006423298, "acc_norm": 0.8387096774193549, "acc_norm_stderr": 0.020923327006423298 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5665024630541872, "acc_stderr": 0.03486731727419872, "acc_norm": 0.5665024630541872, "acc_norm_stderr": 0.03486731727419872 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8939393939393939, "acc_stderr": 0.021938047738853113, "acc_norm": 0.8939393939393939, "acc_norm_stderr": 0.021938047738853113 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9326424870466321, "acc_stderr": 0.018088393839078912, "acc_norm": 0.9326424870466321, "acc_norm_stderr": 0.018088393839078912 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7538461538461538, "acc_stderr": 0.021840866990423084, "acc_norm": 0.7538461538461538, "acc_norm_stderr": 0.021840866990423084 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.02904560029061626, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.02904560029061626 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7983193277310925, "acc_stderr": 0.026064313406304527, "acc_norm": 0.7983193277310925, "acc_norm_stderr": 0.026064313406304527 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4304635761589404, "acc_stderr": 0.04042809961395634, "acc_norm": 0.4304635761589404, "acc_norm_stderr": 0.04042809961395634 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8862385321100917, "acc_stderr": 0.013613614800232808, "acc_norm": 0.8862385321100917, "acc_norm_stderr": 0.013613614800232808 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8823529411764706, "acc_stderr": 0.022613286601132012, "acc_norm": 0.8823529411764706, "acc_norm_stderr": 0.022613286601132012 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8818565400843882, "acc_stderr": 0.021011052659878463, "acc_norm": 0.8818565400843882, "acc_norm_stderr": 0.021011052659878463 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.757847533632287, "acc_stderr": 0.028751392398694755, "acc_norm": 0.757847533632287, "acc_norm_stderr": 0.028751392398694755 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8625954198473282, "acc_stderr": 0.030194823996804475, "acc_norm": 0.8625954198473282, "acc_norm_stderr": 0.030194823996804475 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8264462809917356, "acc_stderr": 0.03457272836917669, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.03457272836917669 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.032472243899179465, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.032472243899179465 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8220858895705522, "acc_stderr": 0.03004735765580664, "acc_norm": 0.8220858895705522, "acc_norm_stderr": 0.03004735765580664 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5982142857142857, "acc_stderr": 0.04653333146973647, "acc_norm": 0.5982142857142857, "acc_norm_stderr": 0.04653333146973647 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.0376017800602662, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.0376017800602662 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9102564102564102, "acc_stderr": 0.018724301741941632, "acc_norm": 0.9102564102564102, "acc_norm_stderr": 0.018724301741941632 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.879948914431673, "acc_stderr": 0.011622736692041263, "acc_norm": 0.879948914431673, "acc_norm_stderr": 0.011622736692041263 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7658959537572254, "acc_stderr": 0.022797110278071124, "acc_norm": 0.7658959537572254, "acc_norm_stderr": 0.022797110278071124 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.41675977653631285, "acc_stderr": 0.016489134962438954, "acc_norm": 0.41675977653631285, "acc_norm_stderr": 0.016489134962438954 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7745098039215687, "acc_stderr": 0.023929155517351298, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.023929155517351298 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7717041800643086, "acc_stderr": 0.023839303311398188, "acc_norm": 0.7717041800643086, "acc_norm_stderr": 0.023839303311398188 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7839506172839507, "acc_stderr": 0.022899162918445796, "acc_norm": 0.7839506172839507, "acc_norm_stderr": 0.022899162918445796 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5390070921985816, "acc_stderr": 0.02973659252642444, "acc_norm": 0.5390070921985816, "acc_norm_stderr": 0.02973659252642444 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5286831812255541, "acc_stderr": 0.012749206007657459, "acc_norm": 0.5286831812255541, "acc_norm_stderr": 0.012749206007657459 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.027778298701545436, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.027778298701545436 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.75, "acc_stderr": 0.01751781884501444, "acc_norm": 0.75, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940589, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940589 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7591836734693878, "acc_stderr": 0.02737294220178816, "acc_norm": 0.7591836734693878, "acc_norm_stderr": 0.02737294220178816 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306053, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306053 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38310893512851896, "mc1_stderr": 0.017018461679389855, "mc2": 0.538427969031974, "mc2_stderr": 0.015499026242399048 }, "harness\|winogrande\|5": { "acc": 0.7995264404104183, "acc_stderr": 0.011251958281205069 }, "harness\|gsm8k\|5": { "acc": 0.599696739954511, "acc_stderr": 0.013495926436566441 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_AA051610__AZG	[ "region:us" ]	2023-12-09T22:14:06+00:00	{"pretty_name": "Evaluation run of AA051610/AZG", "dataset_summary": "Dataset automatically created during the evaluation run of model [AA051610/AZG](https://huggingface.co/AA051610/AZG) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AA051610__AZG\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T22:11:18.691101](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__AZG/blob/main/results_2023-12-09T22-11-18.691101.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6997224830510826,\n \"acc_stderr\": 0.03037702672483155,\n \"acc_norm\": 0.7036495225534056,\n \"acc_norm_stderr\": 0.030966589072480434,\n \"mc1\": 0.38310893512851896,\n \"mc1_stderr\": 0.017018461679389855,\n \"mc2\": 0.538427969031974,\n \"mc2_stderr\": 0.015499026242399048\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5981228668941979,\n \"acc_stderr\": 0.014327268614578274,\n \"acc_norm\": 0.628839590443686,\n \"acc_norm_stderr\": 0.014117971901142822\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.619398526190002,\n \"acc_stderr\": 0.0048454245247640405,\n \"acc_norm\": 0.8201553475403306,\n \"acc_norm_stderr\": 0.003832731017592104\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.030643607071677088,\n \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.030643607071677088\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.027134291628741706,\n \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.027134291628741706\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7986111111111112,\n \"acc_stderr\": 0.033536474697138406,\n \"acc_norm\": 0.7986111111111112,\n \"acc_norm_stderr\": 0.033536474697138406\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7063829787234043,\n \"acc_stderr\": 0.029771642712491227,\n \"acc_norm\": 0.7063829787234043,\n \"acc_norm_stderr\": 0.029771642712491227\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.543859649122807,\n \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.543859649122807,\n \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7103448275862069,\n \"acc_stderr\": 0.03780019230438015,\n \"acc_norm\": 0.7103448275862069,\n \"acc_norm_stderr\": 0.03780019230438015\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.5423280423280423,\n \"acc_stderr\": 0.02565886886205833,\n \"acc_norm\": 0.5423280423280423,\n \"acc_norm_stderr\": 0.02565886886205833\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8387096774193549,\n \"acc_stderr\": 0.020923327006423298,\n \"acc_norm\": 0.8387096774193549,\n \"acc_norm_stderr\": 0.020923327006423298\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5665024630541872,\n \"acc_stderr\": 0.03486731727419872,\n \"acc_norm\": 0.5665024630541872,\n \"acc_norm_stderr\": 0.03486731727419872\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.8939393939393939,\n \"acc_stderr\": 0.021938047738853113,\n \"acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.021938047738853113\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078912,\n \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078912\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.7538461538461538,\n \"acc_stderr\": 0.021840866990423084,\n \"acc_norm\": 0.7538461538461538,\n \"acc_norm_stderr\": 0.021840866990423084\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.02904560029061626,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.02904560029061626\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7983193277310925,\n \"acc_stderr\": 0.026064313406304527,\n \"acc_norm\": 0.7983193277310925,\n \"acc_norm_stderr\": 0.026064313406304527\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4304635761589404,\n \"acc_stderr\": 0.04042809961395634,\n \"acc_norm\": 0.4304635761589404,\n \"acc_norm_stderr\": 0.04042809961395634\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8862385321100917,\n \"acc_stderr\": 0.013613614800232808,\n \"acc_norm\": 0.8862385321100917,\n \"acc_norm_stderr\": 0.013613614800232808\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8823529411764706,\n \"acc_stderr\": 0.022613286601132012,\n \"acc_norm\": 0.8823529411764706,\n \"acc_norm_stderr\": 0.022613286601132012\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8818565400843882,\n \"acc_stderr\": 0.021011052659878463,\n \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.021011052659878463\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.757847533632287,\n \"acc_stderr\": 0.028751392398694755,\n \"acc_norm\": 0.757847533632287,\n \"acc_norm_stderr\": 0.028751392398694755\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.032472243899179465,\n \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.032472243899179465\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580664,\n \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580664\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5982142857142857,\n \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9102564102564102,\n \"acc_stderr\": 0.018724301741941632,\n \"acc_norm\": 0.9102564102564102,\n \"acc_norm_stderr\": 0.018724301741941632\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.879948914431673,\n \"acc_stderr\": 0.011622736692041263,\n \"acc_norm\": 0.879948914431673,\n \"acc_norm_stderr\": 0.011622736692041263\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7658959537572254,\n \"acc_stderr\": 0.022797110278071124,\n \"acc_norm\": 0.7658959537572254,\n \"acc_norm_stderr\": 0.022797110278071124\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.41675977653631285,\n \"acc_stderr\": 0.016489134962438954,\n \"acc_norm\": 0.41675977653631285,\n \"acc_norm_stderr\": 0.016489134962438954\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.023929155517351298,\n \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.023929155517351298\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7717041800643086,\n \"acc_stderr\": 0.023839303311398188,\n \"acc_norm\": 0.7717041800643086,\n \"acc_norm_stderr\": 0.023839303311398188\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7839506172839507,\n \"acc_stderr\": 0.022899162918445796,\n \"acc_norm\": 0.7839506172839507,\n \"acc_norm_stderr\": 0.022899162918445796\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.5390070921985816,\n \"acc_stderr\": 0.02973659252642444,\n \"acc_norm\": 0.5390070921985816,\n \"acc_norm_stderr\": 0.02973659252642444\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5286831812255541,\n \"acc_stderr\": 0.012749206007657459,\n \"acc_norm\": 0.5286831812255541,\n \"acc_norm_stderr\": 0.012749206007657459\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545436,\n \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545436\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8557213930348259,\n \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5602409638554217,\n \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.38310893512851896,\n \"mc1_stderr\": 0.017018461679389855,\n \"mc2\": 0.538427969031974,\n \"mc2_stderr\": 0.015499026242399048\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7995264404104183,\n \"acc_stderr\": 0.011251958281205069\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.599696739954511,\n \"acc_stderr\": 0.013495926436566441\n }\n}\n```", "repo_url": "https://huggingface.co/AA051610/AZG", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|gsm8k\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hellaswag\|10_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T22-11-18.691101.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T22-11-18.691101.parquet", 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["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": 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["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["/details_harness\|winogrande\|5_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-09T22-11-18.691101.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_09T22_11_18.691101", "path": ["results_2023-12-09T22-11-18.691101.parquet"]}, {"split": "latest", "path": ["results_2023-12-09T22-11-18.691101.parquet"]}]}]}	2023-12-09T22:14:50+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of AA051610/AZG ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model AA051610/AZG on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-09T22:11:18.691101(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of AA051610/AZG", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AA051610/AZG on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T22:11:18.691101(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AA051610/AZG", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AA051610/AZG on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-09T22:11:18.691101(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 16, 31, 165, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AA051610/AZG## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AA051610/AZG on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T22:11:18.691101(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
40dcff541f1dc1cbab71bfedd248843ebd97ed6d	# Dataset Card for "idrid_grading" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	marcelittle/idrid_grading	[ "region:us" ]	2023-12-09T23:02:03+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19595295.0, "num_examples": 257}], "download_size": 19434641, "dataset_size": 19595295.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-09T23:02:09+00:00	[]	[]	TAGS #region-us	# Dataset Card for "idrid_grading" More Information needed	[ "# Dataset Card for \"idrid_grading\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"idrid_grading\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"idrid_grading\"\n\nMore Information needed" ]
97b27f3acfb9fa683bdf0c3a344d665e43847e04	# Dataset Card for "rapidapi-example-responses-tokenized-xlm-roberta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses-tokenized-xlm-roberta	[ "region:us" ]	2023-12-09T23:58:32+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 166566222.06213877, "num_examples": 43755}, {"name": "test", "num_bytes": 18508626.93786124, "num_examples": 4862}], "download_size": 62641988, "dataset_size": 185074849.0}}	2023-12-09T23:58:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses-tokenized-xlm-roberta" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses-tokenized-xlm-roberta\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses-tokenized-xlm-roberta\"\n\nMore Information needed" ]	[ 6, 30 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses-tokenized-xlm-roberta\"\n\nMore Information needed" ]
557bc2c1ff6e04709ce61ced1a448b861c861821	# Dataset Card for Evaluation run of NeverSleep/Noromaid-7b-v0.1.1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/NeverSleep/Noromaid-7b-v0.1.1 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [NeverSleep/Noromaid-7b-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-7b-v0.1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_NeverSleep__Noromaid-7b-v0.1.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T00:08:08.403687](https://huggingface.co/datasets/open-llm-leaderboard/details_NeverSleep__Noromaid-7b-v0.1.1/blob/main/results_2023-12-10T00-08-08.403687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6317982878988913, "acc_stderr": 0.032611423846025014, "acc_norm": 0.6377453054345599, "acc_norm_stderr": 0.033270865682523715, "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502353, "mc2": 0.4429984716658762, "mc2_stderr": 0.014505119561026104 }, "harness\|arc:challenge\|25": { "acc": 0.5870307167235495, "acc_stderr": 0.014388344935398326, "acc_norm": 0.6220136518771331, "acc_norm_stderr": 0.014169664520303098 }, "harness\|hellaswag\|10": { "acc": 0.6429994025094603, "acc_stderr": 0.004781358113341955, "acc_norm": 0.842760406293567, "acc_norm_stderr": 0.003632825479128595 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.49019607843137253, "acc_stderr": 0.04974229460422817, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.04974229460422817 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246483, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246483 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7451612903225806, "acc_stderr": 0.024790118459332208, "acc_norm": 0.7451612903225806, "acc_norm_stderr": 0.024790118459332208 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386414, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386414 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8549222797927462, "acc_stderr": 0.02541634309630643, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.02541634309630643 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6358974358974359, "acc_stderr": 0.024396672985094767, "acc_norm": 0.6358974358974359, "acc_norm_stderr": 0.024396672985094767 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.02904560029061626, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.02904560029061626 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.030489911417673227, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.030489911417673227 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3973509933774834, "acc_stderr": 0.039955240076816806, "acc_norm": 0.3973509933774834, "acc_norm_stderr": 0.039955240076816806 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8201834862385321, "acc_stderr": 0.01646534546739155, "acc_norm": 0.8201834862385321, "acc_norm_stderr": 0.01646534546739155 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.0340763209385405, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.0340763209385405 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7794117647058824, "acc_stderr": 0.02910225438967408, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.02910225438967408 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6457399103139013, "acc_stderr": 0.03210062154134987, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.03210062154134987 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728742, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728742 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742179, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742179 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.02363687331748927, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.02363687331748927 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7982120051085568, "acc_stderr": 0.014351702181636863, "acc_norm": 0.7982120051085568, "acc_norm_stderr": 0.014351702181636863 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7109826589595376, "acc_stderr": 0.02440517393578323, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.02440517393578323 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23910614525139665, "acc_stderr": 0.014265554192331152, "acc_norm": 0.23910614525139665, "acc_norm_stderr": 0.014265554192331152 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7331189710610932, "acc_stderr": 0.025122637608816653, "acc_norm": 0.7331189710610932, "acc_norm_stderr": 0.025122637608816653 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765134, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765134 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4406779661016949, "acc_stderr": 0.012680037994097062, "acc_norm": 0.4406779661016949, "acc_norm_stderr": 0.012680037994097062 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.028332959514031215, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.028332959514031215 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6633986928104575, "acc_stderr": 0.019117213911495155, "acc_norm": 0.6633986928104575, "acc_norm_stderr": 0.019117213911495155 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675596, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675596 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8656716417910447, "acc_stderr": 0.02411267824090081, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.02411267824090081 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502353, "mc2": 0.4429984716658762, "mc2_stderr": 0.014505119561026104 }, "harness\|winogrande\|5": { "acc": 0.7790055248618785, "acc_stderr": 0.01166122363764341 }, "harness\|gsm8k\|5": { "acc": 0.3684609552691433, "acc_stderr": 0.013287342651674569 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_NeverSleep__Noromaid-7b-v0.1.1	[ "region:us" ]	2023-12-10T00:10:59+00:00	{"pretty_name": "Evaluation run of NeverSleep/Noromaid-7b-v0.1.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [NeverSleep/Noromaid-7b-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-7b-v0.1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NeverSleep__Noromaid-7b-v0.1.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T00:08:08.403687](https://huggingface.co/datasets/open-llm-leaderboard/details_NeverSleep__Noromaid-7b-v0.1.1/blob/main/results_2023-12-10T00-08-08.403687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6317982878988913,\n \"acc_stderr\": 0.032611423846025014,\n \"acc_norm\": 0.6377453054345599,\n \"acc_norm_stderr\": 0.033270865682523715,\n \"mc1\": 0.2864137086903305,\n \"mc1_stderr\": 0.015826142439502353,\n \"mc2\": 0.4429984716658762,\n \"mc2_stderr\": 0.014505119561026104\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5870307167235495,\n \"acc_stderr\": 0.014388344935398326,\n \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.014169664520303098\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6429994025094603,\n \"acc_stderr\": 0.004781358113341955,\n \"acc_norm\": 0.842760406293567,\n \"acc_norm_stderr\": 0.003632825479128595\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7451612903225806,\n \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7575757575757576,\n 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"path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["/details_harness\|winogrande\|5_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T00-08-08.403687.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T00_08_08.403687", "path": ["results_2023-12-10T00-08-08.403687.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T00-08-08.403687.parquet"]}]}]}	2023-12-10T00:11:43+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of NeverSleep/Noromaid-7b-v0.1.1 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model NeverSleep/Noromaid-7b-v0.1.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T00:08:08.403687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of NeverSleep/Noromaid-7b-v0.1.1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-7b-v0.1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T00:08:08.403687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NeverSleep/Noromaid-7b-v0.1.1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-7b-v0.1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T00:08:08.403687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NeverSleep/Noromaid-7b-v0.1.1## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-7b-v0.1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T00:08:08.403687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
9fcf571ea07cde4e5dd34b8bdae97d3033994b93	# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15-base ## Dataset Description - Homepage: - Repository: https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15-base - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-deepseek-67b-v15-base](https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15-base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T00:18:57.450795](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15-base/blob/main/results_2023-12-10T00-18-57.450795.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7077078977933982, "acc_stderr": 0.030015760444243065, "acc_norm": 0.7114942838020437, "acc_norm_stderr": 0.030600759357365358, "mc1": 0.3671970624235006, "mc1_stderr": 0.016874805001453178, "mc2": 0.5230963516759597, "mc2_stderr": 0.014845955802002899 }, "harness\|arc:challenge\|25": { "acc": 0.6356655290102389, "acc_stderr": 0.014063260279882419, "acc_norm": 0.6629692832764505, "acc_norm_stderr": 0.013813476652902276 }, "harness\|hellaswag\|10": { "acc": 0.6751643098984266, "acc_stderr": 0.004673563250946104, "acc_norm": 0.8602867954590719, "acc_norm_stderr": 0.003459806991389836 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.674074074074074, "acc_stderr": 0.040491220417025055, "acc_norm": 0.674074074074074, "acc_norm_stderr": 0.040491220417025055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8157894736842105, "acc_stderr": 0.0315469804508223, "acc_norm": 0.8157894736842105, "acc_norm_stderr": 0.0315469804508223 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7962264150943397, "acc_stderr": 0.024790784501775406, "acc_norm": 0.7962264150943397, "acc_norm_stderr": 0.024790784501775406 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8541666666666666, "acc_stderr": 0.029514245964291762, "acc_norm": 0.8541666666666666, "acc_norm_stderr": 0.029514245964291762 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7021276595744681, "acc_stderr": 0.02989614568209546, "acc_norm": 0.7021276595744681, "acc_norm_stderr": 0.02989614568209546 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5350877192982456, "acc_stderr": 0.046920083813689104, "acc_norm": 0.5350877192982456, "acc_norm_stderr": 0.046920083813689104 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6896551724137931, "acc_stderr": 0.03855289616378949, "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03855289616378949 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.5317460317460317, "acc_stderr": 0.025699352832131792, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.025699352832131792 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8290322580645161, "acc_stderr": 0.02141724293632159, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.02141724293632159 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5665024630541872, "acc_stderr": 0.034867317274198714, "acc_norm": 0.5665024630541872, "acc_norm_stderr": 0.034867317274198714 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8242424242424242, "acc_stderr": 0.02972094300622445, "acc_norm": 0.8242424242424242, "acc_norm_stderr": 0.02972094300622445 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9090909090909091, "acc_stderr": 0.020482086775424208, "acc_norm": 0.9090909090909091, "acc_norm_stderr": 0.020482086775424208 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9481865284974094, "acc_stderr": 0.01599622932024412, "acc_norm": 0.9481865284974094, "acc_norm_stderr": 0.01599622932024412 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7076923076923077, "acc_stderr": 0.02306043838085774, "acc_norm": 0.7076923076923077, "acc_norm_stderr": 0.02306043838085774 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.029869605095316908, "acc_norm": 0.4, "acc_norm_stderr": 0.029869605095316908 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.819327731092437, "acc_stderr": 0.02499196496660076, "acc_norm": 0.819327731092437, "acc_norm_stderr": 0.02499196496660076 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.41721854304635764, "acc_stderr": 0.04026141497634612, "acc_norm": 0.41721854304635764, "acc_norm_stderr": 0.04026141497634612 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9009174311926605, "acc_stderr": 0.012809780081878927, "acc_norm": 0.9009174311926605, "acc_norm_stderr": 0.012809780081878927 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.033509916046960436, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.033509916046960436 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.019907399791316942, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.019907399791316942 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8945147679324894, "acc_stderr": 0.01999556072375854, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.01999556072375854 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8071748878923767, "acc_stderr": 0.026478240960489365, "acc_norm": 0.8071748878923767, "acc_norm_stderr": 0.026478240960489365 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8396946564885496, "acc_stderr": 0.03217829420744631, "acc_norm": 0.8396946564885496, "acc_norm_stderr": 0.03217829420744631 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8220858895705522, "acc_stderr": 0.03004735765580662, "acc_norm": 0.8220858895705522, "acc_norm_stderr": 0.03004735765580662 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5535714285714286, "acc_stderr": 0.04718471485219587, "acc_norm": 0.5535714285714286, "acc_norm_stderr": 0.04718471485219587 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8446601941747572, "acc_stderr": 0.03586594738573974, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.03586594738573974 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9188034188034188, "acc_stderr": 0.01789378490401853, "acc_norm": 0.9188034188034188, "acc_norm_stderr": 0.01789378490401853 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8991060025542784, "acc_stderr": 0.010770472014886722, "acc_norm": 0.8991060025542784, "acc_norm_stderr": 0.010770472014886722 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7803468208092486, "acc_stderr": 0.02228963885261789, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.02228963885261789 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4145251396648045, "acc_stderr": 0.016476342210253996, "acc_norm": 0.4145251396648045, "acc_norm_stderr": 0.016476342210253996 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7908496732026143, "acc_stderr": 0.023287685312334806, "acc_norm": 0.7908496732026143, "acc_norm_stderr": 0.023287685312334806 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8549382716049383, "acc_stderr": 0.019594877019727956, "acc_norm": 0.8549382716049383, "acc_norm_stderr": 0.019594877019727956 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5177304964539007, "acc_stderr": 0.02980873964223777, "acc_norm": 0.5177304964539007, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5632333767926988, "acc_stderr": 0.012667701919603657, "acc_norm": 0.5632333767926988, "acc_norm_stderr": 0.012667701919603657 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7463235294117647, "acc_stderr": 0.026431329870789527, "acc_norm": 0.7463235294117647, "acc_norm_stderr": 0.026431329870789527 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7794117647058824, "acc_stderr": 0.016774672365468504, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.016774672365468504 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7673469387755102, "acc_stderr": 0.02704925791589618, "acc_norm": 0.7673469387755102, "acc_norm_stderr": 0.02704925791589618 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.93, "acc_stderr": 0.025643239997624294, "acc_norm": 0.93, "acc_norm_stderr": 0.025643239997624294 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8713450292397661, "acc_stderr": 0.02567934272327692, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.02567934272327692 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.016874805001453178, "mc2": 0.5230963516759597, "mc2_stderr": 0.014845955802002899 }, "harness\|winogrande\|5": { "acc": 0.8358326756116812, "acc_stderr": 0.010410849775222782 }, "harness\|gsm8k\|5": { "acc": 0.5686125852918877, "acc_stderr": 0.013642195352511575 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15-base	[ "region:us" ]	2023-12-10T00:21:39+00:00	{"pretty_name": "Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15-base", "dataset_summary": "Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-deepseek-67b-v15-base](https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15-base\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T00:18:57.450795](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15-base/blob/main/results_2023-12-10T00-18-57.450795.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7077078977933982,\n \"acc_stderr\": 0.030015760444243065,\n \"acc_norm\": 0.7114942838020437,\n \"acc_norm_stderr\": 0.030600759357365358,\n \"mc1\": 0.3671970624235006,\n \"mc1_stderr\": 0.016874805001453178,\n \"mc2\": 0.5230963516759597,\n \"mc2_stderr\": 0.014845955802002899\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6356655290102389,\n \"acc_stderr\": 0.014063260279882419,\n \"acc_norm\": 0.6629692832764505,\n \"acc_norm_stderr\": 0.013813476652902276\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6751643098984266,\n \"acc_stderr\": 0.004673563250946104,\n \"acc_norm\": 0.8602867954590719,\n \"acc_norm_stderr\": 0.003459806991389836\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.674074074074074,\n \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7962264150943397,\n \"acc_stderr\": 0.024790784501775406,\n \"acc_norm\": 0.7962264150943397,\n \"acc_norm_stderr\": 0.024790784501775406\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8541666666666666,\n \"acc_stderr\": 0.029514245964291762,\n \"acc_norm\": 0.8541666666666666,\n \"acc_norm_stderr\": 0.029514245964291762\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7021276595744681,\n \"acc_stderr\": 0.02989614568209546,\n \"acc_norm\": 0.7021276595744681,\n \"acc_norm_stderr\": 0.02989614568209546\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5350877192982456,\n \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.5350877192982456,\n \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.6896551724137931,\n \"acc_stderr\": 0.03855289616378949,\n \"acc_norm\": 0.6896551724137931,\n \"acc_norm_stderr\": 0.03855289616378949\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.5317460317460317,\n \"acc_stderr\": 0.025699352832131792,\n \"acc_norm\": 0.5317460317460317,\n \"acc_norm_stderr\": 0.025699352832131792\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8290322580645161,\n \"acc_stderr\": 0.02141724293632159,\n \"acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.02141724293632159\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5665024630541872,\n \"acc_stderr\": 0.034867317274198714,\n \"acc_norm\": 0.5665024630541872,\n \"acc_norm_stderr\": 0.034867317274198714\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8242424242424242,\n \"acc_stderr\": 0.02972094300622445,\n \"acc_norm\": 0.8242424242424242,\n \"acc_norm_stderr\": 0.02972094300622445\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9090909090909091,\n \"acc_stderr\": 0.020482086775424208,\n \"acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.020482086775424208\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.7076923076923077,\n \"acc_stderr\": 0.02306043838085774,\n \"acc_norm\": 0.7076923076923077,\n \"acc_norm_stderr\": 0.02306043838085774\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.029869605095316908,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.029869605095316908\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.819327731092437,\n \"acc_stderr\": 0.02499196496660076,\n \"acc_norm\": 0.819327731092437,\n \"acc_norm_stderr\": 0.02499196496660076\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.41721854304635764,\n \"acc_stderr\": 0.04026141497634612,\n \"acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.04026141497634612\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9009174311926605,\n \"acc_stderr\": 0.012809780081878927,\n \"acc_norm\": 0.9009174311926605,\n \"acc_norm_stderr\": 0.012809780081878927\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.033509916046960436,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.033509916046960436\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316942,\n \"acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316942\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375854,\n \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375854\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8071748878923767,\n \"acc_stderr\": 0.026478240960489365,\n \"acc_norm\": 0.8071748878923767,\n \"acc_norm_stderr\": 0.026478240960489365\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744631,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744631\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580662,\n \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580662\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5535714285714286,\n \"acc_stderr\": 0.04718471485219587,\n \"acc_norm\": 0.5535714285714286,\n \"acc_norm_stderr\": 0.04718471485219587\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9188034188034188,\n \"acc_stderr\": 0.01789378490401853,\n \"acc_norm\": 0.9188034188034188,\n \"acc_norm_stderr\": 0.01789378490401853\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8991060025542784,\n \"acc_stderr\": 0.010770472014886722,\n \"acc_norm\": 0.8991060025542784,\n \"acc_norm_stderr\": 0.010770472014886722\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7803468208092486,\n \"acc_stderr\": 0.02228963885261789,\n \"acc_norm\": 0.7803468208092486,\n \"acc_norm_stderr\": 0.02228963885261789\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4145251396648045,\n \"acc_stderr\": 0.016476342210253996,\n \"acc_norm\": 0.4145251396648045,\n \"acc_norm_stderr\": 0.016476342210253996\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7908496732026143,\n \"acc_stderr\": 0.023287685312334806,\n \"acc_norm\": 0.7908496732026143,\n \"acc_norm_stderr\": 0.023287685312334806\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T00-18-57.450795.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T00-18-57.450795.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T00_18_57.450795", "path": ["/details_harness\|winogrande\|5_2023-12-10T00-18-57.450795.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T00-18-57.450795.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T00_18_57.450795", "path": ["results_2023-12-10T00-18-57.450795.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T00-18-57.450795.parquet"]}]}]}	2023-12-10T00:22:24+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15-base ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseek-67b-v15-base on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T00:18:57.450795(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15-base", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseek-67b-v15-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T00:18:57.450795(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15-base", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseek-67b-v15-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T00:18:57.450795(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 28, 31, 177, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15-base## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseek-67b-v15-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T00:18:57.450795(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
e398dc168a6e5932e5071ed1980cbc06314d4223	# Dataset Card for "cai-conversation-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	vwxyzjn/cai-conversation-dev	[ "region:us" ]	2023-12-10T00:50:16+00:00	{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "init_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "init_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "critic_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "critic_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "revision_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "revision_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_sft", "num_bytes": 9128, "num_examples": 4}, {"name": "train_prefs", "num_bytes": 10733, "num_examples": 4}, {"name": "test_sft", "num_bytes": 15069, "num_examples": 4}, {"name": "test_prefs", "num_bytes": 11987, "num_examples": 4}], "download_size": 126881, "dataset_size": 46917}, "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-"}, {"split": "train_prefs", "path": "data/train_prefs-"}, {"split": "test_sft", "path": "data/test_sft-"}, {"split": "test_prefs", "path": "data/test_prefs-"}]}]}	2024-01-09T19:30:16+00:00	[]	[]	TAGS #region-us	# Dataset Card for "cai-conversation-dev" More Information needed	[ "# Dataset Card for \"cai-conversation-dev\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"cai-conversation-dev\"\n\nMore Information needed" ]
d7606a863f20fc226b727e73f8f1a70ae29e598a	本数据集是为了部分不适合直接显示的角色进行hugging face存储。text部分做了简单的编码加密使用方法载入函数 ```python from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("silk-road/Chat-Haruhi_qwen_1_8", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("silk-road/Chat-Haruhi_qwen_1_8", trust_remote_code=True).half().cuda() model = model.eval() ``` 具体看https://github.com/LC1332/Chat-Haruhi-Suzumiya/blob/main/notebook/ChatHaruhi_x_Qwen1_8B.ipynb 这个notebook ```python from ChatHaruhi import ChatHaruhi chatbot = ChatHaruhi( role_from_hf = 'silk-road/ChatHaruhi-Waifu/女贤者', max_len_story = 1000 ) prompt = chatbot.generate_prompt(role='男子', text = '你已经不能动了') response, _ = model.chat(tokenizer, prompt, history=[]) print(response) chatbot.append_response(response) #模型输出: #女贤者:「啊啊啊，不可以！」 ``` 项目链接https://github.com/LC1332/Chat-Haruhi-Suzumiya 欢迎提供新的语料	silk-road/ChatHaruhi-Waifu	[ "task_categories:text-generation", "size_categories:n<1K", "language:zh", "license:cc-by-4.0", "region:us" ]	2023-12-10T01:00:10+00:00	{"language": ["zh"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"]}	2023-12-10T01:25:05+00:00	[]	[ "zh" ]	TAGS #task_categories-text-generation #size_categories-n<1K #language-Chinese #license-cc-by-4.0 #region-us	本数据集是为了部分不适合直接显示的角色进行hugging face存储。text部分做了简单的编码加密使用方法载入函数具体看https://URL 这个notebook 项目链接https://URL 欢迎提供新的语料	[]	[ "TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Chinese #license-cc-by-4.0 #region-us \n" ]	[ 41 ]	[ "passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Chinese #license-cc-by-4.0 #region-us \n" ]
ce65b53c0fd657004586e9a23ae1128069c01d44	# Dataset Card for "summarize_from_feedback_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	chargoddard/summarize_from_feedback_alpaca	[ "region:us" ]	2023-12-10T01:32:22+00:00	{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 138986664, "num_examples": 92858}], "download_size": 16466576, "dataset_size": 138986664}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-10T01:32:24+00:00	[]	[]	TAGS #region-us	# Dataset Card for "summarize_from_feedback_alpaca" More Information needed	[ "# Dataset Card for \"summarize_from_feedback_alpaca\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"summarize_from_feedback_alpaca\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"summarize_from_feedback_alpaca\"\n\nMore Information needed" ]
5e65876b465126ff5fe0ffd5cacbb8d24d9fc81e	# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-3-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T01:51:52.298552](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-3-Slerp/blob/main/results_2023-12-10T01-51-52.298552.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6460435872902499, "acc_stderr": 0.03203449074198557, "acc_norm": 0.6469349129421068, "acc_norm_stderr": 0.032681317097745945, "mc1": 0.4602203182374541, "mc1_stderr": 0.017448017223960884, "mc2": 0.627788323256757, "mc2_stderr": 0.014997858897015229 }, "harness\|arc:challenge\|25": { "acc": 0.6450511945392492, "acc_stderr": 0.013983036904094092, "acc_norm": 0.6808873720136519, "acc_norm_stderr": 0.013621696119173311 }, "harness\|hellaswag\|10": { "acc": 0.667894841665007, "acc_stderr": 0.00470005967137464, "acc_norm": 0.861979685321649, "acc_norm_stderr": 0.0034421638433628794 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337142, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337142 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406796, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406796 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723285, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723285 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.02912652283458682, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.02912652283458682 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6487179487179487, "acc_stderr": 0.024203665177902803, "acc_norm": 0.6487179487179487, "acc_norm_stderr": 0.024203665177902803 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35185185185185186, "acc_stderr": 0.02911661760608301, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.02911661760608301 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.03006676158297793, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.03006676158297793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588663, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588663 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601432, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601432 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752599, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752599 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8181818181818182, "acc_stderr": 0.03520893951097654, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.03520893951097654 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243839, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243839 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.02410571260775431, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.02410571260775431 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3675977653631285, "acc_stderr": 0.016125543823552954, "acc_norm": 0.3675977653631285, "acc_norm_stderr": 0.016125543823552954 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875192, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875192 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6881028938906752, "acc_stderr": 0.02631185807185416, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.02631185807185416 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4556714471968709, "acc_stderr": 0.012719949543032197, "acc_norm": 0.4556714471968709, "acc_norm_stderr": 0.012719949543032197 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.01899970738316268, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.01899970738316268 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274645, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274645 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8756218905472637, "acc_stderr": 0.023335401790166327, "acc_norm": 0.8756218905472637, "acc_norm_stderr": 0.023335401790166327 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4602203182374541, "mc1_stderr": 0.017448017223960884, "mc2": 0.627788323256757, "mc2_stderr": 0.014997858897015229 }, "harness\|winogrande\|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987726 }, "harness\|gsm8k\|5": { "acc": 0.6777862016679302, "acc_stderr": 0.012872435481188778 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-3-Slerp	[ "region:us" ]	2023-12-10T01:54:43+00:00	{"pretty_name": "Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-3-Slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T01:51:52.298552](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__OpenHermes-2.5-neural-chat-v3-3-Slerp/blob/main/results_2023-12-10T01-51-52.298552.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6460435872902499,\n \"acc_stderr\": 0.03203449074198557,\n \"acc_norm\": 0.6469349129421068,\n \"acc_norm_stderr\": 0.032681317097745945,\n \"mc1\": 0.4602203182374541,\n \"mc1_stderr\": 0.017448017223960884,\n \"mc2\": 0.627788323256757,\n \"mc2_stderr\": 0.014997858897015229\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6450511945392492,\n \"acc_stderr\": 0.013983036904094092,\n \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173311\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.667894841665007,\n \"acc_stderr\": 0.00470005967137464,\n \"acc_norm\": 0.861979685321649,\n \"acc_norm_stderr\": 0.0034421638433628794\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337142,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337142\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406796,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406796\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723285,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723285\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.02912652283458682,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.02912652283458682\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6487179487179487,\n \"acc_stderr\": 0.024203665177902803,\n \"acc_norm\": 0.6487179487179487,\n \"acc_norm_stderr\": 0.024203665177902803\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588663,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588663\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601432,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601432\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752599,\n \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752599\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097654,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097654\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406964\n },\n 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T01-51-52.298552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T01-51-52.298552.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T01_51_52.298552", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T01-51-52.298552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T01-51-52.298552.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T01_51_52.298552", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T01-51-52.298552.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T01-51-52.298552.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T01_51_52.298552", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T01-51-52.298552.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T01-51-52.298552.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T01_51_52.298552", "path": ["/details_harness\|winogrande\|5_2023-12-10T01-51-52.298552.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T01-51-52.298552.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T01_51_52.298552", "path": ["results_2023-12-10T01-51-52.298552.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T01-51-52.298552.parquet"]}]}]}	2023-12-10T01:55:28+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T01:51:52.298552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T01:51:52.298552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T01:51:52.298552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 30, 31, 179, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T01:51:52.298552(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
d52f7989c0968eedcf0d64ac4aae3ba4ea1a6550	# Dataset Card for "mbxp_wasm_no_funcname" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	JeremiahZ/mbxp_wasm_no_funcname	[ "region:us" ]	2023-12-10T01:57:07+00:00	{"dataset_info": {"features": [{"name": "task_id", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "entry_point", "dtype": "string"}, {"name": "canonical_solution", "dtype": "string"}, {"name": "wat", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3916582, "num_examples": 773}], "download_size": 956941, "dataset_size": 3916582}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-10T01:57:12+00:00	[]	[]	TAGS #region-us	# Dataset Card for "mbxp_wasm_no_funcname" More Information needed	[ "# Dataset Card for \"mbxp_wasm_no_funcname\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"mbxp_wasm_no_funcname\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"mbxp_wasm_no_funcname\"\n\nMore Information needed" ]
5285730a29262dad6e1cbd4e5e167b7292a6e17c	# Dataset Card for Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T02:45:05.724710](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp/blob/main/results_2023-12-10T02-45-05.724710.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6464664842416276, "acc_stderr": 0.03217172590988582, "acc_norm": 0.646376680571289, "acc_norm_stderr": 0.032836550184029964, "mc1": 0.39167686658506734, "mc1_stderr": 0.01708779588176963, "mc2": 0.5514034273421413, "mc2_stderr": 0.015341235748555455 }, "harness\|arc:challenge\|25": { "acc": 0.6220136518771331, "acc_stderr": 0.014169664520303098, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.013975454122756564 }, "harness\|hellaswag\|10": { "acc": 0.6632144991037642, "acc_stderr": 0.004716449792353795, "acc_norm": 0.8539135630352519, "acc_norm_stderr": 0.003524710243768616 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.03656343653353159, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353159 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036845, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036845 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944447, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944447 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723292, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723292 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251972, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251972 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601443, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601443 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993452, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993452 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.02410571260775431, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.02410571260775431 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3877094972067039, "acc_stderr": 0.01629533232815581, "acc_norm": 0.3877094972067039, "acc_norm_stderr": 0.01629533232815581 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826524, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826524 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.026082700695399665, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.026082700695399665 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135118, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135118 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422466, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422466 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.45697522816166886, "acc_stderr": 0.012722869501611419, "acc_norm": 0.45697522816166886, "acc_norm_stderr": 0.012722869501611419 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6617647058823529, "acc_stderr": 0.028739328513983572, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.028739328513983572 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.019047485239360378, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.019047485239360378 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8538011695906432, "acc_stderr": 0.02709729011807082, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.02709729011807082 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.39167686658506734, "mc1_stderr": 0.01708779588176963, "mc2": 0.5514034273421413, "mc2_stderr": 0.015341235748555455 }, "harness\|winogrande\|5": { "acc": 0.7963693764798737, "acc_stderr": 0.011317798781626915 }, "harness\|gsm8k\|5": { "acc": 0.7164518574677786, "acc_stderr": 0.012415070917508124 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp	[ "region:us" ]	2023-12-10T02:47:58+00:00	{"pretty_name": "Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T02:45:05.724710](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp/blob/main/results_2023-12-10T02-45-05.724710.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6464664842416276,\n \"acc_stderr\": 0.03217172590988582,\n \"acc_norm\": 0.646376680571289,\n \"acc_norm_stderr\": 0.032836550184029964,\n \"mc1\": 0.39167686658506734,\n \"mc1_stderr\": 0.01708779588176963,\n \"mc2\": 0.5514034273421413,\n \"mc2_stderr\": 0.015341235748555455\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303098,\n \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756564\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6632144991037642,\n \"acc_stderr\": 0.004716449792353795,\n \"acc_norm\": 0.8539135630352519,\n \"acc_norm_stderr\": 0.003524710243768616\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.03656343653353159\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944447,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944447\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8237547892720306,\n \"acc_stderr\": 0.013625556907993452,\n \"acc_norm\": 0.8237547892720306,\n \"acc_norm_stderr\": 0.013625556907993452\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.02410571260775431,\n \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.02410571260775431\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3877094972067039,\n \"acc_stderr\": 0.01629533232815581,\n \"acc_norm\": 0.3877094972067039,\n \"acc_norm_stderr\": 0.01629533232815581\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n \"acc_stderr\": 0.026082700695399665,\n \"acc_norm\": 0.6977491961414791,\n \"acc_norm_stderr\": 0.026082700695399665\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135118,\n \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135118\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422466,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422466\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.45697522816166886,\n \"acc_stderr\": 0.012722869501611419,\n \"acc_norm\": 0.45697522816166886,\n \"acc_norm_stderr\": 0.012722869501611419\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T02-45-05.724710.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T02-45-05.724710.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T02_45_05.724710", "path": ["/details_harness\|winogrande\|5_2023-12-10T02-45-05.724710.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T02-45-05.724710.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T02_45_05.724710", "path": ["results_2023-12-10T02-45-05.724710.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T02-45-05.724710.parquet"]}]}]}	2023-12-10T02:48:41+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T02:45:05.724710(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T02:45:05.724710(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T02:45:05.724710(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 34, 31, 183, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PulsarAI/MetaMath-OpenHermes-2.5-neural-chat-v3-3-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T02:45:05.724710(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
5df6118d465bbc82a8d1d76d93050e501279db17	# Dataset Card for Evaluation run of sequelbox/SunsetBoulevard ## Dataset Description - Homepage: - Repository: https://huggingface.co/sequelbox/SunsetBoulevard - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [sequelbox/SunsetBoulevard](https://huggingface.co/sequelbox/SunsetBoulevard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_sequelbox__SunsetBoulevard", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T03:02:57.544409](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__SunsetBoulevard/blob/main/results_2023-12-10T03-02-57.544409.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7110861444687467, "acc_stderr": 0.030063430253086363, "acc_norm": 0.7154441745417264, "acc_norm_stderr": 0.030639690759115483, "mc1": 0.5569155446756426, "mc1_stderr": 0.01738973034687711, "mc2": 0.7029226076594556, "mc2_stderr": 0.013335950631417065 }, "harness\|arc:challenge\|25": { "acc": 0.6552901023890785, "acc_stderr": 0.01388881628678211, "acc_norm": 0.7133105802047781, "acc_norm_stderr": 0.013214986329274776 }, "harness\|hellaswag\|10": { "acc": 0.7438757219677355, "acc_stderr": 0.004355992090031012, "acc_norm": 0.9095797649870544, "acc_norm_stderr": 0.0028619676953189122 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8157894736842105, "acc_stderr": 0.0315469804508223, "acc_norm": 0.8157894736842105, "acc_norm_stderr": 0.0315469804508223 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.78, "acc_stderr": 0.041633319989322605, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322605 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7358490566037735, "acc_stderr": 0.0271342916287417, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.0271342916287417 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956913, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956913 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6820809248554913, "acc_stderr": 0.035506839891655796, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.035506839891655796 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6893617021276596, "acc_stderr": 0.03025123757921317, "acc_norm": 0.6893617021276596, "acc_norm_stderr": 0.03025123757921317 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6482758620689655, "acc_stderr": 0.0397923663749741, "acc_norm": 0.6482758620689655, "acc_norm_stderr": 0.0397923663749741 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4656084656084656, "acc_stderr": 0.025690321762493848, "acc_norm": 0.4656084656084656, "acc_norm_stderr": 0.025690321762493848 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8258064516129032, "acc_stderr": 0.02157624818451459, "acc_norm": 0.8258064516129032, "acc_norm_stderr": 0.02157624818451459 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5320197044334976, "acc_stderr": 0.035107665979592154, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.035107665979592154 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036624, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036624 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8303030303030303, "acc_stderr": 0.029311188674983127, "acc_norm": 0.8303030303030303, "acc_norm_stderr": 0.029311188674983127 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8787878787878788, "acc_stderr": 0.023253157951942084, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.023253157951942084 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9378238341968912, "acc_stderr": 0.017426974154240528, "acc_norm": 0.9378238341968912, "acc_norm_stderr": 0.017426974154240528 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7128205128205128, "acc_stderr": 0.022939925418530616, "acc_norm": 0.7128205128205128, "acc_norm_stderr": 0.022939925418530616 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7773109243697479, "acc_stderr": 0.02702543349888238, "acc_norm": 0.7773109243697479, "acc_norm_stderr": 0.02702543349888238 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5165562913907285, "acc_stderr": 0.04080244185628972, "acc_norm": 0.5165562913907285, "acc_norm_stderr": 0.04080244185628972 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9100917431192661, "acc_stderr": 0.012264304540230444, "acc_norm": 0.9100917431192661, "acc_norm_stderr": 0.012264304540230444 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6342592592592593, "acc_stderr": 0.032847388576472056, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.032847388576472056 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9166666666666666, "acc_stderr": 0.019398452135813905, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.019398452135813905 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.890295358649789, "acc_stderr": 0.02034340073486884, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.02034340073486884 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8295964125560538, "acc_stderr": 0.025234593447136175, "acc_norm": 0.8295964125560538, "acc_norm_stderr": 0.025234593447136175 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8473282442748091, "acc_stderr": 0.031545216720054725, "acc_norm": 0.8473282442748091, "acc_norm_stderr": 0.031545216720054725 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8512396694214877, "acc_stderr": 0.03248470083807194, "acc_norm": 0.8512396694214877, "acc_norm_stderr": 0.03248470083807194 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.03602814176392645, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.03602814176392645 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8404907975460123, "acc_stderr": 0.028767481725983854, "acc_norm": 0.8404907975460123, "acc_norm_stderr": 0.028767481725983854 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5267857142857143, "acc_stderr": 0.047389751192741546, "acc_norm": 0.5267857142857143, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8349514563106796, "acc_stderr": 0.036756688322331886, "acc_norm": 0.8349514563106796, "acc_norm_stderr": 0.036756688322331886 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132346, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132346 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8722860791826309, "acc_stderr": 0.011935626313999876, "acc_norm": 0.8722860791826309, "acc_norm_stderr": 0.011935626313999876 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7976878612716763, "acc_stderr": 0.02162807738019612, "acc_norm": 0.7976878612716763, "acc_norm_stderr": 0.02162807738019612 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.6122905027932961, "acc_stderr": 0.016295332328155807, "acc_norm": 0.6122905027932961, "acc_norm_stderr": 0.016295332328155807 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7712418300653595, "acc_stderr": 0.024051029739912258, "acc_norm": 0.7712418300653595, "acc_norm_stderr": 0.024051029739912258 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.77491961414791, "acc_stderr": 0.023720088516179027, "acc_norm": 0.77491961414791, "acc_norm_stderr": 0.023720088516179027 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.02073635840806, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.02073635840806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.599290780141844, "acc_stderr": 0.029233465745573096, "acc_norm": 0.599290780141844, "acc_norm_stderr": 0.029233465745573096 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5684485006518905, "acc_stderr": 0.012650007999463909, "acc_norm": 0.5684485006518905, "acc_norm_stderr": 0.012650007999463909 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.026799562024887667, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.026799562024887667 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7679738562091504, "acc_stderr": 0.01707737337785693, "acc_norm": 0.7679738562091504, "acc_norm_stderr": 0.01707737337785693 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940588, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940588 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8122448979591836, "acc_stderr": 0.02500025603954619, "acc_norm": 0.8122448979591836, "acc_norm_stderr": 0.02500025603954619 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8706467661691543, "acc_stderr": 0.02372983088101853, "acc_norm": 0.8706467661691543, "acc_norm_stderr": 0.02372983088101853 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.02876234912646612, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646612 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015575, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015575 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5569155446756426, "mc1_stderr": 0.01738973034687711, "mc2": 0.7029226076594556, "mc2_stderr": 0.013335950631417065 }, "harness\|winogrande\|5": { "acc": 0.8421468034727704, "acc_stderr": 0.010247165248719763 }, "harness\|gsm8k\|5": { "acc": 0.5466262319939348, "acc_stderr": 0.013712471049515446 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_sequelbox__SunsetBoulevard	[ "region:us" ]	2023-12-10T03:05:57+00:00	{"pretty_name": "Evaluation run of sequelbox/SunsetBoulevard", "dataset_summary": "Dataset automatically created during the evaluation run of model [sequelbox/SunsetBoulevard](https://huggingface.co/sequelbox/SunsetBoulevard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sequelbox__SunsetBoulevard\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T03:02:57.544409](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__SunsetBoulevard/blob/main/results_2023-12-10T03-02-57.544409.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7110861444687467,\n \"acc_stderr\": 0.030063430253086363,\n \"acc_norm\": 0.7154441745417264,\n \"acc_norm_stderr\": 0.030639690759115483,\n \"mc1\": 0.5569155446756426,\n \"mc1_stderr\": 0.01738973034687711,\n \"mc2\": 0.7029226076594556,\n \"mc2_stderr\": 0.013335950631417065\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6552901023890785,\n \"acc_stderr\": 0.01388881628678211,\n \"acc_norm\": 0.7133105802047781,\n \"acc_norm_stderr\": 0.013214986329274776\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7438757219677355,\n \"acc_stderr\": 0.004355992090031012,\n \"acc_norm\": 0.9095797649870544,\n \"acc_norm_stderr\": 0.0028619676953189122\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322605,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322605\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.0271342916287417,\n \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.0271342916287417\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.035506839891655796,\n \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.035506839891655796\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.6482758620689655,\n \"acc_stderr\": 0.0397923663749741,\n \"acc_norm\": 0.6482758620689655,\n \"acc_norm_stderr\": 0.0397923663749741\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4656084656084656,\n \"acc_stderr\": 0.025690321762493848,\n \"acc_norm\": 0.4656084656084656,\n \"acc_norm_stderr\": 0.025690321762493848\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.48412698412698413,\n \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8258064516129032,\n \"acc_stderr\": 0.02157624818451459,\n \"acc_norm\": 0.8258064516129032,\n \"acc_norm_stderr\": 0.02157624818451459\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983127,\n \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983127\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.8787878787878788,\n \"acc_stderr\": 0.023253157951942084,\n \"acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.023253157951942084\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240528,\n \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240528\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530616,\n \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530616\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7773109243697479,\n \"acc_stderr\": 0.02702543349888238,\n \"acc_norm\": 0.7773109243697479,\n \"acc_norm_stderr\": 0.02702543349888238\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.5165562913907285,\n \"acc_stderr\": 0.04080244185628972,\n \"acc_norm\": 0.5165562913907285,\n \"acc_norm_stderr\": 0.04080244185628972\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9100917431192661,\n \"acc_stderr\": 0.012264304540230444,\n \"acc_norm\": 0.9100917431192661,\n \"acc_norm_stderr\": 0.012264304540230444\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n \"acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.890295358649789,\n \"acc_stderr\": 0.02034340073486884,\n \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.02034340073486884\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8295964125560538,\n \"acc_stderr\": 0.025234593447136175,\n \"acc_norm\": 0.8295964125560538,\n \"acc_norm_stderr\": 0.025234593447136175\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8404907975460123,\n \"acc_stderr\": 0.028767481725983854,\n \"acc_norm\": 0.8404907975460123,\n \"acc_norm_stderr\": 0.028767481725983854\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5267857142857143,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n \"acc_stderr\": 0.017004368568132346,\n \"acc_norm\": 0.9273504273504274,\n \"acc_norm_stderr\": 0.017004368568132346\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8722860791826309,\n \"acc_stderr\": 0.011935626313999876,\n \"acc_norm\": 0.8722860791826309,\n \"acc_norm_stderr\": 0.011935626313999876\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7976878612716763,\n \"acc_stderr\": 0.02162807738019612,\n \"acc_norm\": 0.7976878612716763,\n \"acc_norm_stderr\": 0.02162807738019612\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.6122905027932961,\n \"acc_stderr\": 0.016295332328155807,\n \"acc_norm\": 0.6122905027932961,\n \"acc_norm_stderr\": 0.016295332328155807\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7712418300653595,\n \"acc_stderr\": 0.024051029739912258,\n \"acc_norm\": 0.7712418300653595,\n \"acc_norm_stderr\": 0.024051029739912258\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.77491961414791,\n \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.02073635840806,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.02073635840806\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.599290780141844,\n \"acc_stderr\": 0.029233465745573096,\n \"acc_norm\": 0.599290780141844,\n \"acc_norm_stderr\": 0.029233465745573096\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5684485006518905,\n \"acc_stderr\": 0.012650007999463909,\n \"acc_norm\": 0.5684485006518905,\n \"acc_norm_stderr\": 0.012650007999463909\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.026799562024887667,\n \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.026799562024887667\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.7679738562091504,\n \"acc_stderr\": 0.01707737337785693,\n \"acc_norm\": 0.7679738562091504,\n \"acc_norm_stderr\": 0.01707737337785693\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04265792110940588,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04265792110940588\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.02500025603954619,\n \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.02500025603954619\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8706467661691543,\n \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646612,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646612\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5569155446756426,\n \"mc1_stderr\": 0.01738973034687711,\n \"mc2\": 0.7029226076594556,\n \"mc2_stderr\": 0.013335950631417065\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8421468034727704,\n \"acc_stderr\": 0.010247165248719763\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5466262319939348,\n \"acc_stderr\": 0.013712471049515446\n }\n}\n```", "repo_url": "https://huggingface.co/sequelbox/SunsetBoulevard", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|gsm8k\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hellaswag\|10_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T03-02-57.544409.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T03-02-57.544409.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["/details_harness\|winogrande\|5_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T03-02-57.544409.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T03_02_57.544409", "path": ["results_2023-12-10T03-02-57.544409.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T03-02-57.544409.parquet"]}]}]}	2023-12-10T03:06:40+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of sequelbox/SunsetBoulevard ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model sequelbox/SunsetBoulevard on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T03:02:57.544409(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of sequelbox/SunsetBoulevard", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model sequelbox/SunsetBoulevard on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T03:02:57.544409(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of sequelbox/SunsetBoulevard", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model sequelbox/SunsetBoulevard on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T03:02:57.544409(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 18, 31, 167, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of sequelbox/SunsetBoulevard## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model sequelbox/SunsetBoulevard on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T03:02:57.544409(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
4d2a9c731160fcb79fc97c3f826fa20962f40208	# Dataset Card for Evaluation run of chargoddard/piano-medley-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/chargoddard/piano-medley-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [chargoddard/piano-medley-7b](https://huggingface.co/chargoddard/piano-medley-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chargoddard__piano-medley-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T03:24:54.482171](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__piano-medley-7b/blob/main/results_2023-12-10T03-24-54.482171.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6462767300930756, "acc_stderr": 0.032134853847514466, "acc_norm": 0.6489933678568897, "acc_norm_stderr": 0.03277330582106223, "mc1": 0.44063647490820074, "mc1_stderr": 0.017379697555437446, "mc2": 0.6142054505900651, "mc2_stderr": 0.015456544162012987 }, "harness\|arc:challenge\|25": { "acc": 0.6399317406143344, "acc_stderr": 0.014027516814585186, "acc_norm": 0.6757679180887372, "acc_norm_stderr": 0.013678810399518826 }, "harness\|hellaswag\|10": { "acc": 0.6645090619398526, "acc_stderr": 0.004711968379069029, "acc_norm": 0.8536148177653854, "acc_norm_stderr": 0.0035276951498235004 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.04115324610336953, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.04115324610336953 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.0358687928008034, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3941798941798942, "acc_stderr": 0.02516798233389414, "acc_norm": 0.3941798941798942, "acc_norm_stderr": 0.02516798233389414 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8, "acc_stderr": 0.02275520495954294, "acc_norm": 0.8, "acc_norm_stderr": 0.02275520495954294 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.02833560973246336, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.02833560973246336 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.023661296393964273, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.023661296393964273 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948482, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948482 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6974789915966386, "acc_stderr": 0.029837962388291932, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.029837962388291932 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126253, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126253 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7932489451476793, "acc_stderr": 0.026361651668389094, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.026361651668389094 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7130044843049327, "acc_stderr": 0.03036037971029195, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.03036037971029195 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507332, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507332 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8263090676883781, "acc_stderr": 0.013547415658662253, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.013547415658662253 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577612, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577612 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.41787709497206704, "acc_stderr": 0.01649540063582008, "acc_norm": 0.41787709497206704, "acc_norm_stderr": 0.01649540063582008 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7314814814814815, "acc_stderr": 0.02465968518596728, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.02465968518596728 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.45371577574967403, "acc_stderr": 0.01271540484127774, "acc_norm": 0.45371577574967403, "acc_norm_stderr": 0.01271540484127774 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.028064998167040094, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.028064998167040094 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6519607843137255, "acc_stderr": 0.019270998708223977, "acc_norm": 0.6519607843137255, "acc_norm_stderr": 0.019270998708223977 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.044612721759105085, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.044612721759105085 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142783, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142783 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306053, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306053 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.44063647490820074, "mc1_stderr": 0.017379697555437446, "mc2": 0.6142054505900651, "mc2_stderr": 0.015456544162012987 }, "harness\|winogrande\|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987729 }, "harness\|gsm8k\|5": { "acc": 0.5655799848369977, "acc_stderr": 0.013653507211411417 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_chargoddard__piano-medley-7b	[ "region:us" ]	2023-12-10T03:27:47+00:00	{"pretty_name": "Evaluation run of chargoddard/piano-medley-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [chargoddard/piano-medley-7b](https://huggingface.co/chargoddard/piano-medley-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__piano-medley-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T03:24:54.482171](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__piano-medley-7b/blob/main/results_2023-12-10T03-24-54.482171.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6462767300930756,\n \"acc_stderr\": 0.032134853847514466,\n \"acc_norm\": 0.6489933678568897,\n \"acc_norm_stderr\": 0.03277330582106223,\n \"mc1\": 0.44063647490820074,\n \"mc1_stderr\": 0.017379697555437446,\n \"mc2\": 0.6142054505900651,\n \"mc2_stderr\": 0.015456544162012987\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6399317406143344,\n \"acc_stderr\": 0.014027516814585186,\n \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.013678810399518826\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6645090619398526,\n \"acc_stderr\": 0.004711968379069029,\n \"acc_norm\": 0.8536148177653854,\n \"acc_norm_stderr\": 0.0035276951498235004\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.04115324610336953,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.04115324610336953\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3941798941798942,\n \"acc_stderr\": 0.02516798233389414,\n \"acc_norm\": 0.3941798941798942,\n \"acc_norm_stderr\": 0.02516798233389414\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02275520495954294,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02275520495954294\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.023661296393964273,\n \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.023661296393964273\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948482,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948482\n },\n 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["/details_harness\|winogrande\|5_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T03-24-54.482171.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T03_24_54.482171", "path": ["results_2023-12-10T03-24-54.482171.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T03-24-54.482171.parquet"]}]}]}	2023-12-10T03:28:30+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of chargoddard/piano-medley-7b ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model chargoddard/piano-medley-7b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T03:24:54.482171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of chargoddard/piano-medley-7b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/piano-medley-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T03:24:54.482171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of chargoddard/piano-medley-7b", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/piano-medley-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T03:24:54.482171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 21, 31, 170, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of chargoddard/piano-medley-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/piano-medley-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T03:24:54.482171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
fe9a7c5cfba77d1c0f7185474254642a80b15ed8	This is a direct Chinese translation using GPT4 of the Verified-Camel dataset. I hope you find it useful. https://huggingface.co/datasets/LDJnr/Verified-Camel Citation: ``` @article{daniele2023amplify-instruct, title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.}, author={Daniele, Luigi and Suphavadeeprasit}, journal={arXiv preprint arXiv:(comming soon)}, year={2023} } ```	noobmaster29/Verified-Camel-zh	[ "task_categories:conversational", "task_categories:question-answering", "task_categories:text-generation", "size_categories:n<1K", "language:en", "language:zh", "license:apache-2.0", "Physics", "Chemistry", "Math", "Biology", "Culture", "Logic", "region:us" ]	2023-12-10T03:40:28+00:00	{"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["conversational", "question-answering", "text-generation"], "tags": ["Physics", "Chemistry", "Math", "Biology", "Culture", "Logic"]}	2023-12-10T03:57:00+00:00	[]	[ "en", "zh" ]	TAGS #task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-English #language-Chinese #license-apache-2.0 #Physics #Chemistry #Math #Biology #Culture #Logic #region-us	This is a direct Chinese translation using GPT4 of the Verified-Camel dataset. I hope you find it useful. URL Citation:	[]	[ "TAGS\n#task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-English #language-Chinese #license-apache-2.0 #Physics #Chemistry #Math #Biology #Culture #Logic #region-us \n" ]	[ 86 ]	[ "passage: TAGS\n#task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-English #language-Chinese #license-apache-2.0 #Physics #Chemistry #Math #Biology #Culture #Logic #region-us \n" ]
4caac69447532b1d6d6fb0e26154c274cd1b32b0	The following data set was vectorized with the [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) model and an index file created by faiss. [oshizo/japanese-wikipedia-paragraphs](https://huggingface.co/datasets/oshizo/japanese-wikipedia-paragraphs) ## Usage First, download index_me5-base_IVF2048_PQ192.faiss from this repository. ```python import faiss import datasets from sentence_transformers import SentenceTransformer ds = datasets.load_dataset("oshizo/japanese-wikipedia-paragraphs", split="train") index = faiss.read_index("./index_me5-base_IVF2048_PQ192.faiss") model = SentenceTransformer("intfloat/multilingual-e5-base") question = "日本で二番目に高い山は？" emb = model.encode(["query: " + question]) scores, indexes = index.search(emb, 10) scores = scores[0] indexes = indexes[0] results = [] for idx, score in zip(indexes, scores): idx = int(idx) passage = ds[idx] passage["score"] = score results.append((passage))	oshizo/japanese-wikipedia-paragraphs-embeddings	[ "language:ja", "license:cc-by-sa-4.0", "region:us" ]	2023-12-10T03:41:14+00:00	{"language": ["ja"], "license": "cc-by-sa-4.0"}	2023-12-15T13:16:42+00:00	[]	[ "ja" ]	TAGS #language-Japanese #license-cc-by-sa-4.0 #region-us	The following data set was vectorized with the intfloat/multilingual-e5-base model and an index file created by faiss. oshizo/japanese-wikipedia-paragraphs ## Usage First, download index_me5-base_IVF2048_PQ192.faiss from this repository. '''python import faiss import datasets from sentence_transformers import SentenceTransformer ds = datasets.load_dataset("oshizo/japanese-wikipedia-paragraphs", split="train") index = faiss.read_index("./index_me5-base_IVF2048_PQ192.faiss") model = SentenceTransformer("intfloat/multilingual-e5-base") question = "日本で二番目に高い山は？" emb = URL(["query: " + question]) scores, indexes = URL(emb, 10) scores = scores[0] indexes = indexes[0] results = [] for idx, score in zip(indexes, scores): idx = int(idx) passage = ds[idx] passage["score"] = score URL((passage))	[ "## Usage\n\nFirst, download index_me5-base_IVF2048_PQ192.faiss from this repository.\n\n'''python\nimport faiss\nimport datasets\nfrom sentence_transformers import SentenceTransformer\n\nds = datasets.load_dataset(\"oshizo/japanese-wikipedia-paragraphs\", split=\"train\")\n\nindex = faiss.read_index(\"./index_me5-base_IVF2048_PQ192.faiss\")\n\nmodel = SentenceTransformer(\"intfloat/multilingual-e5-base\")\n\nquestion = \"日本で二番目に高い山は？\"\nemb = URL([\"query: \" + question])\nscores, indexes = URL(emb, 10)\nscores = scores[0]\nindexes = indexes[0]\n\nresults = []\nfor idx, score in zip(indexes, scores):\n idx = int(idx)\n passage = ds[idx]\n passage[\"score\"] = score\n URL((passage))" ]	[ "TAGS\n#language-Japanese #license-cc-by-sa-4.0 #region-us \n", "## Usage\n\nFirst, download index_me5-base_IVF2048_PQ192.faiss from this repository.\n\n'''python\nimport faiss\nimport datasets\nfrom sentence_transformers import SentenceTransformer\n\nds = datasets.load_dataset(\"oshizo/japanese-wikipedia-paragraphs\", split=\"train\")\n\nindex = faiss.read_index(\"./index_me5-base_IVF2048_PQ192.faiss\")\n\nmodel = SentenceTransformer(\"intfloat/multilingual-e5-base\")\n\nquestion = \"日本で二番目に高い山は？\"\nemb = URL([\"query: \" + question])\nscores, indexes = URL(emb, 10)\nscores = scores[0]\nindexes = indexes[0]\n\nresults = []\nfor idx, score in zip(indexes, scores):\n idx = int(idx)\n passage = ds[idx]\n passage[\"score\"] = score\n URL((passage))" ]	[ 23, 238 ]	[ "passage: TAGS\n#language-Japanese #license-cc-by-sa-4.0 #region-us \n## Usage\n\nFirst, download index_me5-base_IVF2048_PQ192.faiss from this repository.\n\n'''python\nimport faiss\nimport datasets\nfrom sentence_transformers import SentenceTransformer\n\nds = datasets.load_dataset(\"oshizo/japanese-wikipedia-paragraphs\", split=\"train\")\n\nindex = faiss.read_index(\"./index_me5-base_IVF2048_PQ192.faiss\")\n\nmodel = SentenceTransformer(\"intfloat/multilingual-e5-base\")\n\nquestion = \"日本で二番目に高い山は？\"\nemb = URL([\"query: \" + question])\nscores, indexes = URL(emb, 10)\nscores = scores[0]\nindexes = indexes[0]\n\nresults = []\nfor idx, score in zip(indexes, scores):\n idx = int(idx)\n passage = ds[idx]\n passage[\"score\"] = score\n URL((passage))" ]
00ffcde7262f2ca67ed149328b86712014e7ee4b	# Synthetic Malaysian QA Generated common QA using ChatGPT3 for, 1. Agrobank 2. Bank Negara Malaysia 3. Bank Perusahaan Kecil dan Sederhana Malaysia 4. Bank Rakyat 5. Bank Simpanan Nasional 6. Bursa Malaysia 7. Dewan Bahasa dan Pustaka 8. Institut Kesihatan Umum 9. Institut Penyelidikan Perubatan 10. Institut Penyelidikan Sains dan Teknologi Pertahanan 11. Institut Penyelidikan Tingkahlaku Kesihatan 12. Institut Penyelidikan dan Kemajuan Pertanian Malaysia 13. Jabatan Akauntan Negara 14. Jabatan Bomba dan Penyelamat Malaysia 15. Jabatan Hal Ehwal Kesatuan Sekerja 16. Jabatan Hal Ehwal Veteran 17. Jabatan Imigresen Malaysia 18. Jabatan Kastam Diraja Malaysia 19. Jabatan Kebajikan Masyarakat 20. Jabatan Kemajuan Orang Asli 21. Jabatan Kerajaan Tempatan 22. Jabatan Kerja Raya 23. Jabatan Keselamatan Jalan Raya 24. Jabatan Keselamatan dan Keselamatan Pekerjaan 25. Jabatan Ketua Hakim Peguam 26. Jabatan Landskap Negara 27. Jabatan Latihan Khidmat Negara 28. Jabatan Laut Malaysia 29. Jabatan Pembangunan Wanita 30. Jabatan Pendaftaran Pertubuhan Malaysia 31. Jabatan Penerangan Malaysia 32. Jabatan Pengangkutan Jalan 33. Jabatan Pengurusan Sisa Pepejal Negara 34. Jabatan Penilaian dan Perkhidmatan Negara 35. Jabatan Penjara Malaysia 36. Jabatan Perancangan Bandar dan Desa 37. Jabatan Perdana Menteri Malaysia 38. Jabatan Perhubungan Perusahaan 39. Jabatan Perikanan Malaysia 40. Jabatan Perkhidmatan Kuarantin dan Pemeriksaan Malaysia 41. Jabatan Perkhidmatan Veterinar 42. Jabatan Pertanian Malaysia 43. Jabatan Perumahan Negara 44. Jabatan Perumahan dan Pengurusan Strata 45. Jabatan Sukarelawan Malaysia 46. Jabatan Tenaga Kerja 47. Jabatan Tenaga Kerja Manusia 48. Khazanah Nasional 49. Kolej Pertanian 50. Kumpulan Wang Persaraan 51. Kumpulan Wang Simpanan Pekerja 52. Lembaga Hasil Dalam Negeri Malaysia 53. Lembaga Kemajuan Ikan Malaysia 54. Lembaga Kemajuan Pertanian Kemubu 55. Lembaga Kemajuan Pertanian Muda 56. Lembaga Pelabuhan Bintulu 57. Lembaga Pelabuhan Johor 58. Lembaga Pelabuhan Klang 59. Lembaga Pelabuhan Kuantan 60. Lembaga Pemasaran Pertanian Persekutuan 61. Lembaga Pembangunan Pelaburan Malaysia 62. Lembaga Pembiayaan Perumahan Sektor Awam 63. Lembaga Penapisan Filem 64. Lembaga Penduduk dan Pembangunan Keluarga Negara 65. Lembaga Peperiksaan Malaysia 66. Lembaga Perindustrian Nanas Malaysia 67. Lembaga Perkhidmatan Kewangan Labuan 68. Lembaga Pertubuhan Peladang 69. Lembaga Promosi Kesihatan Malaysia 70. Lembaga Totalisator Malaysia 71. Pusat Pergigian Kanak-Kanak & Kolej Latihan Pergigian Malaysia Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/chatgpt3.5-synthetic-malaysian-qa	mesolitica/chatgpt-malaysian-general-qa	[ "region:us" ]	2023-12-10T04:12:32+00:00	{}	2023-12-10T18:50:21+00:00	[]	[]	TAGS #region-us	# Synthetic Malaysian QA Generated common QA using ChatGPT3 for, 1. Agrobank 2. Bank Negara Malaysia 3. Bank Perusahaan Kecil dan Sederhana Malaysia 4. Bank Rakyat 5. Bank Simpanan Nasional 6. Bursa Malaysia 7. Dewan Bahasa dan Pustaka 8. Institut Kesihatan Umum 9. Institut Penyelidikan Perubatan 10. Institut Penyelidikan Sains dan Teknologi Pertahanan 11. Institut Penyelidikan Tingkahlaku Kesihatan 12. Institut Penyelidikan dan Kemajuan Pertanian Malaysia 13. Jabatan Akauntan Negara 14. Jabatan Bomba dan Penyelamat Malaysia 15. Jabatan Hal Ehwal Kesatuan Sekerja 16. Jabatan Hal Ehwal Veteran 17. Jabatan Imigresen Malaysia 18. Jabatan Kastam Diraja Malaysia 19. Jabatan Kebajikan Masyarakat 20. Jabatan Kemajuan Orang Asli 21. Jabatan Kerajaan Tempatan 22. Jabatan Kerja Raya 23. Jabatan Keselamatan Jalan Raya 24. Jabatan Keselamatan dan Keselamatan Pekerjaan 25. Jabatan Ketua Hakim Peguam 26. Jabatan Landskap Negara 27. Jabatan Latihan Khidmat Negara 28. Jabatan Laut Malaysia 29. Jabatan Pembangunan Wanita 30. Jabatan Pendaftaran Pertubuhan Malaysia 31. Jabatan Penerangan Malaysia 32. Jabatan Pengangkutan Jalan 33. Jabatan Pengurusan Sisa Pepejal Negara 34. Jabatan Penilaian dan Perkhidmatan Negara 35. Jabatan Penjara Malaysia 36. Jabatan Perancangan Bandar dan Desa 37. Jabatan Perdana Menteri Malaysia 38. Jabatan Perhubungan Perusahaan 39. Jabatan Perikanan Malaysia 40. Jabatan Perkhidmatan Kuarantin dan Pemeriksaan Malaysia 41. Jabatan Perkhidmatan Veterinar 42. Jabatan Pertanian Malaysia 43. Jabatan Perumahan Negara 44. Jabatan Perumahan dan Pengurusan Strata 45. Jabatan Sukarelawan Malaysia 46. Jabatan Tenaga Kerja 47. Jabatan Tenaga Kerja Manusia 48. Khazanah Nasional 49. Kolej Pertanian 50. Kumpulan Wang Persaraan 51. Kumpulan Wang Simpanan Pekerja 52. Lembaga Hasil Dalam Negeri Malaysia 53. Lembaga Kemajuan Ikan Malaysia 54. Lembaga Kemajuan Pertanian Kemubu 55. Lembaga Kemajuan Pertanian Muda 56. Lembaga Pelabuhan Bintulu 57. Lembaga Pelabuhan Johor 58. Lembaga Pelabuhan Klang 59. Lembaga Pelabuhan Kuantan 60. Lembaga Pemasaran Pertanian Persekutuan 61. Lembaga Pembangunan Pelaburan Malaysia 62. Lembaga Pembiayaan Perumahan Sektor Awam 63. Lembaga Penapisan Filem 64. Lembaga Penduduk dan Pembangunan Keluarga Negara 65. Lembaga Peperiksaan Malaysia 66. Lembaga Perindustrian Nanas Malaysia 67. Lembaga Perkhidmatan Kewangan Labuan 68. Lembaga Pertubuhan Peladang 69. Lembaga Promosi Kesihatan Malaysia 70. Lembaga Totalisator Malaysia 71. Pusat Pergigian Kanak-Kanak & Kolej Latihan Pergigian Malaysia Notebooks at URL	[ "# Synthetic Malaysian QA\n\nGenerated common QA using ChatGPT3 for,\n1. Agrobank\n2. Bank Negara Malaysia\n3. Bank Perusahaan Kecil dan Sederhana Malaysia\n4. Bank Rakyat\n5. Bank Simpanan Nasional\n6. Bursa Malaysia\n7. Dewan Bahasa dan Pustaka\n8. Institut Kesihatan Umum\n9. Institut Penyelidikan Perubatan\n10. Institut Penyelidikan Sains dan Teknologi Pertahanan\n11. Institut Penyelidikan Tingkahlaku Kesihatan\n12. Institut Penyelidikan dan Kemajuan Pertanian Malaysia\n13. Jabatan Akauntan Negara\n14. Jabatan Bomba dan Penyelamat Malaysia\n15. Jabatan Hal Ehwal Kesatuan Sekerja\n16. Jabatan Hal Ehwal Veteran\n17. Jabatan Imigresen Malaysia\n18. Jabatan Kastam Diraja Malaysia\n19. Jabatan Kebajikan Masyarakat\n20. Jabatan Kemajuan Orang Asli\n21. Jabatan Kerajaan Tempatan\n22. Jabatan Kerja Raya\n23. Jabatan Keselamatan Jalan Raya\n24. Jabatan Keselamatan dan Keselamatan Pekerjaan\n25. Jabatan Ketua Hakim Peguam\n26. Jabatan Landskap Negara\n27. Jabatan Latihan Khidmat Negara\n28. Jabatan Laut Malaysia\n29. Jabatan Pembangunan Wanita\n30. Jabatan Pendaftaran Pertubuhan Malaysia\n31. Jabatan Penerangan Malaysia\n32. Jabatan Pengangkutan Jalan\n33. Jabatan Pengurusan Sisa Pepejal Negara\n34. Jabatan Penilaian dan Perkhidmatan Negara\n35. Jabatan Penjara Malaysia\n36. Jabatan Perancangan Bandar dan Desa\n37. Jabatan Perdana Menteri Malaysia\n38. Jabatan Perhubungan Perusahaan\n39. Jabatan Perikanan Malaysia\n40. Jabatan Perkhidmatan Kuarantin dan Pemeriksaan Malaysia\n41. Jabatan Perkhidmatan Veterinar\n42. Jabatan Pertanian Malaysia\n43. Jabatan Perumahan Negara\n44. Jabatan Perumahan dan Pengurusan Strata\n45. Jabatan Sukarelawan Malaysia\n46. Jabatan Tenaga Kerja\n47. Jabatan Tenaga Kerja Manusia\n48. Khazanah Nasional\n49. Kolej Pertanian\n50. Kumpulan Wang Persaraan\n51. Kumpulan Wang Simpanan Pekerja\n52. Lembaga Hasil Dalam Negeri Malaysia\n53. Lembaga Kemajuan Ikan Malaysia\n54. Lembaga Kemajuan Pertanian Kemubu\n55. Lembaga Kemajuan Pertanian Muda\n56. Lembaga Pelabuhan Bintulu\n57. Lembaga Pelabuhan Johor\n58. Lembaga Pelabuhan Klang\n59. Lembaga Pelabuhan Kuantan\n60. Lembaga Pemasaran Pertanian Persekutuan\n61. Lembaga Pembangunan Pelaburan Malaysia\n62. Lembaga Pembiayaan Perumahan Sektor Awam\n63. Lembaga Penapisan Filem\n64. Lembaga Penduduk dan Pembangunan Keluarga Negara\n65. Lembaga Peperiksaan Malaysia\n66. Lembaga Perindustrian Nanas Malaysia\n67. Lembaga Perkhidmatan Kewangan Labuan\n68. Lembaga Pertubuhan Peladang\n69. Lembaga Promosi Kesihatan Malaysia\n70. Lembaga Totalisator Malaysia\n71. Pusat Pergigian Kanak-Kanak & Kolej Latihan Pergigian Malaysia\n\nNotebooks at URL" ]	[ "TAGS\n#region-us \n", "# Synthetic Malaysian QA\n\nGenerated common QA using ChatGPT3 for,\n1. Agrobank\n2. Bank Negara Malaysia\n3. Bank Perusahaan Kecil dan Sederhana Malaysia\n4. Bank Rakyat\n5. Bank Simpanan Nasional\n6. Bursa Malaysia\n7. Dewan Bahasa dan Pustaka\n8. Institut Kesihatan Umum\n9. Institut Penyelidikan Perubatan\n10. Institut Penyelidikan Sains dan Teknologi Pertahanan\n11. Institut Penyelidikan Tingkahlaku Kesihatan\n12. Institut Penyelidikan dan Kemajuan Pertanian Malaysia\n13. Jabatan Akauntan Negara\n14. Jabatan Bomba dan Penyelamat Malaysia\n15. Jabatan Hal Ehwal Kesatuan Sekerja\n16. Jabatan Hal Ehwal Veteran\n17. Jabatan Imigresen Malaysia\n18. Jabatan Kastam Diraja Malaysia\n19. Jabatan Kebajikan Masyarakat\n20. Jabatan Kemajuan Orang Asli\n21. Jabatan Kerajaan Tempatan\n22. Jabatan Kerja Raya\n23. Jabatan Keselamatan Jalan Raya\n24. Jabatan Keselamatan dan Keselamatan Pekerjaan\n25. Jabatan Ketua Hakim Peguam\n26. Jabatan Landskap Negara\n27. Jabatan Latihan Khidmat Negara\n28. Jabatan Laut Malaysia\n29. Jabatan Pembangunan Wanita\n30. Jabatan Pendaftaran Pertubuhan Malaysia\n31. Jabatan Penerangan Malaysia\n32. Jabatan Pengangkutan Jalan\n33. Jabatan Pengurusan Sisa Pepejal Negara\n34. Jabatan Penilaian dan Perkhidmatan Negara\n35. Jabatan Penjara Malaysia\n36. Jabatan Perancangan Bandar dan Desa\n37. Jabatan Perdana Menteri Malaysia\n38. Jabatan Perhubungan Perusahaan\n39. Jabatan Perikanan Malaysia\n40. Jabatan Perkhidmatan Kuarantin dan Pemeriksaan Malaysia\n41. Jabatan Perkhidmatan Veterinar\n42. Jabatan Pertanian Malaysia\n43. Jabatan Perumahan Negara\n44. Jabatan Perumahan dan Pengurusan Strata\n45. Jabatan Sukarelawan Malaysia\n46. Jabatan Tenaga Kerja\n47. Jabatan Tenaga Kerja Manusia\n48. Khazanah Nasional\n49. Kolej Pertanian\n50. Kumpulan Wang Persaraan\n51. Kumpulan Wang Simpanan Pekerja\n52. Lembaga Hasil Dalam Negeri Malaysia\n53. Lembaga Kemajuan Ikan Malaysia\n54. Lembaga Kemajuan Pertanian Kemubu\n55. Lembaga Kemajuan Pertanian Muda\n56. Lembaga Pelabuhan Bintulu\n57. Lembaga Pelabuhan Johor\n58. Lembaga Pelabuhan Klang\n59. Lembaga Pelabuhan Kuantan\n60. Lembaga Pemasaran Pertanian Persekutuan\n61. Lembaga Pembangunan Pelaburan Malaysia\n62. Lembaga Pembiayaan Perumahan Sektor Awam\n63. Lembaga Penapisan Filem\n64. Lembaga Penduduk dan Pembangunan Keluarga Negara\n65. Lembaga Peperiksaan Malaysia\n66. Lembaga Perindustrian Nanas Malaysia\n67. Lembaga Perkhidmatan Kewangan Labuan\n68. Lembaga Pertubuhan Peladang\n69. Lembaga Promosi Kesihatan Malaysia\n70. Lembaga Totalisator Malaysia\n71. Pusat Pergigian Kanak-Kanak & Kolej Latihan Pergigian Malaysia\n\nNotebooks at URL" ]	[ 6, 493 ]	[ "passage: TAGS\n#region-us \n# Synthetic Malaysian QA\n\nGenerated common QA using ChatGPT3 for,\n1. Agrobank\n2. Bank Negara Malaysia\n3. Bank Perusahaan Kecil dan Sederhana Malaysia\n4. Bank Rakyat\n5. Bank Simpanan Nasional\n6. Bursa Malaysia\n7. Dewan Bahasa dan Pustaka\n8. Institut Kesihatan Umum\n9. Institut Penyelidikan Perubatan\n10. Institut Penyelidikan Sains dan Teknologi Pertahanan\n11. Institut Penyelidikan Tingkahlaku Kesihatan\n12. Institut Penyelidikan dan Kemajuan Pertanian Malaysia\n13. Jabatan Akauntan Negara\n14. Jabatan Bomba dan Penyelamat Malaysia\n15. Jabatan Hal Ehwal Kesatuan Sekerja\n16. Jabatan Hal Ehwal Veteran\n17. Jabatan Imigresen Malaysia\n18. Jabatan Kastam Diraja Malaysia\n19. Jabatan Kebajikan Masyarakat\n20. Jabatan Kemajuan Orang Asli\n21. Jabatan Kerajaan Tempatan\n22. Jabatan Kerja Raya\n23. Jabatan Keselamatan Jalan Raya\n24. Jabatan Keselamatan dan Keselamatan Pekerjaan\n25. Jabatan Ketua Hakim Peguam\n26. Jabatan Landskap Negara\n27. Jabatan Latihan Khidmat Negara\n28. Jabatan Laut Malaysia\n29. Jabatan Pembangunan Wanita\n30. Jabatan Pendaftaran Pertubuhan Malaysia\n31. Jabatan Penerangan Malaysia\n32. Jabatan Pengangkutan Jalan\n33. Jabatan Pengurusan Sisa Pepejal Negara\n34. Jabatan Penilaian dan Perkhidmatan Negara\n35. Jabatan Penjara Malaysia\n36. Jabatan Perancangan Bandar dan Desa\n37. Jabatan Perdana Menteri Malaysia\n38. Jabatan Perhubungan Perusahaan\n39. Jabatan Perikanan Malaysia\n40. Jabatan Perkhidmatan Kuarantin dan Pemeriksaan Malaysia\n41. Jabatan Perkhidmatan Veterinar\n42. Jabatan Pertanian Malaysia\n43. Jabatan Perumahan Negara\n44. Jabatan Perumahan dan Pengurusan Strata\n45. Jabatan Sukarelawan Malaysia\n46. Jabatan Tenaga Kerja\n47. Jabatan Tenaga Kerja Manusia\n48. Khazanah Nasional\n49. Kolej Pertanian\n50. Kumpulan Wang Persaraan\n51. Kumpulan Wang Simpanan Pekerja\n52. Lembaga Hasil Dalam Negeri Malaysia\n53. Lembaga Kemajuan Ikan Malaysia\n54. Lembaga Kemajuan Pertanian Kemubu\n55. Lembaga Kemajuan Pertanian Muda\n56. Lembaga Pelabuhan Bintulu\n57. Lembaga Pelabuhan Johor\n58. Lembaga Pelabuhan Klang\n59. Lembaga Pelabuhan Kuantan\n60. Lembaga Pemasaran Pertanian Persekutuan\n61. Lembaga Pembangunan Pelaburan Malaysia\n62. Lembaga Pembiayaan Perumahan Sektor Awam\n63. Lembaga Penapisan Filem\n64. Lembaga Penduduk dan Pembangunan Keluarga Negara\n65. Lembaga Peperiksaan Malaysia\n66. Lembaga Perindustrian Nanas Malaysia\n67. Lembaga Perkhidmatan Kewangan Labuan\n68. Lembaga Pertubuhan Peladang\n69. Lembaga Promosi Kesihatan Malaysia\n70. Lembaga Totalisator Malaysia\n71. Pusat Pergigian Kanak-Kanak & Kolej Latihan Pergigian Malaysia\n\nNotebooks at URL" ]
f47538209787f1ba555045f666da0145b6ed8381	# Dataset Card for "tiny_stories_packed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	P1ayer-1/tiny_stories_packed	[ "region:us" ]	2023-12-10T04:40:00+00:00	{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 2146599252.0, "num_examples": 1046101}], "download_size": 894178226, "dataset_size": 2146599252.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-10T04:44:42+00:00	[]	[]	TAGS #region-us	# Dataset Card for "tiny_stories_packed" More Information needed	[ "# Dataset Card for \"tiny_stories_packed\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"tiny_stories_packed\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"tiny_stories_packed\"\n\nMore Information needed" ]
f9c63c9d398aa41d065a272cbfd4e59cf9cc901f	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q ## Dataset Description - Homepage: - Repository: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T04:42:16.291896](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q/blob/main/results_2023-12-10T04-42-16.291896.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.78055182695593, "acc_stderr": 0.02737501256983463, "acc_norm": 0.7866450755467821, "acc_norm_stderr": 0.027870412250259477, "mc1": 0.38555691554467564, "mc1_stderr": 0.017038839010591673, "mc2": 0.5363792883186823, "mc2_stderr": 0.014951574037726555 }, "harness\|arc:challenge\|25": { "acc": 0.6220136518771331, "acc_stderr": 0.014169664520303096, "acc_norm": 0.6569965870307167, "acc_norm_stderr": 0.01387242322371816 }, "harness\|hellaswag\|10": { "acc": 0.6542521410077674, "acc_stderr": 0.004746394613384537, "acc_norm": 0.8522206731726748, "acc_norm_stderr": 0.00354155826377912 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7555555555555555, "acc_stderr": 0.03712537833614866, "acc_norm": 0.7555555555555555, "acc_norm_stderr": 0.03712537833614866 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8881578947368421, "acc_stderr": 0.025648341251693605, "acc_norm": 0.8881578947368421, "acc_norm_stderr": 0.025648341251693605 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036623, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036623 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8037735849056604, "acc_stderr": 0.024442388131100824, "acc_norm": 0.8037735849056604, "acc_norm_stderr": 0.024442388131100824 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9166666666666666, "acc_stderr": 0.023112508176051236, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.023112508176051236 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.48, "acc_stderr": 0.05021167315686779, "acc_norm": 0.48, "acc_norm_stderr": 0.05021167315686779 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.03391750322321659, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.03391750322321659 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.6078431372549019, "acc_stderr": 0.04858083574266345, "acc_norm": 0.6078431372549019, "acc_norm_stderr": 0.04858083574266345 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7957446808510639, "acc_stderr": 0.026355158413349417, "acc_norm": 0.7957446808510639, "acc_norm_stderr": 0.026355158413349417 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6228070175438597, "acc_stderr": 0.04559522141958216, "acc_norm": 0.6228070175438597, "acc_norm_stderr": 0.04559522141958216 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.8137931034482758, "acc_stderr": 0.03243946159004616, "acc_norm": 0.8137931034482758, "acc_norm_stderr": 0.03243946159004616 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.7619047619047619, "acc_stderr": 0.021935878081184763, "acc_norm": 0.7619047619047619, "acc_norm_stderr": 0.021935878081184763 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5952380952380952, "acc_stderr": 0.04390259265377562, "acc_norm": 0.5952380952380952, "acc_norm_stderr": 0.04390259265377562 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9290322580645162, "acc_stderr": 0.01460718907324613, "acc_norm": 0.9290322580645162, "acc_norm_stderr": 0.01460718907324613 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.7044334975369458, "acc_stderr": 0.032104944337514575, "acc_norm": 0.7044334975369458, "acc_norm_stderr": 0.032104944337514575 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993107, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993107 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9792746113989638, "acc_stderr": 0.010281417011909036, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.010281417011909036 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8358974358974359, "acc_stderr": 0.01877843431342371, "acc_norm": 0.8358974358974359, "acc_norm_stderr": 0.01877843431342371 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4888888888888889, "acc_stderr": 0.030478009819615823, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.030478009819615823 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8823529411764706, "acc_stderr": 0.02092847255778879, "acc_norm": 0.8823529411764706, "acc_norm_stderr": 0.02092847255778879 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5695364238410596, "acc_stderr": 0.04042809961395634, "acc_norm": 0.5695364238410596, "acc_norm_stderr": 0.04042809961395634 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9376146788990826, "acc_stderr": 0.01036940784904345, "acc_norm": 0.9376146788990826, "acc_norm_stderr": 0.01036940784904345 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6990740740740741, "acc_stderr": 0.03128039084329881, "acc_norm": 0.6990740740740741, "acc_norm_stderr": 0.03128039084329881 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.01990739979131695, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.01990739979131695 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9240506329113924, "acc_stderr": 0.01724463325106569, "acc_norm": 0.9240506329113924, "acc_norm_stderr": 0.01724463325106569 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7982062780269058, "acc_stderr": 0.02693611191280227, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.02693611191280227 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8854961832061069, "acc_stderr": 0.027927473753597446, "acc_norm": 0.8854961832061069, "acc_norm_stderr": 0.027927473753597446 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9173553719008265, "acc_stderr": 0.02513538235660422, "acc_norm": 0.9173553719008265, "acc_norm_stderr": 0.02513538235660422 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.9259259259259259, "acc_stderr": 0.025317997297209734, "acc_norm": 0.9259259259259259, "acc_norm_stderr": 0.025317997297209734 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8895705521472392, "acc_stderr": 0.024624937788941318, "acc_norm": 0.8895705521472392, "acc_norm_stderr": 0.024624937788941318 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6964285714285714, "acc_stderr": 0.04364226155841044, "acc_norm": 0.6964285714285714, "acc_norm_stderr": 0.04364226155841044 }, "harness\|hendrycksTest-management\|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881349, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881349 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9273504273504274, "acc_stderr": 0.01700436856813235, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.01700436856813235 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292853, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292853 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8294797687861272, "acc_stderr": 0.020247961569303728, "acc_norm": 0.8294797687861272, "acc_norm_stderr": 0.020247961569303728 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7642458100558659, "acc_stderr": 0.014196375686290804, "acc_norm": 0.7642458100558659, "acc_norm_stderr": 0.014196375686290804 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8431372549019608, "acc_stderr": 0.020823758837580916, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.020823758837580916 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8488745980707395, "acc_stderr": 0.02034274974442864, "acc_norm": 0.8488745980707395, "acc_norm_stderr": 0.02034274974442864 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8672839506172839, "acc_stderr": 0.018877353839571853, "acc_norm": 0.8672839506172839, "acc_norm_stderr": 0.018877353839571853 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.02812163604063989, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.02812163604063989 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.622555410691004, "acc_stderr": 0.012380680911165804, "acc_norm": 0.622555410691004, "acc_norm_stderr": 0.012380680911165804 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8272058823529411, "acc_stderr": 0.022966067585581774, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.022966067585581774 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8251633986928104, "acc_stderr": 0.015366167064780648, "acc_norm": 0.8251633986928104, "acc_norm_stderr": 0.015366167064780648 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940589, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940589 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.900497512437811, "acc_stderr": 0.021166216304659393, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.021166216304659393 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38555691554467564, "mc1_stderr": 0.017038839010591673, "mc2": 0.5363792883186823, "mc2_stderr": 0.014951574037726555 }, "harness\|winogrande\|5": { "acc": 0.8303078137332282, "acc_stderr": 0.010549542647363698 }, "harness\|gsm8k\|5": { "acc": 0.604245640636846, "acc_stderr": 0.013469823701048806 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q	[ "region:us" ]	2023-12-10T04:45:04+00:00	{"pretty_name": "Evaluation run of kyujinpy/PlatYi-34B-Llama-Q", "dataset_summary": "Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T04:42:16.291896](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q/blob/main/results_2023-12-10T04-42-16.291896.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.78055182695593,\n \"acc_stderr\": 0.02737501256983463,\n \"acc_norm\": 0.7866450755467821,\n \"acc_norm_stderr\": 0.027870412250259477,\n \"mc1\": 0.38555691554467564,\n \"mc1_stderr\": 0.017038839010591673,\n \"mc2\": 0.5363792883186823,\n \"mc2_stderr\": 0.014951574037726555\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303096,\n \"acc_norm\": 0.6569965870307167,\n \"acc_norm_stderr\": 0.01387242322371816\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6542521410077674,\n \"acc_stderr\": 0.004746394613384537,\n \"acc_norm\": 0.8522206731726748,\n \"acc_norm_stderr\": 0.00354155826377912\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7555555555555555,\n \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.7555555555555555,\n \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.025648341251693605,\n \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.025648341251693605\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8037735849056604,\n \"acc_stderr\": 0.024442388131100824,\n \"acc_norm\": 0.8037735849056604,\n \"acc_norm_stderr\": 0.024442388131100824\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9166666666666666,\n \"acc_stderr\": 0.023112508176051236,\n \"acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.023112508176051236\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349417,\n \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349417\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6228070175438597,\n \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.6228070175438597,\n \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.8137931034482758,\n \"acc_stderr\": 0.03243946159004616,\n \"acc_norm\": 0.8137931034482758,\n \"acc_norm_stderr\": 0.03243946159004616\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.7619047619047619,\n \"acc_stderr\": 0.021935878081184763,\n \"acc_norm\": 0.7619047619047619,\n \"acc_norm_stderr\": 0.021935878081184763\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5952380952380952,\n \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.5952380952380952,\n \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.9290322580645162,\n \"acc_stderr\": 0.01460718907324613,\n \"acc_norm\": 0.9290322580645162,\n \"acc_norm_stderr\": 0.01460718907324613\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.7044334975369458,\n \"acc_stderr\": 0.032104944337514575,\n \"acc_norm\": 0.7044334975369458,\n \"acc_norm_stderr\": 0.032104944337514575\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993107,\n \"acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993107\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.010281417011909036,\n \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.010281417011909036\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.8358974358974359,\n \"acc_stderr\": 0.01877843431342371,\n \"acc_norm\": 0.8358974358974359,\n \"acc_norm_stderr\": 0.01877843431342371\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.4888888888888889,\n \"acc_stderr\": 0.030478009819615823,\n \"acc_norm\": 0.4888888888888889,\n \"acc_norm_stderr\": 0.030478009819615823\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.8823529411764706,\n \"acc_stderr\": 0.02092847255778879,\n \"acc_norm\": 0.8823529411764706,\n \"acc_norm_stderr\": 0.02092847255778879\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.5695364238410596,\n \"acc_stderr\": 0.04042809961395634,\n \"acc_norm\": 0.5695364238410596,\n \"acc_norm_stderr\": 0.04042809961395634\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9376146788990826,\n \"acc_stderr\": 0.01036940784904345,\n \"acc_norm\": 0.9376146788990826,\n \"acc_norm_stderr\": 0.01036940784904345\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6990740740740741,\n \"acc_stderr\": 0.03128039084329881,\n \"acc_norm\": 0.6990740740740741,\n \"acc_norm_stderr\": 0.03128039084329881\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131695,\n \"acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131695\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.9240506329113924,\n \"acc_stderr\": 0.01724463325106569,\n \"acc_norm\": 0.9240506329113924,\n \"acc_norm_stderr\": 0.01724463325106569\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7982062780269058,\n \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8854961832061069,\n \"acc_stderr\": 0.027927473753597446,\n \"acc_norm\": 0.8854961832061069,\n \"acc_norm_stderr\": 0.027927473753597446\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.9173553719008265,\n \"acc_stderr\": 0.02513538235660422,\n \"acc_norm\": 0.9173553719008265,\n \"acc_norm_stderr\": 0.02513538235660422\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.9259259259259259,\n \"acc_stderr\": 0.025317997297209734,\n \"acc_norm\": 0.9259259259259259,\n \"acc_norm_stderr\": 0.025317997297209734\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8895705521472392,\n \"acc_stderr\": 0.024624937788941318,\n \"acc_norm\": 0.8895705521472392,\n \"acc_norm_stderr\": 0.024624937788941318\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6964285714285714,\n \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.6964285714285714,\n \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881349,\n \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881349\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n \"acc_stderr\": 0.01700436856813235,\n \"acc_norm\": 0.9273504273504274,\n \"acc_norm_stderr\": 0.01700436856813235\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9067688378033205,\n \"acc_stderr\": 0.010397417087292853,\n \"acc_norm\": 0.9067688378033205,\n \"acc_norm_stderr\": 0.010397417087292853\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.8294797687861272,\n \"acc_stderr\": 0.020247961569303728,\n \"acc_norm\": 0.8294797687861272,\n \"acc_norm_stderr\": 0.020247961569303728\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7642458100558659,\n \"acc_stderr\": 0.014196375686290804,\n \"acc_norm\": 0.7642458100558659,\n \"acc_norm_stderr\": 0.014196375686290804\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.020823758837580916,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.020823758837580916\n },\n 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0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.38555691554467564,\n \"mc1_stderr\": 0.017038839010591673,\n \"mc2\": 0.5363792883186823,\n \"mc2_stderr\": 0.014951574037726555\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8303078137332282,\n \"acc_stderr\": 0.010549542647363698\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.604245640636846,\n \"acc_stderr\": 0.013469823701048806\n }\n}\n```", "repo_url": "https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q", "leaderboard_url": 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T04-42-16.291896.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T04-42-16.291896.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T04_42_16.291896", "path": ["/details_harness\|winogrande\|5_2023-12-10T04-42-16.291896.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T04-42-16.291896.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T04_42_16.291896", "path": ["results_2023-12-10T04-42-16.291896.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T04-42-16.291896.parquet"]}]}]}	2023-12-10T04:45:47+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T04:42:16.291896(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T04:42:16.291896(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T04:42:16.291896(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 24, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T04:42:16.291896(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
a7a1a2eefd6bef1a9f99cf12c85e701cdb1baa98	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200K-Q ## Dataset Description - Homepage: - Repository: https://huggingface.co/kyujinpy/PlatYi-34B-200K-Q - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-200K-Q](https://huggingface.co/kyujinpy/PlatYi-34B-200K-Q) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200K-Q", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T05:34:24.325158](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200K-Q/blob/main/results_2023-12-10T05-34-24.325158.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7400651755080421, "acc_stderr": 0.02871860714656746, "acc_norm": 0.7513652661162374, "acc_norm_stderr": 0.029282495673523156, "mc1": 0.32068543451652387, "mc1_stderr": 0.016339170373280906, "mc2": 0.44207231913277784, "mc2_stderr": 0.015063393630524507 }, "harness\|arc:challenge\|25": { "acc": 0.6023890784982935, "acc_stderr": 0.014301752223279542, "acc_norm": 0.6390784982935154, "acc_norm_stderr": 0.014034761386175452 }, "harness\|hellaswag\|10": { "acc": 0.6336387173869747, "acc_stderr": 0.00480825126968244, "acc_norm": 0.8351921927902808, "acc_norm_stderr": 0.0037024876621269487 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8355263157894737, "acc_stderr": 0.030167533468632726, "acc_norm": 0.8355263157894737, "acc_norm_stderr": 0.030167533468632726 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372274, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372274 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237101, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237101 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.033450369167889904, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.033450369167889904 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5196078431372549, "acc_stderr": 0.04971358884367406, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.04971358884367406 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7957446808510639, "acc_stderr": 0.026355158413349417, "acc_norm": 0.7957446808510639, "acc_norm_stderr": 0.026355158413349417 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.631578947368421, "acc_stderr": 0.04537815354939391, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.04537815354939391 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7241379310344828, "acc_stderr": 0.03724563619774631, "acc_norm": 0.7241379310344828, "acc_norm_stderr": 0.03724563619774631 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6693121693121693, "acc_stderr": 0.02422996529842509, "acc_norm": 0.6693121693121693, "acc_norm_stderr": 0.02422996529842509 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.896774193548387, "acc_stderr": 0.017308381281034495, "acc_norm": 0.896774193548387, "acc_norm_stderr": 0.017308381281034495 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6896551724137931, "acc_stderr": 0.03255086769970103, "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03255086769970103 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8787878787878788, "acc_stderr": 0.02548549837334323, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.02548549837334323 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9040404040404041, "acc_stderr": 0.020984808610047926, "acc_norm": 0.9040404040404041, "acc_norm_stderr": 0.020984808610047926 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9533678756476683, "acc_stderr": 0.015216761819262584, "acc_norm": 0.9533678756476683, "acc_norm_stderr": 0.015216761819262584 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.764102564102564, "acc_stderr": 0.02152596540740873, "acc_norm": 0.764102564102564, "acc_norm_stderr": 0.02152596540740873 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.42962962962962964, "acc_stderr": 0.030182099804387262, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.030182099804387262 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.819327731092437, "acc_stderr": 0.02499196496660077, "acc_norm": 0.819327731092437, "acc_norm_stderr": 0.02499196496660077 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4370860927152318, "acc_stderr": 0.04050035722230636, "acc_norm": 0.4370860927152318, "acc_norm_stderr": 0.04050035722230636 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.926605504587156, "acc_stderr": 0.011180976446357573, "acc_norm": 0.926605504587156, "acc_norm_stderr": 0.011180976446357573 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6435185185185185, "acc_stderr": 0.032664783315272714, "acc_norm": 0.6435185185185185, "acc_norm_stderr": 0.032664783315272714 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9264705882352942, "acc_stderr": 0.018318855850089674, "acc_norm": 0.9264705882352942, "acc_norm_stderr": 0.018318855850089674 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8945147679324894, "acc_stderr": 0.019995560723758528, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.019995560723758528 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8396946564885496, "acc_stderr": 0.03217829420744631, "acc_norm": 0.8396946564885496, "acc_norm_stderr": 0.03217829420744631 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540627, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540627 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.03247224389917947, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.03247224389917947 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8466257668711656, "acc_stderr": 0.0283116014414386, "acc_norm": 0.8466257668711656, "acc_norm_stderr": 0.0283116014414386 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5982142857142857, "acc_stderr": 0.04653333146973647, "acc_norm": 0.5982142857142857, "acc_norm_stderr": 0.04653333146973647 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8737864077669902, "acc_stderr": 0.032881802788086285, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.032881802788086285 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9444444444444444, "acc_stderr": 0.01500631280644693, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.01500631280644693 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.85, "acc_stderr": 0.035887028128263714, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263714 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8876117496807152, "acc_stderr": 0.011294541351216554, "acc_norm": 0.8876117496807152, "acc_norm_stderr": 0.011294541351216554 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8092485549132948, "acc_stderr": 0.02115267696657527, "acc_norm": 0.8092485549132948, "acc_norm_stderr": 0.02115267696657527 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.582122905027933, "acc_stderr": 0.016495400635820084, "acc_norm": 0.582122905027933, "acc_norm_stderr": 0.016495400635820084 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8366013071895425, "acc_stderr": 0.021170623011213502, "acc_norm": 0.8366013071895425, "acc_norm_stderr": 0.021170623011213502 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.819935691318328, "acc_stderr": 0.02182342285774494, "acc_norm": 0.819935691318328, "acc_norm_stderr": 0.02182342285774494 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8364197530864198, "acc_stderr": 0.020581466138257135, "acc_norm": 0.8364197530864198, "acc_norm_stderr": 0.020581466138257135 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6063829787234043, "acc_stderr": 0.029144544781596157, "acc_norm": 0.6063829787234043, "acc_norm_stderr": 0.029144544781596157 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6003911342894394, "acc_stderr": 0.012510181636960672, "acc_norm": 0.6003911342894394, "acc_norm_stderr": 0.012510181636960672 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.023157468308559352, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.023157468308559352 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.803921568627451, "acc_stderr": 0.016062056421968646, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.016062056421968646 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7545454545454545, "acc_stderr": 0.041220665028782855, "acc_norm": 0.7545454545454545, "acc_norm_stderr": 0.041220665028782855 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8122448979591836, "acc_stderr": 0.02500025603954621, "acc_norm": 0.8122448979591836, "acc_norm_stderr": 0.02500025603954621 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8656716417910447, "acc_stderr": 0.024112678240900798, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.024112678240900798 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8830409356725146, "acc_stderr": 0.024648068961366152, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.024648068961366152 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.32068543451652387, "mc1_stderr": 0.016339170373280906, "mc2": 0.44207231913277784, "mc2_stderr": 0.015063393630524507 }, "harness\|winogrande\|5": { "acc": 0.8105761641673244, "acc_stderr": 0.011012790432989247 }, "harness\|gsm8k\|5": { "acc": 0.24109173616376042, "acc_stderr": 0.011782246325099723 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200K-Q	[ "region:us" ]	2023-12-10T05:37:13+00:00	{"pretty_name": "Evaluation run of kyujinpy/PlatYi-34B-200K-Q", "dataset_summary": "Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-200K-Q](https://huggingface.co/kyujinpy/PlatYi-34B-200K-Q) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200K-Q\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T05:34:24.325158](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200K-Q/blob/main/results_2023-12-10T05-34-24.325158.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7400651755080421,\n \"acc_stderr\": 0.02871860714656746,\n \"acc_norm\": 0.7513652661162374,\n \"acc_norm_stderr\": 0.029282495673523156,\n \"mc1\": 0.32068543451652387,\n \"mc1_stderr\": 0.016339170373280906,\n \"mc2\": 0.44207231913277784,\n \"mc2_stderr\": 0.015063393630524507\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6023890784982935,\n \"acc_stderr\": 0.014301752223279542,\n \"acc_norm\": 0.6390784982935154,\n \"acc_norm_stderr\": 0.014034761386175452\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6336387173869747,\n \"acc_stderr\": 0.00480825126968244,\n \"acc_norm\": 0.8351921927902808,\n \"acc_norm_stderr\": 0.0037024876621269487\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6888888888888889,\n \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8355263157894737,\n \"acc_stderr\": 0.030167533468632726,\n \"acc_norm\": 0.8355263157894737,\n \"acc_norm_stderr\": 0.030167533468632726\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372274,\n \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372274\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9097222222222222,\n \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.033450369167889904,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.033450369167889904\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349417,\n \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349417\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.04537815354939391,\n \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.04537815354939391\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7241379310344828,\n \"acc_stderr\": 0.03724563619774631,\n \"acc_norm\": 0.7241379310344828,\n \"acc_norm_stderr\": 0.03724563619774631\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6693121693121693,\n \"acc_stderr\": 0.02422996529842509,\n \"acc_norm\": 0.6693121693121693,\n \"acc_norm_stderr\": 0.02422996529842509\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.48412698412698413,\n \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.896774193548387,\n \"acc_stderr\": 0.017308381281034495,\n \"acc_norm\": 0.896774193548387,\n \"acc_norm_stderr\": 0.017308381281034495\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6896551724137931,\n \"acc_stderr\": 0.03255086769970103,\n \"acc_norm\": 0.6896551724137931,\n \"acc_norm_stderr\": 0.03255086769970103\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8787878787878788,\n \"acc_stderr\": 0.02548549837334323,\n \"acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.02548549837334323\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9040404040404041,\n \"acc_stderr\": 0.020984808610047926,\n \"acc_norm\": 0.9040404040404041,\n \"acc_norm_stderr\": 0.020984808610047926\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9533678756476683,\n \"acc_stderr\": 0.015216761819262584,\n \"acc_norm\": 0.9533678756476683,\n \"acc_norm_stderr\": 0.015216761819262584\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.764102564102564,\n \"acc_stderr\": 0.02152596540740873,\n \"acc_norm\": 0.764102564102564,\n \"acc_norm_stderr\": 0.02152596540740873\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.030182099804387262,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.030182099804387262\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.819327731092437,\n \"acc_stderr\": 0.02499196496660077,\n \"acc_norm\": 0.819327731092437,\n \"acc_norm_stderr\": 0.02499196496660077\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4370860927152318,\n \"acc_stderr\": 0.04050035722230636,\n \"acc_norm\": 0.4370860927152318,\n \"acc_norm_stderr\": 0.04050035722230636\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.926605504587156,\n \"acc_stderr\": 0.011180976446357573,\n \"acc_norm\": 0.926605504587156,\n \"acc_norm_stderr\": 0.011180976446357573\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6435185185185185,\n \"acc_stderr\": 0.032664783315272714,\n \"acc_norm\": 0.6435185185185185,\n \"acc_norm_stderr\": 0.032664783315272714\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089674,\n \"acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089674\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8945147679324894,\n \"acc_stderr\": 0.019995560723758528,\n \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.019995560723758528\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8026905829596412,\n \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744631,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744631\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540627,\n \"acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540627\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.03247224389917947,\n \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.03247224389917947\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8466257668711656,\n \"acc_stderr\": 0.0283116014414386,\n \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.0283116014414386\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5982142857142857,\n \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.032881802788086285,\n \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.032881802788086285\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9444444444444444,\n \"acc_stderr\": 0.01500631280644693,\n \"acc_norm\": 0.9444444444444444,\n \"acc_norm_stderr\": 0.01500631280644693\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263714,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263714\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8876117496807152,\n \"acc_stderr\": 0.011294541351216554,\n \"acc_norm\": 0.8876117496807152,\n \"acc_norm_stderr\": 0.011294541351216554\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.02115267696657527,\n \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.02115267696657527\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.582122905027933,\n \"acc_stderr\": 0.016495400635820084,\n \"acc_norm\": 0.582122905027933,\n \"acc_norm_stderr\": 0.016495400635820084\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.8366013071895425,\n \"acc_stderr\": 0.021170623011213502,\n \"acc_norm\": 0.8366013071895425,\n \"acc_norm_stderr\": 0.021170623011213502\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.819935691318328,\n \"acc_stderr\": 0.02182342285774494,\n \"acc_norm\": 0.819935691318328,\n \"acc_norm_stderr\": 0.02182342285774494\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.020581466138257135,\n \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.020581466138257135\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.6063829787234043,\n \"acc_stderr\": 0.029144544781596157,\n \"acc_norm\": 0.6063829787234043,\n \"acc_norm_stderr\": 0.029144544781596157\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6003911342894394,\n \"acc_stderr\": 0.012510181636960672,\n \"acc_norm\": 0.6003911342894394,\n \"acc_norm_stderr\": 0.012510181636960672\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.023157468308559352,\n \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.023157468308559352\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.016062056421968646,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.016062056421968646\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7545454545454545,\n \"acc_stderr\": 0.041220665028782855,\n \"acc_norm\": 0.7545454545454545,\n \"acc_norm_stderr\": 0.041220665028782855\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8656716417910447,\n \"acc_stderr\": 0.024112678240900798,\n \"acc_norm\": 0.8656716417910447,\n \"acc_norm_stderr\": 0.024112678240900798\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.32068543451652387,\n \"mc1_stderr\": 0.016339170373280906,\n \"mc2\": 0.44207231913277784,\n \"mc2_stderr\": 0.015063393630524507\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8105761641673244,\n \"acc_stderr\": 0.011012790432989247\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.24109173616376042,\n \"acc_stderr\": 0.011782246325099723\n }\n}\n```", "repo_url": "https://huggingface.co/kyujinpy/PlatYi-34B-200K-Q", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T05_34_24.325158", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T05-34-24.325158.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T05-34-24.325158.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T05_34_24.325158", "path": ["/details_harness\|gsm8k\|5_2023-12-10T05-34-24.325158.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T05-34-24.325158.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T05_34_24.325158", "path": ["/details_harness\|hellaswag\|10_2023-12-10T05-34-24.325158.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T05-34-24.325158.parquet"]}]}, {"config_name": 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kyujinpy/PlatYi-34B-200K-Q on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T05:34:24.325158(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200K-Q", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200K-Q on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:34:24.325158(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200K-Q", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200K-Q on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:34:24.325158(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200K-Q## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200K-Q on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T05:34:24.325158(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
8c843804cfdb78801edb2e757a44080ba5b77191	# Dataset Card for Evaluation run of rwitz/go-bruins-v2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/rwitz/go-bruins-v2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_rwitz__go-bruins-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T05:42:16.717744](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins-v2/blob/main/results_2023-12-10T05-42-16.717744.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6521685007083396, "acc_stderr": 0.03205721368340006, "acc_norm": 0.6521344188001463, "acc_norm_stderr": 0.032717447545898726, "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195974, "mc2": 0.5970340702765861, "mc2_stderr": 0.015540536389561436 }, "harness\|arc:challenge\|25": { "acc": 0.6697952218430034, "acc_stderr": 0.013743085603760424, "acc_norm": 0.6979522184300341, "acc_norm_stderr": 0.01341751914471641 }, "harness\|hellaswag\|10": { "acc": 0.6937860983867755, "acc_stderr": 0.004599776866717491, "acc_norm": 0.8705437163911571, "acc_norm_stderr": 0.003350181812941604 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.037385206761196686, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.037385206761196686 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7245283018867924, "acc_stderr": 0.027495663683724057, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.027495663683724057 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6589595375722543, "acc_stderr": 0.036146654241808254, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.036146654241808254 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6, "acc_stderr": 0.03202563076101735, "acc_norm": 0.6, "acc_norm_stderr": 0.03202563076101735 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42592592592592593, "acc_stderr": 0.025467149045469553, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.025467149045469553 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5158730158730159, "acc_stderr": 0.044698818540726076, "acc_norm": 0.5158730158730159, "acc_norm_stderr": 0.044698818540726076 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586818, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586818 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.023854795680971128, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.023854795680971128 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131154, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131154 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8186274509803921, "acc_stderr": 0.027044621719474082, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.027044621719474082 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8143459915611815, "acc_stderr": 0.025310495376944863, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.025310495376944863 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.036412970813137276, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.036412970813137276 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.0398913985953177, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.0398913985953177 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993457, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993457 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508287, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508287 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4324022346368715, "acc_stderr": 0.016568971233548606, "acc_norm": 0.4324022346368715, "acc_norm_stderr": 0.016568971233548606 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826524, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826524 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6816720257234726, "acc_stderr": 0.02645722506781103, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.02645722506781103 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4634941329856584, "acc_stderr": 0.012736153390214961, "acc_norm": 0.4634941329856584, "acc_norm_stderr": 0.012736153390214961 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6781045751633987, "acc_stderr": 0.01890101532209309, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.01890101532209309 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.02853556033712844, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712844 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195974, "mc2": 0.5970340702765861, "mc2_stderr": 0.015540536389561436 }, "harness\|winogrande\|5": { "acc": 0.8145224940805051, "acc_stderr": 0.010923965303140505 }, "harness\|gsm8k\|5": { "acc": 0.6967399545109931, "acc_stderr": 0.0126615026634187 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_rwitz__go-bruins-v2	[ "region:us" ]	2023-12-10T05:39:00+00:00	{"pretty_name": "Evaluation run of rwitz/go-bruins-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_rwitz__go-bruins-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T05:42:16.717744](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins-v2/blob/main/results_2023-12-10T05-42-16.717744.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6521685007083396,\n \"acc_stderr\": 0.03205721368340006,\n \"acc_norm\": 0.6521344188001463,\n \"acc_norm_stderr\": 0.032717447545898726,\n \"mc1\": 0.4369645042839657,\n \"mc1_stderr\": 0.017363844503195974,\n \"mc2\": 0.5970340702765861,\n \"mc2_stderr\": 0.015540536389561436\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6697952218430034,\n \"acc_stderr\": 0.013743085603760424,\n \"acc_norm\": 0.6979522184300341,\n \"acc_norm_stderr\": 0.01341751914471641\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6937860983867755,\n \"acc_stderr\": 0.004599776866717491,\n \"acc_norm\": 0.8705437163911571,\n \"acc_norm_stderr\": 0.003350181812941604\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724057,\n \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724057\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n 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["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["/details_harness\|winogrande\|5_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["/details_harness\|winogrande\|5_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2023-12-10T05-42-16.717744.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T05_36_09.275219", "path": ["results_2023-12-10T05-36-09.275219.parquet"]}, {"split": "2023_12_10T05_42_16.717744", "path": ["results_2023-12-10T05-42-16.717744.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T05-42-16.717744.parquet"]}]}]}	2023-12-10T05:45:57+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of rwitz/go-bruins-v2 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model rwitz/go-bruins-v2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T05:42:16.717744(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of rwitz/go-bruins-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model rwitz/go-bruins-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:42:16.717744(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of rwitz/go-bruins-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model rwitz/go-bruins-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:42:16.717744(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 19, 31, 168, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of rwitz/go-bruins-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model rwitz/go-bruins-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T05:42:16.717744(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
691e13d924106d57904ab820eac91361a27d12cc	# Dataset Card for "hf-stack-zyx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	update0909/hf-stack-zyx	[ "region:us" ]	2023-12-10T05:47:53+00:00	{"dataset_info": {"features": [{"name": "repo_id", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 114334795, "num_examples": 7212}], "download_size": 38900746, "dataset_size": 114334795}}	2023-12-10T05:56:32+00:00	[]	[]	TAGS #region-us	# Dataset Card for "hf-stack-zyx" More Information needed	[ "# Dataset Card for \"hf-stack-zyx\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"hf-stack-zyx\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"hf-stack-zyx\"\n\nMore Information needed" ]
0f1d6c355f1e6e2bf8c7ffeeb532ecab250c3ac9	# Dataset Card for "MarcBotClips" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	GeneralRincewind/MarcBotClips	[ "region:us" ]	2023-12-10T05:49:18+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 21226643625.208, "num_examples": 12064}], "download_size": 18110074182, "dataset_size": 21226643625.208}}	2023-12-10T06:03:24+00:00	[]	[]	TAGS #region-us	# Dataset Card for "MarcBotClips" More Information needed	[ "# Dataset Card for \"MarcBotClips\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"MarcBotClips\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"MarcBotClips\"\n\nMore Information needed" ]
b5bf143fe7ff42131d1cbe78fad4ce558cd1fd51	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat ## Dataset Description - Homepage: - Repository: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T05:55:07.023442](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat/blob/main/results_2023-12-10T05-55-07.023442.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7741514926490987, "acc_stderr": 0.027646135380835733, "acc_norm": 0.7828326159595959, "acc_norm_stderr": 0.02814394317924737, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5362104216200869, "mc2_stderr": 0.01504184962981019 }, "harness\|arc:challenge\|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042194, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness\|hellaswag\|10": { "acc": 0.6533559051981677, "acc_stderr": 0.004749286071559569, "acc_norm": 0.8525194184425413, "acc_norm_stderr": 0.003538596773704832 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7555555555555555, "acc_stderr": 0.03712537833614866, "acc_norm": 0.7555555555555555, "acc_norm_stderr": 0.03712537833614866 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474938, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474938 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372277, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372277 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9027777777777778, "acc_stderr": 0.024774516250440182, "acc_norm": 0.9027777777777778, "acc_norm_stderr": 0.024774516250440182 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956913, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956913 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.03391750322321659, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.03391750322321659 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.04951218252396262, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.04951218252396262 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7872340425531915, "acc_stderr": 0.02675439134803976, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.02675439134803976 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7724137931034483, "acc_stderr": 0.03493950380131184, "acc_norm": 0.7724137931034483, "acc_norm_stderr": 0.03493950380131184 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.753968253968254, "acc_stderr": 0.022182037202948365, "acc_norm": 0.753968253968254, "acc_norm_stderr": 0.022182037202948365 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.6031746031746031, "acc_stderr": 0.043758884927270585, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.043758884927270585 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9258064516129032, "acc_stderr": 0.01490952930054621, "acc_norm": 0.9258064516129032, "acc_norm_stderr": 0.01490952930054621 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.0188526702349931, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.0188526702349931 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.823076923076923, "acc_stderr": 0.01934807017439698, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.01934807017439698 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4925925925925926, "acc_stderr": 0.0304821923951915, "acc_norm": 0.4925925925925926, "acc_norm_stderr": 0.0304821923951915 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8697478991596639, "acc_stderr": 0.02186325849485212, "acc_norm": 0.8697478991596639, "acc_norm_stderr": 0.02186325849485212 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5496688741721855, "acc_stderr": 0.04062290018683775, "acc_norm": 0.5496688741721855, "acc_norm_stderr": 0.04062290018683775 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9302752293577982, "acc_stderr": 0.010919426411848607, "acc_norm": 0.9302752293577982, "acc_norm_stderr": 0.010919426411848607 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.0305467452649532, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.0305467452649532 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658935, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658935 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.919831223628692, "acc_stderr": 0.017676679991891632, "acc_norm": 0.919831223628692, "acc_norm_stderr": 0.017676679991891632 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.03088466108951538, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.03088466108951538 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9338842975206612, "acc_stderr": 0.022683403691723312, "acc_norm": 0.9338842975206612, "acc_norm_stderr": 0.022683403691723312 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665167, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665167 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6339285714285714, "acc_stderr": 0.04572372358737431, "acc_norm": 0.6339285714285714, "acc_norm_stderr": 0.04572372358737431 }, "harness\|hendrycksTest-management\|5": { "acc": 0.883495145631068, "acc_stderr": 0.031766839486404054, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.031766839486404054 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9487179487179487, "acc_stderr": 0.014450181176872736, "acc_norm": 0.9487179487179487, "acc_norm_stderr": 0.014450181176872736 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9080459770114943, "acc_stderr": 0.010333225570778518, "acc_norm": 0.9080459770114943, "acc_norm_stderr": 0.010333225570778518 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8352601156069365, "acc_stderr": 0.019971040982442265, "acc_norm": 0.8352601156069365, "acc_norm_stderr": 0.019971040982442265 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.788826815642458, "acc_stderr": 0.013650276794312199, "acc_norm": 0.788826815642458, "acc_norm_stderr": 0.013650276794312199 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8660130718954249, "acc_stderr": 0.019504890618464815, "acc_norm": 0.8660130718954249, "acc_norm_stderr": 0.019504890618464815 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8456591639871383, "acc_stderr": 0.020519050342084726, "acc_norm": 0.8456591639871383, "acc_norm_stderr": 0.020519050342084726 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6631205673758865, "acc_stderr": 0.02819553487396673, "acc_norm": 0.6631205673758865, "acc_norm_stderr": 0.02819553487396673 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6186440677966102, "acc_stderr": 0.01240550940188812, "acc_norm": 0.6186440677966102, "acc_norm_stderr": 0.01240550940188812 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8272058823529411, "acc_stderr": 0.022966067585581767, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.022966067585581767 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8316993464052288, "acc_stderr": 0.01513580333869338, "acc_norm": 0.8316993464052288, "acc_norm_stderr": 0.01513580333869338 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8367346938775511, "acc_stderr": 0.023661699177098615, "acc_norm": 0.8367346938775511, "acc_norm_stderr": 0.023661699177098615 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8706467661691543, "acc_stderr": 0.02372983088101853, "acc_norm": 0.8706467661691543, "acc_norm_stderr": 0.02372983088101853 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.93, "acc_stderr": 0.025643239997624294, "acc_norm": 0.93, "acc_norm_stderr": 0.025643239997624294 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015578, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015578 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5362104216200869, "mc2_stderr": 0.01504184962981019 }, "harness\|winogrande\|5": { "acc": 0.8216258879242304, "acc_stderr": 0.010759352014855944 }, "harness\|gsm8k\|5": { "acc": 0.44351781652767247, "acc_stderr": 0.013684327592606165 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat	[ "region:us" ]	2023-12-10T05:57:55+00:00	{"pretty_name": "Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat", "dataset_summary": "Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T05:55:07.023442](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat/blob/main/results_2023-12-10T05-55-07.023442.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7741514926490987,\n \"acc_stderr\": 0.027646135380835733,\n \"acc_norm\": 0.7828326159595959,\n \"acc_norm_stderr\": 0.02814394317924737,\n \"mc1\": 0.3880048959608323,\n \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5362104216200869,\n \"mc2_stderr\": 0.01504184962981019\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042194,\n \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6533559051981677,\n \"acc_stderr\": 0.004749286071559569,\n \"acc_norm\": 0.8525194184425413,\n \"acc_norm_stderr\": 0.003538596773704832\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7555555555555555,\n \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.7555555555555555,\n \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.026293995855474938,\n \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.026293995855474938\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372277,\n \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372277\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9027777777777778,\n \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.04951218252396262,\n \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.04951218252396262\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.02675439134803976,\n \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.02675439134803976\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7724137931034483,\n \"acc_stderr\": 0.03493950380131184,\n \"acc_norm\": 0.7724137931034483,\n \"acc_norm_stderr\": 0.03493950380131184\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.753968253968254,\n \"acc_stderr\": 0.022182037202948365,\n \"acc_norm\": 0.753968253968254,\n \"acc_norm_stderr\": 0.022182037202948365\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.6031746031746031,\n \"acc_stderr\": 0.043758884927270585,\n \"acc_norm\": 0.6031746031746031,\n \"acc_norm_stderr\": 0.043758884927270585\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.9258064516129032,\n \"acc_stderr\": 0.01490952930054621,\n \"acc_norm\": 0.9258064516129032,\n \"acc_norm_stderr\": 0.01490952930054621\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9242424242424242,\n \"acc_stderr\": 0.0188526702349931,\n \"acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.0188526702349931\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.01934807017439698,\n \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.01934807017439698\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.4925925925925926,\n \"acc_stderr\": 0.0304821923951915,\n \"acc_norm\": 0.4925925925925926,\n \"acc_norm_stderr\": 0.0304821923951915\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.02186325849485212,\n \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.02186325849485212\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.5496688741721855,\n \"acc_stderr\": 0.04062290018683775,\n \"acc_norm\": 0.5496688741721855,\n \"acc_norm_stderr\": 0.04062290018683775\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9302752293577982,\n \"acc_stderr\": 0.010919426411848607,\n \"acc_norm\": 0.9302752293577982,\n \"acc_norm_stderr\": 0.010919426411848607\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0305467452649532,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0305467452649532\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658935,\n \"acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658935\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.919831223628692,\n \"acc_stderr\": 0.017676679991891632,\n \"acc_norm\": 0.919831223628692,\n \"acc_norm_stderr\": 0.017676679991891632\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8026905829596412,\n \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951538,\n \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951538\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.9338842975206612,\n \"acc_stderr\": 0.022683403691723312,\n \"acc_norm\": 0.9338842975206612,\n \"acc_norm_stderr\": 0.022683403691723312\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-55-07.023442.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-55-07.023442.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T05_55_07.023442", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-55-07.023442.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-55-07.023442.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T05_55_07.023442", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-55-07.023442.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-55-07.023442.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T05_55_07.023442", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T05-55-07.023442.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T05-55-07.023442.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T05_55_07.023442", "path": ["/details_harness\|winogrande\|5_2023-12-10T05-55-07.023442.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T05-55-07.023442.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T05_55_07.023442", "path": ["results_2023-12-10T05-55-07.023442.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T05-55-07.023442.parquet"]}]}]}	2023-12-10T05:58:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q-FastChat on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T05:55:07.023442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q-FastChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:55:07.023442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q-FastChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:55:07.023442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 28, 31, 177, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-Llama-Q-FastChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T05:55:07.023442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
04decf3a90d3eb5a4930112aa574b9a908c4e141	# Dataset Card for Evaluation run of mncai/yi-34B-v2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/mncai/yi-34B-v2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [mncai/yi-34B-v2](https://huggingface.co/mncai/yi-34B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mncai__yi-34B-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T05:59:23.635398](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__yi-34B-v2/blob/main/results_2023-12-10T05-59-23.635398.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7523453787674309, "acc_stderr": 0.02848483810892476, "acc_norm": 0.756411391315877, "acc_norm_stderr": 0.029027731000189076, "mc1": 0.4173806609547124, "mc1_stderr": 0.017262891063272175, "mc2": 0.5733928094646895, "mc2_stderr": 0.01509801265375318 }, "harness\|arc:challenge\|25": { "acc": 0.6373720136518771, "acc_stderr": 0.014049106564955007, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness\|hellaswag\|10": { "acc": 0.6523600876319459, "acc_stderr": 0.004752476997887817, "acc_norm": 0.8500298745269866, "acc_norm_stderr": 0.0035631244274585126 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6962962962962963, "acc_stderr": 0.03972552884785137, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.03972552884785137 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474935, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474935 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8150943396226416, "acc_stderr": 0.02389335183446432, "acc_norm": 0.8150943396226416, "acc_norm_stderr": 0.02389335183446432 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.03414014007044036, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.03414014007044036 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5294117647058824, "acc_stderr": 0.049665709039785295, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.049665709039785295 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.774468085106383, "acc_stderr": 0.027321078417387533, "acc_norm": 0.774468085106383, "acc_norm_stderr": 0.027321078417387533 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7241379310344828, "acc_stderr": 0.03724563619774631, "acc_norm": 0.7241379310344828, "acc_norm_stderr": 0.03724563619774631 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6878306878306878, "acc_stderr": 0.023865206836972592, "acc_norm": 0.6878306878306878, "acc_norm_stderr": 0.023865206836972592 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6748768472906403, "acc_stderr": 0.032957975663112704, "acc_norm": 0.6748768472906403, "acc_norm_stderr": 0.032957975663112704 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8424242424242424, "acc_stderr": 0.028450388805284343, "acc_norm": 0.8424242424242424, "acc_norm_stderr": 0.028450388805284343 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9292929292929293, "acc_stderr": 0.01826310542019949, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019949 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8282051282051283, "acc_stderr": 0.01912490360342356, "acc_norm": 0.8282051282051283, "acc_norm_stderr": 0.01912490360342356 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3962962962962963, "acc_stderr": 0.029822619458534, "acc_norm": 0.3962962962962963, "acc_norm_stderr": 0.029822619458534 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.023005459446673964, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.023005459446673964 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.926605504587156, "acc_stderr": 0.011180976446357573, "acc_norm": 0.926605504587156, "acc_norm_stderr": 0.011180976446357573 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03214952147802749, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03214952147802749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.01990739979131695, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.01990739979131695 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8161434977578476, "acc_stderr": 0.025998379092356517, "acc_norm": 0.8161434977578476, "acc_norm_stderr": 0.025998379092356517 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9008264462809917, "acc_stderr": 0.02728524631275896, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.02728524631275896 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8796296296296297, "acc_stderr": 0.03145703854306251, "acc_norm": 0.8796296296296297, "acc_norm_stderr": 0.03145703854306251 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.852760736196319, "acc_stderr": 0.027839915278339653, "acc_norm": 0.852760736196319, "acc_norm_stderr": 0.027839915278339653 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5714285714285714, "acc_stderr": 0.04697113923010213, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.04697113923010213 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8446601941747572, "acc_stderr": 0.03586594738573974, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.03586594738573974 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9358974358974359, "acc_stderr": 0.016046261631673137, "acc_norm": 0.9358974358974359, "acc_norm_stderr": 0.016046261631673137 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9016602809706258, "acc_stderr": 0.010648356301876346, "acc_norm": 0.9016602809706258, "acc_norm_stderr": 0.010648356301876346 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8063583815028902, "acc_stderr": 0.021274230317515547, "acc_norm": 0.8063583815028902, "acc_norm_stderr": 0.021274230317515547 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7150837988826816, "acc_stderr": 0.015096222302469792, "acc_norm": 0.7150837988826816, "acc_norm_stderr": 0.015096222302469792 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8366013071895425, "acc_stderr": 0.021170623011213512, "acc_norm": 0.8366013071895425, "acc_norm_stderr": 0.021170623011213512 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8135048231511254, "acc_stderr": 0.022122439772480768, "acc_norm": 0.8135048231511254, "acc_norm_stderr": 0.022122439772480768 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8611111111111112, "acc_stderr": 0.019242526226544543, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.019242526226544543 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.624113475177305, "acc_stderr": 0.028893955412115875, "acc_norm": 0.624113475177305, "acc_norm_stderr": 0.028893955412115875 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5984354628422425, "acc_stderr": 0.01252031512014712, "acc_norm": 0.5984354628422425, "acc_norm_stderr": 0.01252031512014712 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02236867256288675, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02236867256288675 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8169934640522876, "acc_stderr": 0.015643069911273344, "acc_norm": 0.8169934640522876, "acc_norm_stderr": 0.015643069911273344 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8489795918367347, "acc_stderr": 0.022923004094736833, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.022923004094736833 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5903614457831325, "acc_stderr": 0.038284011150790206, "acc_norm": 0.5903614457831325, "acc_norm_stderr": 0.038284011150790206 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276908, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276908 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4173806609547124, "mc1_stderr": 0.017262891063272175, "mc2": 0.5733928094646895, "mc2_stderr": 0.01509801265375318 }, "harness\|winogrande\|5": { "acc": 0.8366219415943172, "acc_stderr": 0.010390695970273759 }, "harness\|gsm8k\|5": { "acc": 0.6497346474601972, "acc_stderr": 0.013140409455571286 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_mncai__yi-34B-v2	[ "region:us" ]	2023-12-10T06:02:12+00:00	{"pretty_name": "Evaluation run of mncai/yi-34B-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [mncai/yi-34B-v2](https://huggingface.co/mncai/yi-34B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mncai__yi-34B-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T05:59:23.635398](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__yi-34B-v2/blob/main/results_2023-12-10T05-59-23.635398.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7523453787674309,\n \"acc_stderr\": 0.02848483810892476,\n \"acc_norm\": 0.756411391315877,\n \"acc_norm_stderr\": 0.029027731000189076,\n \"mc1\": 0.4173806609547124,\n \"mc1_stderr\": 0.017262891063272175,\n \"mc2\": 0.5733928094646895,\n \"mc2_stderr\": 0.01509801265375318\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6373720136518771,\n \"acc_stderr\": 0.014049106564955007,\n \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6523600876319459,\n \"acc_stderr\": 0.004752476997887817,\n \"acc_norm\": 0.8500298745269866,\n \"acc_norm_stderr\": 0.0035631244274585126\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6962962962962963,\n \"acc_stderr\": 0.03972552884785137,\n \"acc_norm\": 0.6962962962962963,\n \"acc_norm_stderr\": 0.03972552884785137\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.026293995855474935,\n \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.026293995855474935\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8150943396226416,\n \"acc_stderr\": 0.02389335183446432,\n \"acc_norm\": 0.8150943396226416,\n \"acc_norm_stderr\": 0.02389335183446432\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9097222222222222,\n \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.03414014007044036,\n \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.03414014007044036\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.774468085106383,\n \"acc_stderr\": 0.027321078417387533,\n \"acc_norm\": 0.774468085106383,\n \"acc_norm_stderr\": 0.027321078417387533\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5614035087719298,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7241379310344828,\n \"acc_stderr\": 0.03724563619774631,\n \"acc_norm\": 0.7241379310344828,\n \"acc_norm_stderr\": 0.03724563619774631\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6878306878306878,\n \"acc_stderr\": 0.023865206836972592,\n \"acc_norm\": 0.6878306878306878,\n \"acc_norm_stderr\": 0.023865206836972592\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5476190476190477,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6748768472906403,\n \"acc_stderr\": 0.032957975663112704,\n \"acc_norm\": 0.6748768472906403,\n \"acc_norm_stderr\": 0.032957975663112704\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.028450388805284343,\n \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.028450388805284343\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019949,\n \"acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019949\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.8282051282051283,\n \"acc_stderr\": 0.01912490360342356,\n \"acc_norm\": 0.8282051282051283,\n \"acc_norm_stderr\": 0.01912490360342356\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3962962962962963,\n \"acc_stderr\": 0.029822619458534,\n \"acc_norm\": 0.3962962962962963,\n \"acc_norm_stderr\": 0.029822619458534\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673964,\n \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673964\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.926605504587156,\n \"acc_stderr\": 0.011180976446357573,\n \"acc_norm\": 0.926605504587156,\n \"acc_norm_stderr\": 0.011180976446357573\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03214952147802749,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03214952147802749\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131695,\n \"acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131695\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8161434977578476,\n \"acc_stderr\": 0.025998379092356517,\n \"acc_norm\": 0.8161434977578476,\n \"acc_norm_stderr\": 0.025998379092356517\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.9008264462809917,\n \"acc_stderr\": 0.02728524631275896,\n \"acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.02728524631275896\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8796296296296297,\n \"acc_stderr\": 0.03145703854306251,\n \"acc_norm\": 0.8796296296296297,\n \"acc_norm_stderr\": 0.03145703854306251\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.852760736196319,\n \"acc_stderr\": 0.027839915278339653,\n \"acc_norm\": 0.852760736196319,\n \"acc_norm_stderr\": 0.027839915278339653\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9358974358974359,\n \"acc_stderr\": 0.016046261631673137,\n \"acc_norm\": 0.9358974358974359,\n \"acc_norm_stderr\": 0.016046261631673137\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9016602809706258,\n \"acc_stderr\": 0.010648356301876346,\n \"acc_norm\": 0.9016602809706258,\n \"acc_norm_stderr\": 0.010648356301876346\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.8063583815028902,\n \"acc_stderr\": 0.021274230317515547,\n \"acc_norm\": 0.8063583815028902,\n \"acc_norm_stderr\": 0.021274230317515547\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7150837988826816,\n \"acc_stderr\": 0.015096222302469792,\n \"acc_norm\": 0.7150837988826816,\n \"acc_norm_stderr\": 0.015096222302469792\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.8366013071895425,\n \"acc_stderr\": 0.021170623011213512,\n \"acc_norm\": 0.8366013071895425,\n \"acc_norm_stderr\": 0.021170623011213512\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8135048231511254,\n \"acc_stderr\": 0.022122439772480768,\n \"acc_norm\": 0.8135048231511254,\n \"acc_norm_stderr\": 0.022122439772480768\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.8611111111111112,\n \"acc_stderr\": 0.019242526226544543,\n \"acc_norm\": 0.8611111111111112,\n \"acc_norm_stderr\": 0.019242526226544543\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.624113475177305,\n \"acc_stderr\": 0.028893955412115875,\n \"acc_norm\": 0.624113475177305,\n \"acc_norm_stderr\": 0.028893955412115875\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5984354628422425,\n \"acc_stderr\": 0.01252031512014712,\n \"acc_norm\": 0.5984354628422425,\n \"acc_norm_stderr\": 0.01252031512014712\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02236867256288675,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02236867256288675\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.8169934640522876,\n \"acc_stderr\": 0.015643069911273344,\n \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.015643069911273344\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736833,\n \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736833\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5903614457831325,\n \"acc_stderr\": 0.038284011150790206,\n \"acc_norm\": 0.5903614457831325,\n \"acc_norm_stderr\": 0.038284011150790206\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276908,\n \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276908\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4173806609547124,\n \"mc1_stderr\": 0.017262891063272175,\n \"mc2\": 0.5733928094646895,\n \"mc2_stderr\": 0.01509801265375318\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8366219415943172,\n \"acc_stderr\": 0.010390695970273759\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6497346474601972,\n \"acc_stderr\": 0.013140409455571286\n }\n}\n```", "repo_url": "https://huggingface.co/mncai/yi-34B-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T05_59_23.635398", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T05-59-23.635398.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T05-59-23.635398.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T05_59_23.635398", "path": ["/details_harness\|gsm8k\|5_2023-12-10T05-59-23.635398.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T05-59-23.635398.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T05_59_23.635398", "path": ["/details_harness\|hellaswag\|10_2023-12-10T05-59-23.635398.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T05-59-23.635398.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_10T05_59_23.635398", "path": 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the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T05:59:23.635398(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of mncai/yi-34B-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model mncai/yi-34B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:59:23.635398(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of mncai/yi-34B-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model mncai/yi-34B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T05:59:23.635398(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 19, 31, 168, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mncai/yi-34B-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model mncai/yi-34B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T05:59:23.635398(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
227f1e4394690659ed979e5524e0a9cb7cc85042	# Dataset Card for "ApolloAuto-zyx-apollo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	update0909/ApolloAuto-zyx-apollo	[ "region:us" ]	2023-12-10T06:05:43+00:00	{"dataset_info": {"features": [{"name": "repo_id", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 152781948, "num_examples": 17194}], "download_size": 46566010, "dataset_size": 152781948}}	2023-12-10T06:59:59+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ApolloAuto-zyx-apollo" More Information needed	[ "# Dataset Card for \"ApolloAuto-zyx-apollo\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ApolloAuto-zyx-apollo\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ApolloAuto-zyx-apollo\"\n\nMore Information needed" ]
c78d0cfe76cb42007625177433bbff2aa4689921	Welcome to the Furry Face Dataset! This dataset is used to fine-tune stable diffusion for human face to anthropomorphic animal face translation! This is for CS 548 at SUNY Poly	DAura951/Furry-Face-Dataset	[ "region:us" ]	2023-12-10T06:23:23+00:00	{}	2023-12-10T22:56:35+00:00	[]	[]	TAGS #region-us	Welcome to the Furry Face Dataset! This dataset is used to fine-tune stable diffusion for human face to anthropomorphic animal face translation! This is for CS 548 at SUNY Poly	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
1d3c95b41f7f87da40165fef4fa8db7896aed5a9	# Dataset Card for Evaluation run of chargoddard/servile-harpsichord-cdpo ## Dataset Description - Homepage: - Repository: https://huggingface.co/chargoddard/servile-harpsichord-cdpo - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [chargoddard/servile-harpsichord-cdpo](https://huggingface.co/chargoddard/servile-harpsichord-cdpo) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chargoddard__servile-harpsichord-cdpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T06:44:09.091422](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__servile-harpsichord-cdpo/blob/main/results_2023-12-10T06-44-09.091422.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6467821760747017, "acc_stderr": 0.032099406932013255, "acc_norm": 0.6493833410875584, "acc_norm_stderr": 0.032737739125074355, "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195978, "mc2": 0.6061030127349698, "mc2_stderr": 0.015471882890395387 }, "harness\|arc:challenge\|25": { "acc": 0.6407849829351536, "acc_stderr": 0.014020224155839157, "acc_norm": 0.6732081911262798, "acc_norm_stderr": 0.013706665975587331 }, "harness\|hellaswag\|10": { "acc": 0.6618203545110536, "acc_stderr": 0.004721231637092722, "acc_norm": 0.851822346146186, "acc_norm_stderr": 0.0035454991695580435 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926605, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926605 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880277, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880277 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3835978835978836, "acc_stderr": 0.025043757318520196, "acc_norm": 0.3835978835978836, "acc_norm_stderr": 0.025043757318520196 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083525, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34444444444444444, "acc_stderr": 0.028972648884844267, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.028972648884844267 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7016806722689075, "acc_stderr": 0.02971914287634286, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.02971914287634286 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126253, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126253 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7932489451476793, "acc_stderr": 0.026361651668389094, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.026361651668389094 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7130044843049327, "acc_stderr": 0.03036037971029195, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.03036037971029195 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507332, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507332 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.02394851290546837, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.02394851290546837 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4212290502793296, "acc_stderr": 0.0165136760311796, "acc_norm": 0.4212290502793296, "acc_norm_stderr": 0.0165136760311796 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694912, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694912 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7314814814814815, "acc_stderr": 0.024659685185967284, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.024659685185967284 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.45241199478487615, "acc_stderr": 0.012712265105889135, "acc_norm": 0.45241199478487615, "acc_norm_stderr": 0.012712265105889135 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6519607843137255, "acc_stderr": 0.019270998708223977, "acc_norm": 0.6519607843137255, "acc_norm_stderr": 0.019270998708223977 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195978, "mc2": 0.6061030127349698, "mc2_stderr": 0.015471882890395387 }, "harness\|winogrande\|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987729 }, "harness\|gsm8k\|5": { "acc": 0.5708870356330553, "acc_stderr": 0.013633369425647234 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_chargoddard__servile-harpsichord-cdpo	[ "region:us" ]	2023-12-10T06:47:01+00:00	{"pretty_name": "Evaluation run of chargoddard/servile-harpsichord-cdpo", "dataset_summary": "Dataset automatically created during the evaluation run of model [chargoddard/servile-harpsichord-cdpo](https://huggingface.co/chargoddard/servile-harpsichord-cdpo) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__servile-harpsichord-cdpo\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T06:44:09.091422](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__servile-harpsichord-cdpo/blob/main/results_2023-12-10T06-44-09.091422.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6467821760747017,\n \"acc_stderr\": 0.032099406932013255,\n \"acc_norm\": 0.6493833410875584,\n \"acc_norm_stderr\": 0.032737739125074355,\n \"mc1\": 0.4369645042839657,\n \"mc1_stderr\": 0.017363844503195978,\n \"mc2\": 0.6061030127349698,\n \"mc2_stderr\": 0.015471882890395387\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6407849829351536,\n \"acc_stderr\": 0.014020224155839157,\n \"acc_norm\": 0.6732081911262798,\n \"acc_norm_stderr\": 0.013706665975587331\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6618203545110536,\n \"acc_stderr\": 0.004721231637092722,\n \"acc_norm\": 0.851822346146186,\n \"acc_norm_stderr\": 0.0035454991695580435\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880277,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880277\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520196,\n \"acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520196\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083525,\n \"acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083525\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.02971914287634286,\n \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.02971914287634286\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126253,\n \"acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126253\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7932489451476793,\n \"acc_stderr\": 0.026361651668389094,\n \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.026361651668389094\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7130044843049327,\n \"acc_stderr\": 0.03036037971029195,\n \"acc_norm\": 0.7130044843049327,\n \"acc_norm_stderr\": 0.03036037971029195\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507332,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507332\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8314176245210728,\n \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546837,\n \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546837\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4212290502793296,\n \"acc_stderr\": 0.0165136760311796,\n \"acc_norm\": 0.4212290502793296,\n \"acc_norm_stderr\": 0.0165136760311796\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.45241199478487615,\n \"acc_stderr\": 0.012712265105889135,\n \"acc_norm\": 0.45241199478487615,\n \"acc_norm_stderr\": 0.012712265105889135\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6519607843137255,\n \"acc_stderr\": 0.019270998708223977,\n \"acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.019270998708223977\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4369645042839657,\n \"mc1_stderr\": 0.017363844503195978,\n \"mc2\": 0.6061030127349698,\n \"mc2_stderr\": 0.015471882890395387\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987729\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5708870356330553,\n \"acc_stderr\": 0.013633369425647234\n }\n}\n```", "repo_url": "https://huggingface.co/chargoddard/servile-harpsichord-cdpo", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T06_44_09.091422", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T06-44-09.091422.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T06-44-09.091422.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T06_44_09.091422", "path": ["/details_harness\|gsm8k\|5_2023-12-10T06-44-09.091422.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T06-44-09.091422.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T06_44_09.091422", "path": ["/details_harness\|hellaswag\|10_2023-12-10T06-44-09.091422.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T06-44-09.091422.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_10T06_44_09.091422", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T06-44-09.091422.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T06-44-09.091422.parquet", 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T06-44-09.091422.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T06-44-09.091422.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T06_44_09.091422", "path": ["/details_harness\|winogrande\|5_2023-12-10T06-44-09.091422.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T06-44-09.091422.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T06_44_09.091422", "path": ["results_2023-12-10T06-44-09.091422.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T06-44-09.091422.parquet"]}]}]}	2023-12-10T06:47:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of chargoddard/servile-harpsichord-cdpo ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model chargoddard/servile-harpsichord-cdpo on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T06:44:09.091422(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of chargoddard/servile-harpsichord-cdpo", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/servile-harpsichord-cdpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T06:44:09.091422(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of chargoddard/servile-harpsichord-cdpo", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/servile-harpsichord-cdpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T06:44:09.091422(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 24, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of chargoddard/servile-harpsichord-cdpo## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/servile-harpsichord-cdpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T06:44:09.091422(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
538f7a39097dcad5007ed53a94fa2eb40f53f2e7	# Dow30 Stock Prediction Dataset ## Overview Welcome to the Dow30 Stock Prediction dataset! This dataset is designed to assist in predicting stock returns for companies in the Dow Jones Industrial Average (Dow30). It includes essential information about each company, such as news from the last two weeks, basic financial data, and stock prices over the same period. ## Dataset Structure The dataset consists of the following columns: 1. prompt: Information about the company, including news from the last two weeks, basic financial data, and stock prices for the same period. The system prompt is generated using the code provided in the [FinGPT_Forecaster](https://github.com/AI4Finance-Foundation/FinGPT/blob/master/fingpt/FinGPT_Forecaster/prepare_data.ipynb) repository. 2. answer: Stock return predictions generated by ChatGPT. 3. period: Time period of the data, recorded on a weekly basis. 4. label: Indicates whether the stock is predicted to go up or down, along with the percentage change. 5. symbol: Stock symbol representing the company in the Dow Jones Industrial Average.	descartes100/Dow30_stock_prediction	[ "region:us" ]	2023-12-10T07:37:36+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "period", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "symbol", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3127735, "num_examples": 480}, {"name": "test", "num_bytes": 797367, "num_examples": 120}], "download_size": 1523163, "dataset_size": 3925102}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}]}	2023-12-10T08:04:49+00:00	[]	[]	TAGS #region-us	# Dow30 Stock Prediction Dataset ## Overview Welcome to the Dow30 Stock Prediction dataset! This dataset is designed to assist in predicting stock returns for companies in the Dow Jones Industrial Average (Dow30). It includes essential information about each company, such as news from the last two weeks, basic financial data, and stock prices over the same period. ## Dataset Structure The dataset consists of the following columns: 1. prompt: Information about the company, including news from the last two weeks, basic financial data, and stock prices for the same period. The system prompt is generated using the code provided in the FinGPT_Forecaster repository. 2. answer: Stock return predictions generated by ChatGPT. 3. period: Time period of the data, recorded on a weekly basis. 4. label: Indicates whether the stock is predicted to go up or down, along with the percentage change. 5. symbol: Stock symbol representing the company in the Dow Jones Industrial Average.	[ "# Dow30 Stock Prediction Dataset", "## Overview\n\nWelcome to the Dow30 Stock Prediction dataset! This dataset is designed to assist in predicting stock returns for companies in the Dow Jones Industrial Average (Dow30). It includes essential information about each company, such as news from the last two weeks, basic financial data, and stock prices over the same period.", "## Dataset Structure\n\nThe dataset consists of the following columns:\n\n1. prompt: Information about the company, including news from the last two weeks, basic financial data, and stock prices for the same period. The system prompt is generated using the code provided in the FinGPT_Forecaster repository.\n\n2. answer: Stock return predictions generated by ChatGPT.\n\n3. period: Time period of the data, recorded on a weekly basis.\n\n4. label: Indicates whether the stock is predicted to go up or down, along with the percentage change.\n\n5. symbol: Stock symbol representing the company in the Dow Jones Industrial Average." ]	[ "TAGS\n#region-us \n", "# Dow30 Stock Prediction Dataset", "## Overview\n\nWelcome to the Dow30 Stock Prediction dataset! This dataset is designed to assist in predicting stock returns for companies in the Dow Jones Industrial Average (Dow30). It includes essential information about each company, such as news from the last two weeks, basic financial data, and stock prices over the same period.", "## Dataset Structure\n\nThe dataset consists of the following columns:\n\n1. prompt: Information about the company, including news from the last two weeks, basic financial data, and stock prices for the same period. The system prompt is generated using the code provided in the FinGPT_Forecaster repository.\n\n2. answer: Stock return predictions generated by ChatGPT.\n\n3. period: Time period of the data, recorded on a weekly basis.\n\n4. label: Indicates whether the stock is predicted to go up or down, along with the percentage change.\n\n5. symbol: Stock symbol representing the company in the Dow Jones Industrial Average." ]	[ 6, 9, 71, 140 ]	[ "passage: TAGS\n#region-us \n# Dow30 Stock Prediction Dataset## Overview\n\nWelcome to the Dow30 Stock Prediction dataset! This dataset is designed to assist in predicting stock returns for companies in the Dow Jones Industrial Average (Dow30). It includes essential information about each company, such as news from the last two weeks, basic financial data, and stock prices over the same period.## Dataset Structure\n\nThe dataset consists of the following columns:\n\n1. prompt: Information about the company, including news from the last two weeks, basic financial data, and stock prices for the same period. The system prompt is generated using the code provided in the FinGPT_Forecaster repository.\n\n2. answer: Stock return predictions generated by ChatGPT.\n\n3. period: Time period of the data, recorded on a weekly basis.\n\n4. label: Indicates whether the stock is predicted to go up or down, along with the percentage change.\n\n5. symbol: Stock symbol representing the company in the Dow Jones Industrial Average." ]
6d01e20b1eea9507fa67911b25b3fb1cd3875f88	# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-34b-qlora-adapter ## Dataset Description - Homepage: - Repository: https://huggingface.co/KnutJaegersberg/Deacon-34b-qlora-adapter - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deacon-34b-qlora-adapter](https://huggingface.co/KnutJaegersberg/Deacon-34b-qlora-adapter) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Deacon-34b-qlora-adapter", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T07:35:32.492424](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deacon-34b-qlora-adapter/blob/main/results_2023-12-10T07-35-32.492424.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7583327240683659, "acc_stderr": 0.02818417949184259, "acc_norm": 0.7633703000884471, "acc_norm_stderr": 0.028709251183600685, "mc1": 0.40636474908200737, "mc1_stderr": 0.0171938358120939, "mc2": 0.5621149830422679, "mc2_stderr": 0.015167725368215625 }, "harness\|arc:challenge\|25": { "acc": 0.6143344709897611, "acc_stderr": 0.014224250973257179, "acc_norm": 0.6484641638225256, "acc_norm_stderr": 0.013952413699600938 }, "harness\|hellaswag\|10": { "acc": 0.655646285600478, "acc_stderr": 0.004741859753178431, "acc_norm": 0.8556064528978291, "acc_norm_stderr": 0.003507699935074239 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7481481481481481, "acc_stderr": 0.03749850709174021, "acc_norm": 0.7481481481481481, "acc_norm_stderr": 0.03749850709174021 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.9013157894736842, "acc_stderr": 0.024270227737522715, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.024270227737522715 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7924528301886793, "acc_stderr": 0.02495991802891127, "acc_norm": 0.7924528301886793, "acc_norm_stderr": 0.02495991802891127 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02628055093284808, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02628055093284808 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.03435568056047875, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.03435568056047875 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5, "acc_stderr": 0.04975185951049946, "acc_norm": 0.5, "acc_norm_stderr": 0.04975185951049946 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7659574468085106, "acc_stderr": 0.027678452578212387, "acc_norm": 0.7659574468085106, "acc_norm_stderr": 0.027678452578212387 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5526315789473685, "acc_stderr": 0.04677473004491199, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.04677473004491199 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6613756613756614, "acc_stderr": 0.02437319786798306, "acc_norm": 0.6613756613756614, "acc_norm_stderr": 0.02437319786798306 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8741935483870967, "acc_stderr": 0.018865834288029997, "acc_norm": 0.8741935483870967, "acc_norm_stderr": 0.018865834288029997 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.645320197044335, "acc_stderr": 0.03366124489051449, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.03366124489051449 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706473, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706473 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02239078763821677, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02239078763821677 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9792746113989638, "acc_stderr": 0.010281417011909042, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.010281417011909042 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7948717948717948, "acc_stderr": 0.020473233173551965, "acc_norm": 0.7948717948717948, "acc_norm_stderr": 0.020473233173551965 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.45925925925925926, "acc_stderr": 0.030384169232350825, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.030384169232350825 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707952, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707952 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5165562913907285, "acc_stderr": 0.04080244185628972, "acc_norm": 0.5165562913907285, "acc_norm_stderr": 0.04080244185628972 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9174311926605505, "acc_stderr": 0.01180036136301657, "acc_norm": 0.9174311926605505, "acc_norm_stderr": 0.01180036136301657 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6574074074074074, "acc_stderr": 0.03236585252602157, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.03236585252602157 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.019907399791316952, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.019907399791316952 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9240506329113924, "acc_stderr": 0.017244633251065702, "acc_norm": 0.9240506329113924, "acc_norm_stderr": 0.017244633251065702 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7847533632286996, "acc_stderr": 0.02758406660220827, "acc_norm": 0.7847533632286996, "acc_norm_stderr": 0.02758406660220827 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9173553719008265, "acc_stderr": 0.025135382356604227, "acc_norm": 0.9173553719008265, "acc_norm_stderr": 0.025135382356604227 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8981481481481481, "acc_stderr": 0.029239272675632748, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.029239272675632748 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6160714285714286, "acc_stderr": 0.04616143075028546, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.04616143075028546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.912621359223301, "acc_stderr": 0.027960689125970654, "acc_norm": 0.912621359223301, "acc_norm_stderr": 0.027960689125970654 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9230769230769231, "acc_stderr": 0.01745698787243619, "acc_norm": 0.9230769230769231, "acc_norm_stderr": 0.01745698787243619 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9029374201787995, "acc_stderr": 0.010586474712018302, "acc_norm": 0.9029374201787995, "acc_norm_stderr": 0.010586474712018302 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8323699421965318, "acc_stderr": 0.02011057991973484, "acc_norm": 0.8323699421965318, "acc_norm_stderr": 0.02011057991973484 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.6458100558659218, "acc_stderr": 0.01599564494729923, "acc_norm": 0.6458100558659218, "acc_norm_stderr": 0.01599564494729923 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8660130718954249, "acc_stderr": 0.019504890618464815, "acc_norm": 0.8660130718954249, "acc_norm_stderr": 0.019504890618464815 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8392282958199357, "acc_stderr": 0.020862388082391888, "acc_norm": 0.8392282958199357, "acc_norm_stderr": 0.020862388082391888 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8765432098765432, "acc_stderr": 0.018303868806891794, "acc_norm": 0.8765432098765432, "acc_norm_stderr": 0.018303868806891794 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6631205673758865, "acc_stderr": 0.02819553487396673, "acc_norm": 0.6631205673758865, "acc_norm_stderr": 0.02819553487396673 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5971316818774446, "acc_stderr": 0.012526955577118009, "acc_norm": 0.5971316818774446, "acc_norm_stderr": 0.012526955577118009 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8088235294117647, "acc_stderr": 0.02388688192244034, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.02388688192244034 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.015422512066262549, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.015422512066262549 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940589, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940589 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8448979591836735, "acc_stderr": 0.0231747988612186, "acc_norm": 0.8448979591836735, "acc_norm_stderr": 0.0231747988612186 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824636, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824636 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015578, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015578 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.40636474908200737, "mc1_stderr": 0.0171938358120939, "mc2": 0.5621149830422679, "mc2_stderr": 0.015167725368215625 }, "harness\|winogrande\|5": { "acc": 0.8310970797158642, "acc_stderr": 0.010529981411838899 }, "harness\|gsm8k\|5": { "acc": 0.6224412433661866, "acc_stderr": 0.013353150666358532 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_KnutJaegersberg__Deacon-34b-qlora-adapter	[ "region:us" ]	2023-12-10T07:38:22+00:00	{"pretty_name": "Evaluation run of KnutJaegersberg/Deacon-34b-qlora-adapter", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deacon-34b-qlora-adapter](https://huggingface.co/KnutJaegersberg/Deacon-34b-qlora-adapter) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Deacon-34b-qlora-adapter\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T07:35:32.492424](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deacon-34b-qlora-adapter/blob/main/results_2023-12-10T07-35-32.492424.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7583327240683659,\n \"acc_stderr\": 0.02818417949184259,\n \"acc_norm\": 0.7633703000884471,\n \"acc_norm_stderr\": 0.028709251183600685,\n \"mc1\": 0.40636474908200737,\n \"mc1_stderr\": 0.0171938358120939,\n \"mc2\": 0.5621149830422679,\n \"mc2_stderr\": 0.015167725368215625\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6143344709897611,\n \"acc_stderr\": 0.014224250973257179,\n \"acc_norm\": 0.6484641638225256,\n \"acc_norm_stderr\": 0.013952413699600938\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.655646285600478,\n \"acc_stderr\": 0.004741859753178431,\n \"acc_norm\": 0.8556064528978291,\n \"acc_norm_stderr\": 0.003507699935074239\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7481481481481481,\n \"acc_stderr\": 0.03749850709174021,\n \"acc_norm\": 0.7481481481481481,\n \"acc_norm_stderr\": 0.03749850709174021\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.9013157894736842,\n \"acc_stderr\": 0.024270227737522715,\n \"acc_norm\": 0.9013157894736842,\n \"acc_norm_stderr\": 0.024270227737522715\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7924528301886793,\n \"acc_stderr\": 0.02495991802891127,\n \"acc_norm\": 0.7924528301886793,\n \"acc_norm_stderr\": 0.02495991802891127\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.02628055093284808,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02628055093284808\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.03435568056047875,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.03435568056047875\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04975185951049946,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04975185951049946\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.027678452578212387,\n \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.027678452578212387\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5526315789473685,\n \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6613756613756614,\n \"acc_stderr\": 0.02437319786798306,\n \"acc_norm\": 0.6613756613756614,\n \"acc_norm_stderr\": 0.02437319786798306\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["/details_harness\|winogrande\|5_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T07-35-32.492424.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T07_35_32.492424", "path": ["results_2023-12-10T07-35-32.492424.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T07-35-32.492424.parquet"]}]}]}	2023-12-10T07:39:05+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-34b-qlora-adapter ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-34b-qlora-adapter on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T07:35:32.492424(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-34b-qlora-adapter", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-34b-qlora-adapter on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T07:35:32.492424(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-34b-qlora-adapter", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-34b-qlora-adapter on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T07:35:32.492424(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 25, 31, 174, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-34b-qlora-adapter## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-34b-qlora-adapter on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T07:35:32.492424(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
9b960801da721d25745d23b14cb24ff78398b785	# How to use ```python import tarfile from huggingface_hub import hf_hub_download hf_dataset_identifier="aisuko/ucf101-subset" filename="UCF101_subset.tar.gz" file_path=hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type="dataset") import tarfile with tarfile.open(file_path) as t: t.extractall(".") ``` # Check the folder ``` UCF101_subset/ train/ BandMarching/ video_1.mp4 video_2.mp4 ... Archery video_1.mp4 video_2.mp4 ... ... val/ BandMarching/ video_1.mp4 video_2.mp4 ... Archery video_1.mp4 video_2.mp4 ... ... test/ BandMarching/ video_1.mp4 video_2.mp4 ... Archery video_1.mp4 video_2.mp4 ... ... ```	aisuko/ucf101-subset	[ "task_categories:video-classification", "license:apache-2.0", "region:us" ]	2023-12-10T07:53:48+00:00	{"license": "apache-2.0", "task_categories": ["video-classification"]}	2023-12-10T08:16:05+00:00	[]	[]	TAGS #task_categories-video-classification #license-apache-2.0 #region-us	# How to use # Check the folder	[ "# How to use", "# Check the folder" ]	[ "TAGS\n#task_categories-video-classification #license-apache-2.0 #region-us \n", "# How to use", "# Check the folder" ]	[ 25, 4, 4 ]	[ "passage: TAGS\n#task_categories-video-classification #license-apache-2.0 #region-us \n# How to use# Check the folder" ]
ec65ab9d274e10ac0ece627a2ea600e4e84fe429	# Dataset Card for "pc9Cap" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ArasAyen/pc9Cap	[ "region:us" ]	2023-12-10T08:29:59+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 265997109.0, "num_examples": 302}], "download_size": 262523050, "dataset_size": 265997109.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-10T08:30:21+00:00	[]	[]	TAGS #region-us	# Dataset Card for "pc9Cap" More Information needed	[ "# Dataset Card for \"pc9Cap\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"pc9Cap\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"pc9Cap\"\n\nMore Information needed" ]
a54a662ed39df02d2f36019662b1e0f6116a5e76	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200k-Q-FastChat ## Dataset Description - Homepage: - Repository: https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-200k-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200k-Q-FastChat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T08:30:20.014698](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200k-Q-FastChat/blob/main/results_2023-12-10T08-30-20.014698.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7630727247628006, "acc_stderr": 0.028221206890446823, "acc_norm": 0.770488792020382, "acc_norm_stderr": 0.028732290582792492, "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897303, "mc2": 0.4838395775572536, "mc2_stderr": 0.014874467350764172 }, "harness\|arc:challenge\|25": { "acc": 0.613481228668942, "acc_stderr": 0.014230084761910471, "acc_norm": 0.6493174061433447, "acc_norm_stderr": 0.013944635930726097 }, "harness\|hellaswag\|10": { "acc": 0.6467835092611034, "acc_stderr": 0.004769924131304649, "acc_norm": 0.8445528779127663, "acc_norm_stderr": 0.003615898928269288 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7185185185185186, "acc_stderr": 0.03885004245800253, "acc_norm": 0.7185185185185186, "acc_norm_stderr": 0.03885004245800253 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8113207547169812, "acc_stderr": 0.02407999513006225, "acc_norm": 0.8113207547169812, "acc_norm_stderr": 0.02407999513006225 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.0332055644308557, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.0332055644308557 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5686274509803921, "acc_stderr": 0.04928099597287534, "acc_norm": 0.5686274509803921, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7829787234042553, "acc_stderr": 0.026947483121496228, "acc_norm": 0.7829787234042553, "acc_norm_stderr": 0.026947483121496228 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6403508771929824, "acc_stderr": 0.04514496132873633, "acc_norm": 0.6403508771929824, "acc_norm_stderr": 0.04514496132873633 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7517241379310344, "acc_stderr": 0.03600105692727771, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.03600105692727771 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.7380952380952381, "acc_stderr": 0.022644212615525218, "acc_norm": 0.7380952380952381, "acc_norm_stderr": 0.022644212615525218 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677173, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677173 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.896774193548387, "acc_stderr": 0.017308381281034527, "acc_norm": 0.896774193548387, "acc_norm_stderr": 0.017308381281034527 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6650246305418719, "acc_stderr": 0.033208527423483104, "acc_norm": 0.6650246305418719, "acc_norm_stderr": 0.033208527423483104 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865397, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865397 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9444444444444444, "acc_stderr": 0.0163199507007674, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.0163199507007674 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9637305699481865, "acc_stderr": 0.013492659751295127, "acc_norm": 0.9637305699481865, "acc_norm_stderr": 0.013492659751295127 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8153846153846154, "acc_stderr": 0.0196716324131003, "acc_norm": 0.8153846153846154, "acc_norm_stderr": 0.0196716324131003 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.43703703703703706, "acc_stderr": 0.030242862397654, "acc_norm": 0.43703703703703706, "acc_norm_stderr": 0.030242862397654 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8613445378151261, "acc_stderr": 0.02244826447683258, "acc_norm": 0.8613445378151261, "acc_norm_stderr": 0.02244826447683258 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5165562913907285, "acc_stderr": 0.04080244185628972, "acc_norm": 0.5165562913907285, "acc_norm_stderr": 0.04080244185628972 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9302752293577982, "acc_stderr": 0.010919426411848607, "acc_norm": 0.9302752293577982, "acc_norm_stderr": 0.010919426411848607 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6759259259259259, "acc_stderr": 0.03191923445686186, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.03191923445686186 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9264705882352942, "acc_stderr": 0.018318855850089678, "acc_norm": 0.9264705882352942, "acc_norm_stderr": 0.018318855850089678 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8987341772151899, "acc_stderr": 0.019637720526065498, "acc_norm": 0.8987341772151899, "acc_norm_stderr": 0.019637720526065498 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8625954198473282, "acc_stderr": 0.030194823996804475, "acc_norm": 0.8625954198473282, "acc_norm_stderr": 0.030194823996804475 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8760330578512396, "acc_stderr": 0.030083098716035216, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.030083098716035216 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.032472243899179465, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.032472243899179465 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.852760736196319, "acc_stderr": 0.027839915278339653, "acc_norm": 0.852760736196319, "acc_norm_stderr": 0.027839915278339653 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6428571428571429, "acc_stderr": 0.045479609997643757, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.045479609997643757 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8932038834951457, "acc_stderr": 0.030581088928331356, "acc_norm": 0.8932038834951457, "acc_norm_stderr": 0.030581088928331356 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253867, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253867 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9144316730523627, "acc_stderr": 0.010002965568647286, "acc_norm": 0.9144316730523627, "acc_norm_stderr": 0.010002965568647286 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.815028901734104, "acc_stderr": 0.020903975842083027, "acc_norm": 0.815028901734104, "acc_norm_stderr": 0.020903975842083027 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7262569832402235, "acc_stderr": 0.014912413096372432, "acc_norm": 0.7262569832402235, "acc_norm_stderr": 0.014912413096372432 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8627450980392157, "acc_stderr": 0.01970403918385981, "acc_norm": 0.8627450980392157, "acc_norm_stderr": 0.01970403918385981 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.842443729903537, "acc_stderr": 0.020692237273583984, "acc_norm": 0.842443729903537, "acc_norm_stderr": 0.020692237273583984 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8734567901234568, "acc_stderr": 0.018498600558790906, "acc_norm": 0.8734567901234568, "acc_norm_stderr": 0.018498600558790906 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6205673758865248, "acc_stderr": 0.028947338851614095, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.028947338851614095 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6173402868318123, "acc_stderr": 0.01241359588289327, "acc_norm": 0.6173402868318123, "acc_norm_stderr": 0.01241359588289327 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8125, "acc_stderr": 0.023709788253811766, "acc_norm": 0.8125, "acc_norm_stderr": 0.023709788253811766 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8202614379084967, "acc_stderr": 0.01553374508338279, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.01553374508338279 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.04069306319721376, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.04069306319721376 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8285714285714286, "acc_stderr": 0.024127463462650163, "acc_norm": 0.8285714285714286, "acc_norm_stderr": 0.024127463462650163 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101716, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101716 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8830409356725146, "acc_stderr": 0.024648068961366152, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.024648068961366152 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897303, "mc2": 0.4838395775572536, "mc2_stderr": 0.014874467350764172 }, "harness\|winogrande\|5": { "acc": 0.8074191002367798, "acc_stderr": 0.01108253884749189 }, "harness\|gsm8k\|5": { "acc": 0.514783927217589, "acc_stderr": 0.0137664630507876 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200k-Q-FastChat	[ "region:us" ]	2023-12-10T08:33:11+00:00	{"pretty_name": "Evaluation run of kyujinpy/PlatYi-34B-200k-Q-FastChat", "dataset_summary": "Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-200k-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200k-Q-FastChat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T08:30:20.014698](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-200k-Q-FastChat/blob/main/results_2023-12-10T08-30-20.014698.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7630727247628006,\n \"acc_stderr\": 0.028221206890446823,\n \"acc_norm\": 0.770488792020382,\n \"acc_norm_stderr\": 0.028732290582792492,\n \"mc1\": 0.34761321909424725,\n \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.4838395775572536,\n \"mc2_stderr\": 0.014874467350764172\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.613481228668942,\n \"acc_stderr\": 0.014230084761910471,\n \"acc_norm\": 0.6493174061433447,\n \"acc_norm_stderr\": 0.013944635930726097\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6467835092611034,\n \"acc_stderr\": 0.004769924131304649,\n \"acc_norm\": 0.8445528779127663,\n \"acc_norm_stderr\": 0.003615898928269288\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7185185185185186,\n \"acc_stderr\": 0.03885004245800253,\n \"acc_norm\": 0.7185185185185186,\n \"acc_norm_stderr\": 0.03885004245800253\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.02407999513006225,\n \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.02407999513006225\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8958333333333334,\n \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5686274509803921,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.5686274509803921,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.026947483121496228,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.026947483121496228\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6403508771929824,\n \"acc_stderr\": 0.04514496132873633,\n \"acc_norm\": 0.6403508771929824,\n \"acc_norm_stderr\": 0.04514496132873633\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.03600105692727771,\n \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.03600105692727771\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.7380952380952381,\n \"acc_stderr\": 0.022644212615525218,\n \"acc_norm\": 0.7380952380952381,\n \"acc_norm_stderr\": 0.022644212615525218\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5317460317460317,\n \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.5317460317460317,\n \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.896774193548387,\n \"acc_stderr\": 0.017308381281034527,\n \"acc_norm\": 0.896774193548387,\n \"acc_norm_stderr\": 0.017308381281034527\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6650246305418719,\n \"acc_stderr\": 0.033208527423483104,\n \"acc_norm\": 0.6650246305418719,\n \"acc_norm_stderr\": 0.033208527423483104\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865397,\n \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865397\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9444444444444444,\n \"acc_stderr\": 0.0163199507007674,\n \"acc_norm\": 0.9444444444444444,\n \"acc_norm_stderr\": 0.0163199507007674\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295127,\n \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295127\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.8153846153846154,\n \"acc_stderr\": 0.0196716324131003,\n \"acc_norm\": 0.8153846153846154,\n \"acc_norm_stderr\": 0.0196716324131003\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.030242862397654,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 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"latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["/details_harness\|winogrande\|5_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T08-30-20.014698.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T08_30_20.014698", "path": ["results_2023-12-10T08-30-20.014698.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T08-30-20.014698.parquet"]}]}]}	2023-12-10T08:33:59+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200k-Q-FastChat ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200k-Q-FastChat on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T08:30:20.014698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200k-Q-FastChat", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200k-Q-FastChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T08:30:20.014698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200k-Q-FastChat", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200k-Q-FastChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T08:30:20.014698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 27, 31, 176, 68, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-200k-Q-FastChat## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model kyujinpy/PlatYi-34B-200k-Q-FastChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T08:30:20.014698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
f3690806e872b686cb26933b973d57d675496fe6	# Glaive Code Assistant [Glaive Code Assistant dataset](https://huggingface.co/datasets/glaiveai/glaive-code-assistant) formatted for training assistant models with the following prompt template: ``` <s>[INST] {question} [/INST] {answer} </s> ``` Trained model can be prompted in Llama style: ``` <s>[INST] {{ user_msg }} [/INST] ```	mwitiderrick/glaive-code-assistant	[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]	2023-12-10T08:48:53+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "Glaive Code Assistant", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 210090644, "num_examples": 136109}], "download_size": 100891258, "dataset_size": 210090644}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-10T09:02:55+00:00	[]	[ "en" ]	TAGS #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us	# Glaive Code Assistant Glaive Code Assistant dataset formatted for training assistant models with the following prompt template: Trained model can be prompted in Llama style:	[ "# Glaive Code Assistant\nGlaive Code Assistant dataset formatted for training assistant models with the following prompt template: \n\n\nTrained model can be prompted in Llama style:" ]	[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n", "# Glaive Code Assistant\nGlaive Code Assistant dataset formatted for training assistant models with the following prompt template: \n\n\nTrained model can be prompted in Llama style:" ]	[ 41, 37 ]	[ "passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n# Glaive Code Assistant\nGlaive Code Assistant dataset formatted for training assistant models with the following prompt template: \n\n\nTrained model can be prompted in Llama style:" ]
413a2b0341d2d1bd3707732c4ac23593008427a4	# Dataset Card for "rapidapi-example-responses-tokenized-phi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses-tokenized-phi	[ "region:us" ]	2023-12-10T09:02:39+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 168469345.01807085, "num_examples": 45271}, {"name": "test", "num_bytes": 18722123.98192915, "num_examples": 5031}], "download_size": 65907419, "dataset_size": 187191469.0}}	2023-12-10T09:02:53+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses-tokenized-phi" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses-tokenized-phi\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses-tokenized-phi\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses-tokenized-phi\"\n\nMore Information needed" ]
0a610511454a0562db5107a3f4e0606f962a07a1	# Dataset Card for Evaluation run of ehartford/dolphin-2.2-yi-34b-200k ## Dataset Description - Homepage: - Repository: https://huggingface.co/ehartford/dolphin-2.2-yi-34b-200k - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [ehartford/dolphin-2.2-yi-34b-200k](https://huggingface.co/ehartford/dolphin-2.2-yi-34b-200k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ehartford__dolphin-2.2-yi-34b-200k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T09:19:14.695653](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.2-yi-34b-200k/blob/main/results_2023-12-10T09-19-14.695653.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5443333155719463, "acc_stderr": 0.03403073973019475, "acc_norm": 0.5545570631884628, "acc_norm_stderr": 0.034865135931915724, "mc1": 0.2839657282741738, "mc1_stderr": 0.01578537085839672, "mc2": 0.45931787186509654, "mc2_stderr": 0.0156737639267665 }, "harness\|arc:challenge\|25": { "acc": 0.3924914675767918, "acc_stderr": 0.014269634635670714, "acc_norm": 0.42150170648464164, "acc_norm_stderr": 0.014430197069326023 }, "harness\|hellaswag\|10": { "acc": 0.5135431189006174, "acc_stderr": 0.004987950663406538, "acc_norm": 0.6818362875921131, "acc_norm_stderr": 0.00464811532232878 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319875, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319875 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6180555555555556, "acc_stderr": 0.040629907841466674, "acc_norm": 0.6180555555555556, "acc_norm_stderr": 0.040629907841466674 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5202312138728323, "acc_stderr": 0.03809342081273956, "acc_norm": 0.5202312138728323, "acc_norm_stderr": 0.03809342081273956 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4978723404255319, "acc_stderr": 0.032685726586674915, "acc_norm": 0.4978723404255319, "acc_norm_stderr": 0.032685726586674915 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.45517241379310347, "acc_stderr": 0.04149886942192117, "acc_norm": 0.45517241379310347, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.024677862841332783, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.024677862841332783 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.040406101782088394, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.040406101782088394 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6806451612903226, "acc_stderr": 0.026522709674667768, "acc_norm": 0.6806451612903226, "acc_norm_stderr": 0.026522709674667768 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4088669950738916, "acc_stderr": 0.034590588158832314, "acc_norm": 0.4088669950738916, "acc_norm_stderr": 0.034590588158832314 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7151515151515152, "acc_stderr": 0.03524390844511781, "acc_norm": 0.7151515151515152, "acc_norm_stderr": 0.03524390844511781 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7474747474747475, "acc_stderr": 0.030954055470365897, "acc_norm": 0.7474747474747475, "acc_norm_stderr": 0.030954055470365897 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.772020725388601, "acc_stderr": 0.030276909945178263, "acc_norm": 0.772020725388601, "acc_norm_stderr": 0.030276909945178263 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.02529460802398648, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.02529460802398648 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.027840811495871927, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.027840811495871927 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.592436974789916, "acc_stderr": 0.03191863374478464, "acc_norm": 0.592436974789916, "acc_norm_stderr": 0.03191863374478464 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.726605504587156, "acc_stderr": 0.019109299846098295, "acc_norm": 0.726605504587156, "acc_norm_stderr": 0.019109299846098295 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.031321798030832904, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.031321798030832904 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.759493670886076, "acc_stderr": 0.027820781981149685, "acc_norm": 0.759493670886076, "acc_norm_stderr": 0.027820781981149685 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.600896860986547, "acc_stderr": 0.03286745312567961, "acc_norm": 0.600896860986547, "acc_norm_stderr": 0.03286745312567961 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6335877862595419, "acc_stderr": 0.04225875451969638, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.04225875451969638 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302871, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302871 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5648148148148148, "acc_stderr": 0.047928981709070624, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.047928981709070624 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.656441717791411, "acc_stderr": 0.03731133519673893, "acc_norm": 0.656441717791411, "acc_norm_stderr": 0.03731133519673893 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5922330097087378, "acc_stderr": 0.04865777570410769, "acc_norm": 0.5922330097087378, "acc_norm_stderr": 0.04865777570410769 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7649572649572649, "acc_stderr": 0.02777883590493543, "acc_norm": 0.7649572649572649, "acc_norm_stderr": 0.02777883590493543 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7611749680715197, "acc_stderr": 0.015246803197398687, "acc_norm": 0.7611749680715197, "acc_norm_stderr": 0.015246803197398687 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.523121387283237, "acc_stderr": 0.026890297881303125, "acc_norm": 0.523121387283237, "acc_norm_stderr": 0.026890297881303125 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.35307262569832404, "acc_stderr": 0.01598420454526857, "acc_norm": 0.35307262569832404, "acc_norm_stderr": 0.01598420454526857 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6339869281045751, "acc_stderr": 0.027582811415159628, "acc_norm": 0.6339869281045751, "acc_norm_stderr": 0.027582811415159628 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6045016077170418, "acc_stderr": 0.02777091853142784, "acc_norm": 0.6045016077170418, "acc_norm_stderr": 0.02777091853142784 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6018518518518519, "acc_stderr": 0.027237415094592477, "acc_norm": 0.6018518518518519, "acc_norm_stderr": 0.027237415094592477 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3829787234042553, "acc_stderr": 0.028999080904806178, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.028999080904806178 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4302477183833116, "acc_stderr": 0.01264536143511522, "acc_norm": 0.4302477183833116, "acc_norm_stderr": 0.01264536143511522 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5330882352941176, "acc_stderr": 0.030306257722468317, "acc_norm": 0.5330882352941176, "acc_norm_stderr": 0.030306257722468317 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5228758169934641, "acc_stderr": 0.02020665318788478, "acc_norm": 0.5228758169934641, "acc_norm_stderr": 0.02020665318788478 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661895, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661895 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6571428571428571, "acc_stderr": 0.030387262919547728, "acc_norm": 0.6571428571428571, "acc_norm_stderr": 0.030387262919547728 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7164179104477612, "acc_stderr": 0.031871875379197966, "acc_norm": 0.7164179104477612, "acc_norm_stderr": 0.031871875379197966 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.42771084337349397, "acc_stderr": 0.03851597683718533, "acc_norm": 0.42771084337349397, "acc_norm_stderr": 0.03851597683718533 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7017543859649122, "acc_stderr": 0.03508771929824565, "acc_norm": 0.7017543859649122, "acc_norm_stderr": 0.03508771929824565 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2839657282741738, "mc1_stderr": 0.01578537085839672, "mc2": 0.45931787186509654, "mc2_stderr": 0.0156737639267665 }, "harness\|winogrande\|5": { "acc": 0.6456195737963694, "acc_stderr": 0.013443314368356088 }, "harness\|gsm8k\|5": { "acc": 0.037149355572403335, "acc_stderr": 0.00520951628307378 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_ehartford__dolphin-2.2-yi-34b-200k	[ "region:us" ]	2023-12-10T09:22:04+00:00	{"pretty_name": "Evaluation run of ehartford/dolphin-2.2-yi-34b-200k", "dataset_summary": "Dataset automatically created during the evaluation run of model [ehartford/dolphin-2.2-yi-34b-200k](https://huggingface.co/ehartford/dolphin-2.2-yi-34b-200k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ehartford__dolphin-2.2-yi-34b-200k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T09:19:14.695653](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.2-yi-34b-200k/blob/main/results_2023-12-10T09-19-14.695653.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5443333155719463,\n \"acc_stderr\": 0.03403073973019475,\n \"acc_norm\": 0.5545570631884628,\n \"acc_norm_stderr\": 0.034865135931915724,\n \"mc1\": 0.2839657282741738,\n \"mc1_stderr\": 0.01578537085839672,\n \"mc2\": 0.45931787186509654,\n \"mc2_stderr\": 0.0156737639267665\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.3924914675767918,\n \"acc_stderr\": 0.014269634635670714,\n \"acc_norm\": 0.42150170648464164,\n \"acc_norm_stderr\": 0.014430197069326023\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5135431189006174,\n \"acc_stderr\": 0.004987950663406538,\n \"acc_norm\": 0.6818362875921131,\n \"acc_norm_stderr\": 0.00464811532232878\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6180555555555556,\n \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.6180555555555556,\n \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5202312138728323,\n \"acc_stderr\": 0.03809342081273956,\n \"acc_norm\": 0.5202312138728323,\n \"acc_norm_stderr\": 0.03809342081273956\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.40350877192982454,\n \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.024677862841332783,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.024677862841332783\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.040406101782088394,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.040406101782088394\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6806451612903226,\n \"acc_stderr\": 0.026522709674667768,\n \"acc_norm\": 0.6806451612903226,\n \"acc_norm_stderr\": 0.026522709674667768\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4088669950738916,\n \"acc_stderr\": 0.034590588158832314,\n \"acc_norm\": 0.4088669950738916,\n \"acc_norm_stderr\": 0.034590588158832314\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.03524390844511781,\n \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.03524390844511781\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.772020725388601,\n \"acc_stderr\": 0.030276909945178263,\n \"acc_norm\": 0.772020725388601,\n \"acc_norm_stderr\": 0.030276909945178263\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.02529460802398648,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.02529460802398648\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871927,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871927\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.592436974789916,\n \"acc_stderr\": 0.03191863374478464,\n \"acc_norm\": 0.592436974789916,\n \"acc_norm_stderr\": 0.03191863374478464\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.726605504587156,\n \"acc_stderr\": 0.019109299846098295,\n \"acc_norm\": 0.726605504587156,\n \"acc_norm_stderr\": 0.019109299846098295\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.031321798030832904,\n \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.031321798030832904\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.600896860986547,\n \"acc_stderr\": 0.03286745312567961,\n \"acc_norm\": 0.600896860986547,\n \"acc_norm_stderr\": 0.03286745312567961\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969638,\n \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969638\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.743801652892562,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5648148148148148,\n \"acc_stderr\": 0.047928981709070624,\n \"acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.047928981709070624\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.656441717791411,\n \"acc_stderr\": 0.03731133519673893,\n \"acc_norm\": 0.656441717791411,\n \"acc_norm_stderr\": 0.03731133519673893\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.5922330097087378,\n \"acc_stderr\": 0.04865777570410769,\n \"acc_norm\": 0.5922330097087378,\n \"acc_norm_stderr\": 0.04865777570410769\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7649572649572649,\n \"acc_stderr\": 0.02777883590493543,\n \"acc_norm\": 0.7649572649572649,\n \"acc_norm_stderr\": 0.02777883590493543\n },\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T09-19-14.695653.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T09-19-14.695653.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T09_19_14.695653", "path": ["/details_harness\|winogrande\|5_2023-12-10T09-19-14.695653.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T09-19-14.695653.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T09_19_14.695653", "path": ["results_2023-12-10T09-19-14.695653.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T09-19-14.695653.parquet"]}]}]}	2023-12-10T09:22:48+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of ehartford/dolphin-2.2-yi-34b-200k ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model ehartford/dolphin-2.2-yi-34b-200k on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T09:19:14.695653(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of ehartford/dolphin-2.2-yi-34b-200k", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model ehartford/dolphin-2.2-yi-34b-200k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T09:19:14.695653(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ehartford/dolphin-2.2-yi-34b-200k", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model ehartford/dolphin-2.2-yi-34b-200k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T09:19:14.695653(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 24, 31, 173, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ehartford/dolphin-2.2-yi-34b-200k## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ehartford/dolphin-2.2-yi-34b-200k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T09:19:14.695653(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
e6e5c42f5d2c478ac178ba726ae80dacc5fde2c1	THIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH AWAY, DO NOT CLAIM THIS IS YOURS! GIVE CREDIT TO: LAYNZ28 AND KALOMAZE --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> THIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH A WAY, DO NOT CLAIM THIS IS YOURS! GIVE CREDIT TO: LAYNZ28 AND KALOMAZE ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - License: [mit]	Lask8/Advanced-Mangio-RVC-Fork	[ "license:mit", "music", "region:us" ]	2023-12-10T10:14:44+00:00	{"license": "mit", "pretty_name": "mangi", "tags": ["music"]}	2023-12-10T10:21:05+00:00	[]	[]	TAGS #license-mit #music #region-us	THIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH AWAY, DO NOT CLAIM THIS IS YOURS! GIVE CREDIT TO: LAYNZ28 AND KALOMAZE --- # Dataset Card for Dataset Name THIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH A WAY, DO NOT CLAIM THIS IS YOURS! GIVE CREDIT TO: LAYNZ28 AND KALOMAZE ## Dataset Details ### Dataset Description - License: [mit]	[ "# Dataset Card for Dataset Name\n\n\n\nTHIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH A WAY, DO NOT CLAIM THIS IS YOURS! \nGIVE CREDIT TO: LAYNZ28 AND KALOMAZE", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- License: [mit]" ]	[ "TAGS\n#license-mit #music #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nTHIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH A WAY, DO NOT CLAIM THIS IS YOURS! \nGIVE CREDIT TO: LAYNZ28 AND KALOMAZE", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- License: [mit]" ]	[ 13, 66, 4, 11 ]	[ "passage: TAGS\n#license-mit #music #region-us \n# Dataset Card for Dataset Name\n\n\n\nTHIS DATASET IS NOT MINE, IT IS OWNED BY KALOMAZE, WHICH I EDITED IN SUCH A WAY, DO NOT CLAIM THIS IS YOURS! \nGIVE CREDIT TO: LAYNZ28 AND KALOMAZE## Dataset Details### Dataset Description\n\n\n\n\n\n- License: [mit]" ]
0b5545cf6de7b1c302f8314b82e41f45b58bfb66	# Dataset Card for Evaluation run of DopeorNope/COKAL-v1-70B ## Dataset Description - Homepage: - Repository: https://huggingface.co/DopeorNope/COKAL-v1-70B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [DopeorNope/COKAL-v1-70B](https://huggingface.co/DopeorNope/COKAL-v1-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_DopeorNope__COKAL-v1-70B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T10:21:56.669760](https://huggingface.co/datasets/open-llm-leaderboard/details_DopeorNope__COKAL-v1-70B/blob/main/results_2023-12-10T10-21-56.669760.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6806080675011864, "acc_stderr": 0.031026141939535783, "acc_norm": 0.6871684287339627, "acc_norm_stderr": 0.03163298834751675, "mc1": 0.609547123623011, "mc1_stderr": 0.017078230743431448, "mc2": 0.7279131434968619, "mc2_stderr": 0.012814436118254086 }, "harness\|arc:challenge\|25": { "acc": 0.8583617747440273, "acc_stderr": 0.010189361609566652, "acc_norm": 0.8745733788395904, "acc_norm_stderr": 0.009678644555462999 }, "harness\|hellaswag\|10": { "acc": 0.6278629755028878, "acc_stderr": 0.004823867761332464, "acc_norm": 0.8329018123879706, "acc_norm_stderr": 0.0037230107458783956 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5481481481481482, "acc_stderr": 0.04299268905480864, "acc_norm": 0.5481481481481482, "acc_norm_stderr": 0.04299268905480864 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7631578947368421, "acc_stderr": 0.034597776068105365, "acc_norm": 0.7631578947368421, "acc_norm_stderr": 0.034597776068105365 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6127659574468085, "acc_stderr": 0.03184389265339526, "acc_norm": 0.6127659574468085, "acc_norm_stderr": 0.03184389265339526 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.38596491228070173, "acc_stderr": 0.04579639422070434, "acc_norm": 0.38596491228070173, "acc_norm_stderr": 0.04579639422070434 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4417989417989418, "acc_stderr": 0.025576257061253837, "acc_norm": 0.4417989417989418, "acc_norm_stderr": 0.025576257061253837 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8, "acc_stderr": 0.02275520495954294, "acc_norm": 0.8, "acc_norm_stderr": 0.02275520495954294 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8787878787878788, "acc_stderr": 0.02548549837334323, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.02548549837334323 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7076923076923077, "acc_stderr": 0.023060438380857733, "acc_norm": 0.7076923076923077, "acc_norm_stderr": 0.023060438380857733 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7647058823529411, "acc_stderr": 0.027553614467863804, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.027553614467863804 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4304635761589404, "acc_stderr": 0.04042809961395634, "acc_norm": 0.4304635761589404, "acc_norm_stderr": 0.04042809961395634 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8917431192660551, "acc_stderr": 0.01332134844761175, "acc_norm": 0.8917431192660551, "acc_norm_stderr": 0.01332134844761175 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6342592592592593, "acc_stderr": 0.032847388576472056, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.032847388576472056 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.019907399791316935, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.019907399791316935 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8945147679324894, "acc_stderr": 0.019995560723758538, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.019995560723758538 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7354260089686099, "acc_stderr": 0.029605103217038325, "acc_norm": 0.7354260089686099, "acc_norm_stderr": 0.029605103217038325 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594626, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594626 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8677685950413223, "acc_stderr": 0.030922788320445795, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.030922788320445795 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5267857142857143, "acc_stderr": 0.047389751192741546, "acc_norm": 0.5267857142857143, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8931623931623932, "acc_stderr": 0.02023714900899093, "acc_norm": 0.8931623931623932, "acc_norm_stderr": 0.02023714900899093 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8365261813537676, "acc_stderr": 0.013223928616741617, "acc_norm": 0.8365261813537676, "acc_norm_stderr": 0.013223928616741617 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.024105712607754307, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.024105712607754307 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.6078212290502794, "acc_stderr": 0.016329061073207453, "acc_norm": 0.6078212290502794, "acc_norm_stderr": 0.016329061073207453 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7620578778135049, "acc_stderr": 0.02418515064781871, "acc_norm": 0.7620578778135049, "acc_norm_stderr": 0.02418515064781871 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7901234567901234, "acc_stderr": 0.02265834408598137, "acc_norm": 0.7901234567901234, "acc_norm_stderr": 0.02265834408598137 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5283687943262412, "acc_stderr": 0.029779450957303055, "acc_norm": 0.5283687943262412, "acc_norm_stderr": 0.029779450957303055 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6114732724902217, "acc_stderr": 0.012448817838292376, "acc_norm": 0.6114732724902217, "acc_norm_stderr": 0.012448817838292376 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7573529411764706, "acc_stderr": 0.026040662474201264, "acc_norm": 0.7573529411764706, "acc_norm_stderr": 0.026040662474201264 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7467320261437909, "acc_stderr": 0.01759348689536683, "acc_norm": 0.7467320261437909, "acc_norm_stderr": 0.01759348689536683 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142783, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142783 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.89, "acc_stderr": 0.03144660377352202, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352202 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.038899512528272166, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.038899512528272166 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8538011695906432, "acc_stderr": 0.02709729011807082, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.02709729011807082 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.609547123623011, "mc1_stderr": 0.017078230743431448, "mc2": 0.7279131434968619, "mc2_stderr": 0.012814436118254086 }, "harness\|winogrande\|5": { "acc": 0.8026835043409629, "acc_stderr": 0.011185026389050374 }, "harness\|gsm8k\|5": { "acc": 0.39272175890826383, "acc_stderr": 0.013451745349586566 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_DopeorNope__COKAL-v1-70B	[ "region:us" ]	2023-12-10T10:24:20+00:00	{"pretty_name": "Evaluation run of DopeorNope/COKAL-v1-70B", "dataset_summary": "Dataset automatically created during the evaluation run of model [DopeorNope/COKAL-v1-70B](https://huggingface.co/DopeorNope/COKAL-v1-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_DopeorNope__COKAL-v1-70B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T10:21:56.669760](https://huggingface.co/datasets/open-llm-leaderboard/details_DopeorNope__COKAL-v1-70B/blob/main/results_2023-12-10T10-21-56.669760.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6806080675011864,\n \"acc_stderr\": 0.031026141939535783,\n \"acc_norm\": 0.6871684287339627,\n \"acc_norm_stderr\": 0.03163298834751675,\n \"mc1\": 0.609547123623011,\n \"mc1_stderr\": 0.017078230743431448,\n \"mc2\": 0.7279131434968619,\n \"mc2_stderr\": 0.012814436118254086\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.8583617747440273,\n \"acc_stderr\": 0.010189361609566652,\n \"acc_norm\": 0.8745733788395904,\n \"acc_norm_stderr\": 0.009678644555462999\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6278629755028878,\n \"acc_stderr\": 0.004823867761332464,\n \"acc_norm\": 0.8329018123879706,\n \"acc_norm_stderr\": 0.0037230107458783956\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5481481481481482,\n \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.5481481481481482,\n \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.034597776068105365,\n \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.034597776068105365\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.6127659574468085,\n \"acc_stderr\": 0.03184389265339526,\n \"acc_norm\": 0.6127659574468085,\n \"acc_norm_stderr\": 0.03184389265339526\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.38596491228070173,\n \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253837,\n \"acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253837\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02275520495954294,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02275520495954294\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8787878787878788,\n \"acc_stderr\": 0.02548549837334323,\n \"acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.02548549837334323\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.7076923076923077,\n \"acc_stderr\": 0.023060438380857733,\n \"acc_norm\": 0.7076923076923077,\n \"acc_norm_stderr\": 0.023060438380857733\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.027553614467863804,\n \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.027553614467863804\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4304635761589404,\n \"acc_stderr\": 0.04042809961395634,\n \"acc_norm\": 0.4304635761589404,\n \"acc_norm_stderr\": 0.04042809961395634\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8917431192660551,\n \"acc_stderr\": 0.01332134844761175,\n \"acc_norm\": 0.8917431192660551,\n \"acc_norm_stderr\": 0.01332134844761175\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n \"acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316935,\n \"acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316935\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8945147679324894,\n \"acc_stderr\": 0.019995560723758538,\n \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.019995560723758538\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7354260089686099,\n \"acc_stderr\": 0.029605103217038325,\n \"acc_norm\": 0.7354260089686099,\n \"acc_norm_stderr\": 0.029605103217038325\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594626,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594626\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445795,\n \"acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445795\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5267857142857143,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8931623931623932,\n \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8365261813537676,\n \"acc_stderr\": 0.013223928616741617,\n \"acc_norm\": 0.8365261813537676,\n \"acc_norm_stderr\": 0.013223928616741617\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.6078212290502794,\n \"acc_stderr\": 0.016329061073207453,\n \"acc_norm\": 0.6078212290502794,\n \"acc_norm_stderr\": 0.016329061073207453\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7620578778135049,\n \"acc_stderr\": 0.02418515064781871,\n \"acc_norm\": 0.7620578778135049,\n \"acc_norm_stderr\": 0.02418515064781871\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7901234567901234,\n \"acc_stderr\": 0.02265834408598137,\n \"acc_norm\": 0.7901234567901234,\n \"acc_norm_stderr\": 0.02265834408598137\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.5283687943262412,\n \"acc_stderr\": 0.029779450957303055,\n \"acc_norm\": 0.5283687943262412,\n \"acc_norm_stderr\": 0.029779450957303055\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6114732724902217,\n \"acc_stderr\": 0.012448817838292376,\n \"acc_norm\": 0.6114732724902217,\n \"acc_norm_stderr\": 0.012448817838292376\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.7573529411764706,\n \"acc_stderr\": 0.026040662474201264,\n \"acc_norm\": 0.7573529411764706,\n \"acc_norm_stderr\": 0.026040662474201264\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.7467320261437909,\n \"acc_stderr\": 0.01759348689536683,\n \"acc_norm\": 0.7467320261437909,\n \"acc_norm_stderr\": 0.01759348689536683\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.02709729011807082,\n \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.02709729011807082\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.609547123623011,\n \"mc1_stderr\": 0.017078230743431448,\n \"mc2\": 0.7279131434968619,\n \"mc2_stderr\": 0.012814436118254086\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8026835043409629,\n \"acc_stderr\": 0.011185026389050374\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.39272175890826383,\n \"acc_stderr\": 0.013451745349586566\n }\n}\n```", "repo_url": "https://huggingface.co/DopeorNope/COKAL-v1-70B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-21-56.669760.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-21-56.669760.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T10_21_56.669760", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-21-56.669760.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-21-56.669760.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T10_21_56.669760", "path": ["results_2023-12-10T10-21-56.669760.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T10-21-56.669760.parquet"]}]}]}	2023-12-10T10:25:05+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of DopeorNope/COKAL-v1-70B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model DopeorNope/COKAL-v1-70B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T10:21:56.669760(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of DopeorNope/COKAL-v1-70B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model DopeorNope/COKAL-v1-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:21:56.669760(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of DopeorNope/COKAL-v1-70B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model DopeorNope/COKAL-v1-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:21:56.669760(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 22, 31, 171, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of DopeorNope/COKAL-v1-70B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model DopeorNope/COKAL-v1-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T10:21:56.669760(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
7cd825cf1090ce0505126ef2de14b77ca408bce5	# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties ## Dataset Description - Homepage: - Repository: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T10:23:58.856045](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties/blob/main/results_2023-12-10T10-23-58.856045.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7470453642761706, "acc_stderr": 0.028619765288934736, "acc_norm": 0.7535136424409922, "acc_norm_stderr": 0.02914053190348252, "mc1": 0.3806609547123623, "mc1_stderr": 0.01699762787190793, "mc2": 0.5283809284788162, "mc2_stderr": 0.01556812706457422 }, "harness\|arc:challenge\|25": { "acc": 0.6245733788395904, "acc_stderr": 0.014150631435111726, "acc_norm": 0.6493174061433447, "acc_norm_stderr": 0.013944635930726096 }, "harness\|hellaswag\|10": { "acc": 0.6541525592511452, "acc_stderr": 0.0047467168057357635, "acc_norm": 0.8499302927703645, "acc_norm_stderr": 0.003564098420387773 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7849056603773585, "acc_stderr": 0.02528839450289137, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.02528839450289137 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.049020713000019756, "acc_norm": 0.61, "acc_norm_stderr": 0.049020713000019756 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7572254335260116, "acc_stderr": 0.0326926380614177, "acc_norm": 0.7572254335260116, "acc_norm_stderr": 0.0326926380614177 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.04951218252396262, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.04951218252396262 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7787234042553192, "acc_stderr": 0.027136349602424056, "acc_norm": 0.7787234042553192, "acc_norm_stderr": 0.027136349602424056 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594964, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594964 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7448275862068966, "acc_stderr": 0.03632984052707842, "acc_norm": 0.7448275862068966, "acc_norm_stderr": 0.03632984052707842 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6587301587301587, "acc_stderr": 0.02441923496681907, "acc_norm": 0.6587301587301587, "acc_norm_stderr": 0.02441923496681907 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8903225806451613, "acc_stderr": 0.017776778700485177, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.017776778700485177 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6650246305418719, "acc_stderr": 0.033208527423483104, "acc_norm": 0.6650246305418719, "acc_norm_stderr": 0.033208527423483104 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9141414141414141, "acc_stderr": 0.01996022556317289, "acc_norm": 0.9141414141414141, "acc_norm_stderr": 0.01996022556317289 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527048, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527048 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7846153846153846, "acc_stderr": 0.020843034557462878, "acc_norm": 0.7846153846153846, "acc_norm_stderr": 0.020843034557462878 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.42962962962962964, "acc_stderr": 0.030182099804387262, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.030182099804387262 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.865546218487395, "acc_stderr": 0.022159373072744442, "acc_norm": 0.865546218487395, "acc_norm_stderr": 0.022159373072744442 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4503311258278146, "acc_stderr": 0.040622900186837764, "acc_norm": 0.4503311258278146, "acc_norm_stderr": 0.040622900186837764 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9211009174311927, "acc_stderr": 0.011558198113769567, "acc_norm": 0.9211009174311927, "acc_norm_stderr": 0.011558198113769567 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6574074074074074, "acc_stderr": 0.032365852526021574, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.032365852526021574 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.019907399791316945, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.019907399791316945 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9156118143459916, "acc_stderr": 0.01809424711647332, "acc_norm": 0.9156118143459916, "acc_norm_stderr": 0.01809424711647332 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7802690582959642, "acc_stderr": 0.027790177064383595, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.027790177064383595 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8396946564885496, "acc_stderr": 0.03217829420744632, "acc_norm": 0.8396946564885496, "acc_norm_stderr": 0.03217829420744632 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540627, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540627 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243631, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243631 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.852760736196319, "acc_stderr": 0.02783991527833965, "acc_norm": 0.852760736196319, "acc_norm_stderr": 0.02783991527833965 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5267857142857143, "acc_stderr": 0.047389751192741546, "acc_norm": 0.5267857142857143, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8543689320388349, "acc_stderr": 0.034926064766237906, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.034926064766237906 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9102564102564102, "acc_stderr": 0.01872430174194165, "acc_norm": 0.9102564102564102, "acc_norm_stderr": 0.01872430174194165 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9016602809706258, "acc_stderr": 0.01064835630187633, "acc_norm": 0.9016602809706258, "acc_norm_stderr": 0.01064835630187633 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8208092485549133, "acc_stderr": 0.020647590029679332, "acc_norm": 0.8208092485549133, "acc_norm_stderr": 0.020647590029679332 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7284916201117319, "acc_stderr": 0.014874252168095268, "acc_norm": 0.7284916201117319, "acc_norm_stderr": 0.014874252168095268 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.021828596053108402, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.021828596053108402 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8070739549839229, "acc_stderr": 0.022411516780911366, "acc_norm": 0.8070739549839229, "acc_norm_stderr": 0.022411516780911366 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8487654320987654, "acc_stderr": 0.01993508609214988, "acc_norm": 0.8487654320987654, "acc_norm_stderr": 0.01993508609214988 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6382978723404256, "acc_stderr": 0.028663820147199485, "acc_norm": 0.6382978723404256, "acc_norm_stderr": 0.028663820147199485 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5893089960886571, "acc_stderr": 0.012564871542534356, "acc_norm": 0.5893089960886571, "acc_norm_stderr": 0.012564871542534356 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8272058823529411, "acc_stderr": 0.022966067585581795, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.022966067585581795 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.815359477124183, "acc_stderr": 0.01569702924075778, "acc_norm": 0.815359477124183, "acc_norm_stderr": 0.01569702924075778 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.04069306319721376, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.04069306319721376 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8448979591836735, "acc_stderr": 0.0231747988612186, "acc_norm": 0.8448979591836735, "acc_norm_stderr": 0.0231747988612186 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.900497512437811, "acc_stderr": 0.021166216304659386, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.021166216304659386 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.93, "acc_stderr": 0.0256432399976243, "acc_norm": 0.93, "acc_norm_stderr": 0.0256432399976243 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8830409356725146, "acc_stderr": 0.024648068961366152, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.024648068961366152 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3806609547123623, "mc1_stderr": 0.01699762787190793, "mc2": 0.5283809284788162, "mc2_stderr": 0.01556812706457422 }, "harness\|winogrande\|5": { "acc": 0.7924230465666929, "acc_stderr": 0.011398593419386776 }, "harness\|gsm8k\|5": { "acc": 0.5405610310841547, "acc_stderr": 0.013727093010429785 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties	[ "region:us" ]	2023-12-10T10:26:49+00:00	{"pretty_name": "Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T10:23:58.856045](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties/blob/main/results_2023-12-10T10-23-58.856045.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7470453642761706,\n \"acc_stderr\": 0.028619765288934736,\n \"acc_norm\": 0.7535136424409922,\n \"acc_norm_stderr\": 0.02914053190348252,\n \"mc1\": 0.3806609547123623,\n \"mc1_stderr\": 0.01699762787190793,\n \"mc2\": 0.5283809284788162,\n \"mc2_stderr\": 0.01556812706457422\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6245733788395904,\n \"acc_stderr\": 0.014150631435111726,\n \"acc_norm\": 0.6493174061433447,\n \"acc_norm_stderr\": 0.013944635930726096\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6541525592511452,\n \"acc_stderr\": 0.0047467168057357635,\n \"acc_norm\": 0.8499302927703645,\n \"acc_norm_stderr\": 0.003564098420387773\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9097222222222222,\n \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.049020713000019756,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.049020713000019756\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.04951218252396262,\n \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.04951218252396262\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7787234042553192,\n \"acc_stderr\": 0.027136349602424056,\n \"acc_norm\": 0.7787234042553192,\n \"acc_norm_stderr\": 0.027136349602424056\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5701754385964912,\n \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.5701754385964912,\n \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7448275862068966,\n \"acc_stderr\": 0.03632984052707842,\n \"acc_norm\": 0.7448275862068966,\n \"acc_norm_stderr\": 0.03632984052707842\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6587301587301587,\n \"acc_stderr\": 0.02441923496681907,\n \"acc_norm\": 0.6587301587301587,\n \"acc_norm_stderr\": 0.02441923496681907\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.49206349206349204,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8903225806451613,\n \"acc_stderr\": 0.017776778700485177,\n \"acc_norm\": 0.8903225806451613,\n \"acc_norm_stderr\": 0.017776778700485177\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6650246305418719,\n \"acc_stderr\": 0.033208527423483104,\n \"acc_norm\": 0.6650246305418719,\n \"acc_norm_stderr\": 0.033208527423483104\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9141414141414141,\n \"acc_stderr\": 0.01996022556317289,\n \"acc_norm\": 0.9141414141414141,\n \"acc_norm_stderr\": 0.01996022556317289\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527048,\n \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527048\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.7846153846153846,\n \"acc_stderr\": 0.020843034557462878,\n \"acc_norm\": 0.7846153846153846,\n \"acc_norm_stderr\": 0.020843034557462878\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.030182099804387262,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.030182099804387262\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.865546218487395,\n \"acc_stderr\": 0.022159373072744442,\n \"acc_norm\": 0.865546218487395,\n \"acc_norm_stderr\": 0.022159373072744442\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4503311258278146,\n \"acc_stderr\": 0.040622900186837764,\n \"acc_norm\": 0.4503311258278146,\n \"acc_norm_stderr\": 0.040622900186837764\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769567,\n \"acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769567\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6574074074074074,\n \"acc_stderr\": 0.032365852526021574,\n \"acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.032365852526021574\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316945,\n \"acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316945\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7802690582959642,\n \"acc_stderr\": 0.027790177064383595,\n \"acc_norm\": 0.7802690582959642,\n \"acc_norm_stderr\": 0.027790177064383595\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744632,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744632\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540627,\n \"acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540627\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8518518518518519,\n \"acc_stderr\": 0.03434300243631,\n \"acc_norm\": 0.8518518518518519,\n \"acc_norm_stderr\": 0.03434300243631\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.852760736196319,\n \"acc_stderr\": 0.02783991527833965,\n \"acc_norm\": 0.852760736196319,\n \"acc_norm_stderr\": 0.02783991527833965\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5267857142857143,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.034926064766237906,\n \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.034926064766237906\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9102564102564102,\n \"acc_stderr\": 0.01872430174194165,\n \"acc_norm\": 0.9102564102564102,\n \"acc_norm_stderr\": 0.01872430174194165\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9016602809706258,\n \"acc_stderr\": 0.01064835630187633,\n \"acc_norm\": 0.9016602809706258,\n \"acc_norm_stderr\": 0.01064835630187633\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7284916201117319,\n \"acc_stderr\": 0.014874252168095268,\n \"acc_norm\": 0.7284916201117319,\n \"acc_norm_stderr\": 0.014874252168095268\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.021828596053108402,\n \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.021828596053108402\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8070739549839229,\n \"acc_stderr\": 0.022411516780911366,\n \"acc_norm\": 0.8070739549839229,\n \"acc_norm_stderr\": 0.022411516780911366\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.01993508609214988,\n \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.01993508609214988\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.6382978723404256,\n \"acc_stderr\": 0.028663820147199485,\n \"acc_norm\": 0.6382978723404256,\n \"acc_norm_stderr\": 0.028663820147199485\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5893089960886571,\n \"acc_stderr\": 0.012564871542534356,\n \"acc_norm\": 0.5893089960886571,\n \"acc_norm_stderr\": 0.012564871542534356\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.022966067585581795,\n \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.022966067585581795\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.815359477124183,\n \"acc_stderr\": 0.01569702924075778,\n \"acc_norm\": 0.815359477124183,\n \"acc_norm_stderr\": 0.01569702924075778\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.04069306319721376,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.04069306319721376\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.8448979591836735,\n \"acc_stderr\": 0.0231747988612186,\n \"acc_norm\": 0.8448979591836735,\n \"acc_norm_stderr\": 0.0231747988612186\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.900497512437811,\n \"acc_stderr\": 0.021166216304659386,\n \"acc_norm\": 0.900497512437811,\n \"acc_norm_stderr\": 0.021166216304659386\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.93,\n \"acc_stderr\": 0.0256432399976243,\n \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.0256432399976243\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3806609547123623,\n \"mc1_stderr\": 0.01699762787190793,\n \"mc2\": 0.5283809284788162,\n \"mc2_stderr\": 0.01556812706457422\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7924230465666929,\n \"acc_stderr\": 0.011398593419386776\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5405610310841547,\n \"acc_stderr\": 0.013727093010429785\n }\n}\n```", "repo_url": "https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|gsm8k\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hellaswag\|10_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T10-23-58.856045.parquet", "/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T10-23-58.856045.parquet", 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["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-23-58.856045.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T10_23_58.856045", "path": ["results_2023-12-10T10-23-58.856045.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T10-23-58.856045.parquet"]}]}]}	2023-12-10T10:27:33+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T10:23:58.856045(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:23:58.856045(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:23:58.856045(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 39, 31, 188, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T10:23:58.856045(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
d61fd51276cce0c553121ebf998e68e2e66b5352	## Dataset Description A dataset for Chinese Sentiment-Analyze Merged two datasets - Weibo-Sentiment - Shopping-Review	t1annnnn/Chinese_sentimentAnalyze	[ "license:mit", "region:us" ]	2023-12-10T10:32:28+00:00	{"license": "mit", "dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21107188, "num_examples": 148036}, {"name": "validation", "num_bytes": 2327791, "num_examples": 16449}, {"name": "test", "num_bytes": 2615618, "num_examples": 18277}], "download_size": 20038622, "dataset_size": 26050597}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}]}	2023-12-30T06:51:16+00:00	[]	[]	TAGS #license-mit #region-us	## Dataset Description A dataset for Chinese Sentiment-Analyze Merged two datasets - Weibo-Sentiment - Shopping-Review	[ "## Dataset Description\n\nA dataset for Chinese Sentiment-Analyze\n\nMerged two datasets\n- Weibo-Sentiment\n- Shopping-Review" ]	[ "TAGS\n#license-mit #region-us \n", "## Dataset Description\n\nA dataset for Chinese Sentiment-Analyze\n\nMerged two datasets\n- Weibo-Sentiment\n- Shopping-Review" ]	[ 11, 33 ]	[ "passage: TAGS\n#license-mit #region-us \n## Dataset Description\n\nA dataset for Chinese Sentiment-Analyze\n\nMerged two datasets\n- Weibo-Sentiment\n- Shopping-Review" ]
02028560bc3ee7eee4b48e2f96efb2d7772e0643	# Dataset Card for Evaluation run of abdulrahman-nuzha/finetuned-llama-v2.0 ## Dataset Description - Homepage: - Repository: https://huggingface.co/abdulrahman-nuzha/finetuned-llama-v2.0 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [abdulrahman-nuzha/finetuned-llama-v2.0](https://huggingface.co/abdulrahman-nuzha/finetuned-llama-v2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_abdulrahman-nuzha__finetuned-llama-v2.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T10:47:40.022995](https://huggingface.co/datasets/open-llm-leaderboard/details_abdulrahman-nuzha__finetuned-llama-v2.0/blob/main/results_2023-12-10T10-47-40.022995.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.43945994951550066, "acc_stderr": 0.034385529407471936, "acc_norm": 0.4442918982351828, "acc_norm_stderr": 0.035190222707291795, "mc1": 0.24969400244798043, "mc1_stderr": 0.015152286907148128, "mc2": 0.3908033560283727, "mc2_stderr": 0.013656125379191442 }, "harness\|arc:challenge\|25": { "acc": 0.4803754266211604, "acc_stderr": 0.014600132075947087, "acc_norm": 0.5315699658703071, "acc_norm_stderr": 0.014582236460866978 }, "harness\|hellaswag\|10": { "acc": 0.5789683330013942, "acc_stderr": 0.0049271558825981845, "acc_norm": 0.7775343557060347, "acc_norm_stderr": 0.0041505226302310265 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.40789473684210525, "acc_stderr": 0.03999309712777471, "acc_norm": 0.40789473684210525, "acc_norm_stderr": 0.03999309712777471 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.44528301886792454, "acc_stderr": 0.030588052974270655, "acc_norm": 0.44528301886792454, "acc_norm_stderr": 0.030588052974270655 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04155319955593146, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04155319955593146 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.37572254335260113, "acc_stderr": 0.036928207672648664, "acc_norm": 0.37572254335260113, "acc_norm_stderr": 0.036928207672648664 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.18627450980392157, "acc_stderr": 0.038739587141493524, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.038739587141493524 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4425531914893617, "acc_stderr": 0.03246956919789958, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.03246956919789958 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537315, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537315 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.02201908001221789, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.02201908001221789 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04216370213557835, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04216370213557835 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.4290322580645161, "acc_stderr": 0.02815603653823321, "acc_norm": 0.4290322580645161, "acc_norm_stderr": 0.02815603653823321 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3448275862068966, "acc_stderr": 0.03344283744280458, "acc_norm": 0.3448275862068966, "acc_norm_stderr": 0.03344283744280458 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5636363636363636, "acc_stderr": 0.03872592983524754, "acc_norm": 0.5636363636363636, "acc_norm_stderr": 0.03872592983524754 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.4696969696969697, "acc_stderr": 0.03555804051763929, "acc_norm": 0.4696969696969697, "acc_norm_stderr": 0.03555804051763929 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6321243523316062, "acc_stderr": 0.034801756684660366, "acc_norm": 0.6321243523316062, "acc_norm_stderr": 0.034801756684660366 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4, "acc_stderr": 0.024838811988033158, "acc_norm": 0.4, "acc_norm_stderr": 0.024838811988033158 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.0263357394040558 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.3907563025210084, "acc_stderr": 0.031693802357129965, "acc_norm": 0.3907563025210084, "acc_norm_stderr": 0.031693802357129965 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.03734535676787198, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.03734535676787198 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5853211009174312, "acc_stderr": 0.021122903208602585, "acc_norm": 0.5853211009174312, "acc_norm_stderr": 0.021122903208602585 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.18055555555555555, "acc_stderr": 0.02623287897149166, "acc_norm": 0.18055555555555555, "acc_norm_stderr": 0.02623287897149166 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.4803921568627451, "acc_stderr": 0.03506612560524867, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.03506612560524867 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5443037974683544, "acc_stderr": 0.03241920684693334, "acc_norm": 0.5443037974683544, "acc_norm_stderr": 0.03241920684693334 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5291479820627802, "acc_stderr": 0.03350073248773404, "acc_norm": 0.5291479820627802, "acc_norm_stderr": 0.03350073248773404 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.46564885496183206, "acc_stderr": 0.04374928560599738, "acc_norm": 0.46564885496183206, "acc_norm_stderr": 0.04374928560599738 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.628099173553719, "acc_stderr": 0.044120158066245044, "acc_norm": 0.628099173553719, "acc_norm_stderr": 0.044120158066245044 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.04826217294139894, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.04826217294139894 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.4601226993865031, "acc_stderr": 0.03915857291436972, "acc_norm": 0.4601226993865031, "acc_norm_stderr": 0.03915857291436972 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness\|hendrycksTest-management\|5": { "acc": 0.47572815533980584, "acc_stderr": 0.049449010929737795, "acc_norm": 0.47572815533980584, "acc_norm_stderr": 0.049449010929737795 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6837606837606838, "acc_stderr": 0.030463656747340275, "acc_norm": 0.6837606837606838, "acc_norm_stderr": 0.030463656747340275 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6002554278416348, "acc_stderr": 0.017516847907053282, "acc_norm": 0.6002554278416348, "acc_norm_stderr": 0.017516847907053282 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.48554913294797686, "acc_stderr": 0.02690784985628254, "acc_norm": 0.48554913294797686, "acc_norm_stderr": 0.02690784985628254 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5, "acc_stderr": 0.028629916715693413, "acc_norm": 0.5, "acc_norm_stderr": 0.028629916715693413 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5562700964630225, "acc_stderr": 0.028217683556652308, "acc_norm": 0.5562700964630225, "acc_norm_stderr": 0.028217683556652308 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5030864197530864, "acc_stderr": 0.027820214158594363, "acc_norm": 0.5030864197530864, "acc_norm_stderr": 0.027820214158594363 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.32269503546099293, "acc_stderr": 0.02788913930053478, "acc_norm": 0.32269503546099293, "acc_norm_stderr": 0.02788913930053478 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.32920469361147325, "acc_stderr": 0.012002091666902297, "acc_norm": 0.32920469361147325, "acc_norm_stderr": 0.012002091666902297 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.44485294117647056, "acc_stderr": 0.030187532060329387, "acc_norm": 0.44485294117647056, "acc_norm_stderr": 0.030187532060329387 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4297385620915033, "acc_stderr": 0.020027122784928554, "acc_norm": 0.4297385620915033, "acc_norm_stderr": 0.020027122784928554 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.45454545454545453, "acc_stderr": 0.04769300568972744, "acc_norm": 0.45454545454545453, "acc_norm_stderr": 0.04769300568972744 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.363265306122449, "acc_stderr": 0.03078905113903081, "acc_norm": 0.363265306122449, "acc_norm_stderr": 0.03078905113903081 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5920398009950248, "acc_stderr": 0.03475116365194092, "acc_norm": 0.5920398009950248, "acc_norm_stderr": 0.03475116365194092 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3855421686746988, "acc_stderr": 0.037891344246115496, "acc_norm": 0.3855421686746988, "acc_norm_stderr": 0.037891344246115496 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6374269005847953, "acc_stderr": 0.0368713061556206, "acc_norm": 0.6374269005847953, "acc_norm_stderr": 0.0368713061556206 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.24969400244798043, "mc1_stderr": 0.015152286907148128, "mc2": 0.3908033560283727, "mc2_stderr": 0.013656125379191442 }, "harness\|winogrande\|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440474 }, "harness\|gsm8k\|5": { "acc": 0.09931766489764973, "acc_stderr": 0.008238371412683965 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_abdulrahman-nuzha__finetuned-llama-v2.0	[ "region:us" ]	2023-12-10T10:50:45+00:00	{"pretty_name": "Evaluation run of abdulrahman-nuzha/finetuned-llama-v2.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [abdulrahman-nuzha/finetuned-llama-v2.0](https://huggingface.co/abdulrahman-nuzha/finetuned-llama-v2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abdulrahman-nuzha__finetuned-llama-v2.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T10:47:40.022995](https://huggingface.co/datasets/open-llm-leaderboard/details_abdulrahman-nuzha__finetuned-llama-v2.0/blob/main/results_2023-12-10T10-47-40.022995.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.43945994951550066,\n \"acc_stderr\": 0.034385529407471936,\n \"acc_norm\": 0.4442918982351828,\n \"acc_norm_stderr\": 0.035190222707291795,\n \"mc1\": 0.24969400244798043,\n \"mc1_stderr\": 0.015152286907148128,\n \"mc2\": 0.3908033560283727,\n \"mc2_stderr\": 0.013656125379191442\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.4803754266211604,\n \"acc_stderr\": 0.014600132075947087,\n \"acc_norm\": 0.5315699658703071,\n \"acc_norm_stderr\": 0.014582236460866978\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5789683330013942,\n \"acc_stderr\": 0.0049271558825981845,\n \"acc_norm\": 0.7775343557060347,\n \"acc_norm_stderr\": 0.0041505226302310265\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.40789473684210525,\n \"acc_stderr\": 0.03999309712777471,\n \"acc_norm\": 0.40789473684210525,\n \"acc_norm_stderr\": 0.03999309712777471\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.44528301886792454,\n \"acc_stderr\": 0.030588052974270655,\n \"acc_norm\": 0.44528301886792454,\n \"acc_norm_stderr\": 0.030588052974270655\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04155319955593146,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04155319955593146\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.37572254335260113,\n \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.37572254335260113,\n \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.038739587141493524,\n \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.038739587141493524\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.03246956919789958,\n \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.03246956919789958\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.02201908001221789,\n \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.02201908001221789\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.4290322580645161,\n \"acc_stderr\": 0.02815603653823321,\n \"acc_norm\": 0.4290322580645161,\n \"acc_norm_stderr\": 0.02815603653823321\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.3448275862068966,\n \"acc_stderr\": 0.03344283744280458,\n \"acc_norm\": 0.3448275862068966,\n \"acc_norm_stderr\": 0.03344283744280458\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-47-40.022995.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T10_47_40.022995", "path": ["results_2023-12-10T10-47-40.022995.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T10-47-40.022995.parquet"]}]}]}	2023-12-10T10:51:27+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of abdulrahman-nuzha/finetuned-llama-v2.0 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model abdulrahman-nuzha/finetuned-llama-v2.0 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T10:47:40.022995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of abdulrahman-nuzha/finetuned-llama-v2.0", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model abdulrahman-nuzha/finetuned-llama-v2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:47:40.022995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of abdulrahman-nuzha/finetuned-llama-v2.0", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model abdulrahman-nuzha/finetuned-llama-v2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:47:40.022995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 26, 31, 175, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of abdulrahman-nuzha/finetuned-llama-v2.0## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model abdulrahman-nuzha/finetuned-llama-v2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T10:47:40.022995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
2f983e01d7d420ecd751b54a13486709e9849025	# Dataset Card for Evaluation run of meta-math/MetaMath-Llemma-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/meta-math/MetaMath-Llemma-7B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [meta-math/MetaMath-Llemma-7B](https://huggingface.co/meta-math/MetaMath-Llemma-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_meta-math__MetaMath-Llemma-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T10:48:07.737490](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-math__MetaMath-Llemma-7B/blob/main/results_2023-12-10T10-48-07.737490.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.4805727831472479, "acc_stderr": 0.03501873176922748, "acc_norm": 0.47876803306000837, "acc_norm_stderr": 0.03574673517078834, "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557994, "mc2": 0.39610018025256144, "mc2_stderr": 0.015159247351087708 }, "harness\|arc:challenge\|25": { "acc": 0.439419795221843, "acc_stderr": 0.014503747823580125, "acc_norm": 0.46501706484641636, "acc_norm_stderr": 0.01457558392201967 }, "harness\|hellaswag\|10": { "acc": 0.4731129257120096, "acc_stderr": 0.004982561815214125, "acc_norm": 0.6169089822744473, "acc_norm_stderr": 0.004851466623601442 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353228, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353228 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.04063302731486671, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.04063302731486671 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.47924528301886793, "acc_stderr": 0.030746349975723463, "acc_norm": 0.47924528301886793, "acc_norm_stderr": 0.030746349975723463 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4930555555555556, "acc_stderr": 0.04180806750294938, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.04180806750294938 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4682080924855491, "acc_stderr": 0.03804749744364763, "acc_norm": 0.4682080924855491, "acc_norm_stderr": 0.03804749744364763 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663434, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663434 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4808510638297872, "acc_stderr": 0.032662042990646796, "acc_norm": 0.4808510638297872, "acc_norm_stderr": 0.032662042990646796 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.32456140350877194, "acc_stderr": 0.044045561573747664, "acc_norm": 0.32456140350877194, "acc_norm_stderr": 0.044045561573747664 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944423, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944423 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5193548387096775, "acc_stderr": 0.028422687404312107, "acc_norm": 0.5193548387096775, "acc_norm_stderr": 0.028422687404312107 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.41379310344827586, "acc_stderr": 0.03465304488406795, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.03465304488406795 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5636363636363636, "acc_stderr": 0.03872592983524754, "acc_norm": 0.5636363636363636, "acc_norm_stderr": 0.03872592983524754 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5757575757575758, "acc_stderr": 0.035212249088415845, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.035212249088415845 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.5492227979274611, "acc_stderr": 0.03590910952235524, "acc_norm": 0.5492227979274611, "acc_norm_stderr": 0.03590910952235524 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5, "acc_stderr": 0.02535100632816969, "acc_norm": 0.5, "acc_norm_stderr": 0.02535100632816969 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.27037037037037037, "acc_stderr": 0.027080372815145665, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.027080372815145665 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.4831932773109244, "acc_stderr": 0.03246013680375308, "acc_norm": 0.4831932773109244, "acc_norm_stderr": 0.03246013680375308 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6091743119266055, "acc_stderr": 0.02092005834611106, "acc_norm": 0.6091743119266055, "acc_norm_stderr": 0.02092005834611106 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4583333333333333, "acc_stderr": 0.033981108902946366, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.033981108902946366 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5049019607843137, "acc_stderr": 0.03509143375606785, "acc_norm": 0.5049019607843137, "acc_norm_stderr": 0.03509143375606785 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5654008438818565, "acc_stderr": 0.03226759995510145, "acc_norm": 0.5654008438818565, "acc_norm_stderr": 0.03226759995510145 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.39461883408071746, "acc_stderr": 0.03280400504755291, "acc_norm": 0.39461883408071746, "acc_norm_stderr": 0.03280400504755291 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5114503816793893, "acc_stderr": 0.04384140024078016, "acc_norm": 0.5114503816793893, "acc_norm_stderr": 0.04384140024078016 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6115702479338843, "acc_stderr": 0.04449270350068384, "acc_norm": 0.6115702479338843, "acc_norm_stderr": 0.04449270350068384 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.49074074074074076, "acc_stderr": 0.04832853553437055, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.04832853553437055 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5398773006134969, "acc_stderr": 0.039158572914369714, "acc_norm": 0.5398773006134969, "acc_norm_stderr": 0.039158572914369714 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.33035714285714285, "acc_stderr": 0.04464285714285713, "acc_norm": 0.33035714285714285, "acc_norm_stderr": 0.04464285714285713 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6407766990291263, "acc_stderr": 0.04750458399041697, "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.04750458399041697 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.030572811310299607, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.030572811310299607 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.561941251596424, "acc_stderr": 0.01774223223825723, "acc_norm": 0.561941251596424, "acc_norm_stderr": 0.01774223223825723 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.49710982658959535, "acc_stderr": 0.026918645383239022, "acc_norm": 0.49710982658959535, "acc_norm_stderr": 0.026918645383239022 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.30837988826815643, "acc_stderr": 0.015445716910998893, "acc_norm": 0.30837988826815643, "acc_norm_stderr": 0.015445716910998893 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5163398692810458, "acc_stderr": 0.028614624752805434, "acc_norm": 0.5163398692810458, "acc_norm_stderr": 0.028614624752805434 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.4983922829581994, "acc_stderr": 0.02839794490780661, "acc_norm": 0.4983922829581994, "acc_norm_stderr": 0.02839794490780661 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.44753086419753085, "acc_stderr": 0.02766713856942271, "acc_norm": 0.44753086419753085, "acc_norm_stderr": 0.02766713856942271 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35815602836879434, "acc_stderr": 0.028602085862759422, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.028602085862759422 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3396349413298566, "acc_stderr": 0.012095592506931967, "acc_norm": 0.3396349413298566, "acc_norm_stderr": 0.012095592506931967 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.41544117647058826, "acc_stderr": 0.02993534270787775, "acc_norm": 0.41544117647058826, "acc_norm_stderr": 0.02993534270787775 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4068627450980392, "acc_stderr": 0.019873802005061177, "acc_norm": 0.4068627450980392, "acc_norm_stderr": 0.019873802005061177 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5, "acc_stderr": 0.04789131426105757, "acc_norm": 0.5, "acc_norm_stderr": 0.04789131426105757 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5551020408163265, "acc_stderr": 0.031814251181977865, "acc_norm": 0.5551020408163265, "acc_norm_stderr": 0.031814251181977865 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6368159203980099, "acc_stderr": 0.034005985055990146, "acc_norm": 0.6368159203980099, "acc_norm_stderr": 0.034005985055990146 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3855421686746988, "acc_stderr": 0.037891344246115496, "acc_norm": 0.3855421686746988, "acc_norm_stderr": 0.037891344246115496 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.03811079669833531, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.03811079669833531 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557994, "mc2": 0.39610018025256144, "mc2_stderr": 0.015159247351087708 }, "harness\|winogrande\|5": { "acc": 0.6274664561957379, "acc_stderr": 0.013588173888522445 }, "harness\|gsm8k\|5": { "acc": 0.6095526914329037, "acc_stderr": 0.013437829864668582 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_meta-math__MetaMath-Llemma-7B	[ "region:us" ]	2023-12-10T10:51:06+00:00	{"pretty_name": "Evaluation run of meta-math/MetaMath-Llemma-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [meta-math/MetaMath-Llemma-7B](https://huggingface.co/meta-math/MetaMath-Llemma-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_meta-math__MetaMath-Llemma-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T10:48:07.737490](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-math__MetaMath-Llemma-7B/blob/main/results_2023-12-10T10-48-07.737490.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4805727831472479,\n \"acc_stderr\": 0.03501873176922748,\n \"acc_norm\": 0.47876803306000837,\n \"acc_norm_stderr\": 0.03574673517078834,\n \"mc1\": 0.2594859241126071,\n \"mc1_stderr\": 0.015345409485557994,\n \"mc2\": 0.39610018025256144,\n \"mc2_stderr\": 0.015159247351087708\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.439419795221843,\n \"acc_stderr\": 0.014503747823580125,\n \"acc_norm\": 0.46501706484641636,\n \"acc_norm_stderr\": 0.01457558392201967\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4731129257120096,\n \"acc_stderr\": 0.004982561815214125,\n \"acc_norm\": 0.6169089822744473,\n \"acc_norm_stderr\": 0.004851466623601442\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.04063302731486671,\n \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.04063302731486671\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.47924528301886793,\n \"acc_stderr\": 0.030746349975723463,\n \"acc_norm\": 0.47924528301886793,\n \"acc_norm_stderr\": 0.030746349975723463\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4930555555555556,\n \"acc_stderr\": 0.04180806750294938,\n \"acc_norm\": 0.4930555555555556,\n \"acc_norm_stderr\": 0.04180806750294938\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4682080924855491,\n \"acc_stderr\": 0.03804749744364763,\n \"acc_norm\": 0.4682080924855491,\n \"acc_norm_stderr\": 0.03804749744364763\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663434,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663434\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.032662042990646796,\n \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.032662042990646796\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.32456140350877194,\n \"acc_stderr\": 0.044045561573747664,\n \"acc_norm\": 0.32456140350877194,\n \"acc_norm_stderr\": 0.044045561573747664\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944423,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944423\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5193548387096775,\n \"acc_stderr\": 0.028422687404312107,\n \"acc_norm\": 0.5193548387096775,\n \"acc_norm_stderr\": 0.028422687404312107\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406795,\n \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406795\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.5636363636363636,\n \"acc_stderr\": 0.03872592983524754,\n \"acc_norm\": 0.5636363636363636,\n \"acc_norm_stderr\": 0.03872592983524754\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.5757575757575758,\n \"acc_stderr\": 0.035212249088415845,\n \"acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.035212249088415845\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.5492227979274611,\n \"acc_stderr\": 0.03590910952235524,\n \"acc_norm\": 0.5492227979274611,\n \"acc_norm_stderr\": 0.03590910952235524\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.02535100632816969,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.02535100632816969\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.27037037037037037,\n \"acc_stderr\": 0.027080372815145665,\n \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.027080372815145665\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.4831932773109244,\n \"acc_stderr\": 0.03246013680375308,\n \"acc_norm\": 0.4831932773109244,\n \"acc_norm_stderr\": 0.03246013680375308\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.6091743119266055,\n \"acc_stderr\": 0.02092005834611106,\n \"acc_norm\": 0.6091743119266055,\n \"acc_norm_stderr\": 0.02092005834611106\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.033981108902946366,\n \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.033981108902946366\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.5049019607843137,\n \"acc_stderr\": 0.03509143375606785,\n \"acc_norm\": 0.5049019607843137,\n \"acc_norm_stderr\": 0.03509143375606785\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.5654008438818565,\n \"acc_stderr\": 0.03226759995510145,\n \"acc_norm\": 0.5654008438818565,\n \"acc_norm_stderr\": 0.03226759995510145\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.39461883408071746,\n \"acc_stderr\": 0.03280400504755291,\n \"acc_norm\": 0.39461883408071746,\n \"acc_norm_stderr\": 0.03280400504755291\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.5114503816793893,\n \"acc_stderr\": 0.04384140024078016,\n \"acc_norm\": 0.5114503816793893,\n \"acc_norm_stderr\": 0.04384140024078016\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.6115702479338843,\n \"acc_stderr\": 0.04449270350068384,\n \"acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.04449270350068384\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.039158572914369714,\n \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.039158572914369714\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.33035714285714285,\n \"acc_stderr\": 0.04464285714285713,\n \"acc_norm\": 0.33035714285714285,\n \"acc_norm_stderr\": 0.04464285714285713\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.6407766990291263,\n \"acc_stderr\": 0.04750458399041697,\n \"acc_norm\": 0.6407766990291263,\n \"acc_norm_stderr\": 0.04750458399041697\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.030572811310299607,\n \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.030572811310299607\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.561941251596424,\n \"acc_stderr\": 0.01774223223825723,\n \"acc_norm\": 0.561941251596424,\n \"acc_norm_stderr\": 0.01774223223825723\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.026918645383239022,\n \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.026918645383239022\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.30837988826815643,\n \"acc_stderr\": 0.015445716910998893,\n \"acc_norm\": 0.30837988826815643,\n \"acc_norm_stderr\": 0.015445716910998893\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.5163398692810458,\n \"acc_stderr\": 0.028614624752805434,\n \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.028614624752805434\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.4983922829581994,\n \"acc_stderr\": 0.02839794490780661,\n \"acc_norm\": 0.4983922829581994,\n \"acc_norm_stderr\": 0.02839794490780661\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.44753086419753085,\n \"acc_stderr\": 0.02766713856942271,\n \"acc_norm\": 0.44753086419753085,\n \"acc_norm_stderr\": 0.02766713856942271\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759422,\n \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.028602085862759422\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3396349413298566,\n \"acc_stderr\": 0.012095592506931967,\n \"acc_norm\": 0.3396349413298566,\n \"acc_norm_stderr\": 0.012095592506931967\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.41544117647058826,\n \"acc_stderr\": 0.02993534270787775,\n \"acc_norm\": 0.41544117647058826,\n \"acc_norm_stderr\": 0.02993534270787775\n },\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-48-07.737490.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T10-48-07.737490.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T10_48_07.737490", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-48-07.737490.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T10-48-07.737490.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T10_48_07.737490", "path": ["results_2023-12-10T10-48-07.737490.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T10-48-07.737490.parquet"]}]}]}	2023-12-10T10:51:49+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of meta-math/MetaMath-Llemma-7B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model meta-math/MetaMath-Llemma-7B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T10:48:07.737490(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of meta-math/MetaMath-Llemma-7B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model meta-math/MetaMath-Llemma-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:48:07.737490(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of meta-math/MetaMath-Llemma-7B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model meta-math/MetaMath-Llemma-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T10:48:07.737490(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 22, 31, 171, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of meta-math/MetaMath-Llemma-7B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model meta-math/MetaMath-Llemma-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T10:48:07.737490(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
3d480fc1c44e77dee170bacd70d8764879dbb75a	# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity ## Dataset Description - Homepage: - Repository: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T11:09:27.293582](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity/blob/main/results_2023-12-10T11-09-27.293582.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7687816232153334, "acc_stderr": 0.027928447952356685, "acc_norm": 0.7740918392624115, "acc_norm_stderr": 0.028444170747139307, "mc1": 0.42472460220318237, "mc1_stderr": 0.017304000957167477, "mc2": 0.5784493836096679, "mc2_stderr": 0.015412397526106352 }, "harness\|arc:challenge\|25": { "acc": 0.6484641638225256, "acc_stderr": 0.013952413699600933, "acc_norm": 0.674061433447099, "acc_norm_stderr": 0.013697432466693247 }, "harness\|hellaswag\|10": { "acc": 0.663114917347142, "acc_stderr": 0.00471679287443321, "acc_norm": 0.857697669786895, "acc_norm_stderr": 0.0034864596026062417 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.725925925925926, "acc_stderr": 0.03853254836552003, "acc_norm": 0.725925925925926, "acc_norm_stderr": 0.03853254836552003 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8947368421052632, "acc_stderr": 0.024974533450920697, "acc_norm": 0.8947368421052632, "acc_norm_stderr": 0.024974533450920697 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036843, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036843 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8113207547169812, "acc_stderr": 0.024079995130062246, "acc_norm": 0.8113207547169812, "acc_norm_stderr": 0.024079995130062246 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9166666666666666, "acc_stderr": 0.023112508176051236, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.023112508176051236 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.0498887651569859, "acc_norm": 0.44, "acc_norm_stderr": 0.0498887651569859 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.0332055644308557, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.0332055644308557 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.048971049527263666, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.048971049527263666 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.8, "acc_stderr": 0.0261488180184245, "acc_norm": 0.8, "acc_norm_stderr": 0.0261488180184245 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04434600701584925, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04434600701584925 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7724137931034483, "acc_stderr": 0.03493950380131183, "acc_norm": 0.7724137931034483, "acc_norm_stderr": 0.03493950380131183 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.7116402116402116, "acc_stderr": 0.023330654054535892, "acc_norm": 0.7116402116402116, "acc_norm_stderr": 0.023330654054535892 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9064516129032258, "acc_stderr": 0.01656575466827098, "acc_norm": 0.9064516129032258, "acc_norm_stderr": 0.01656575466827098 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.645320197044335, "acc_stderr": 0.03366124489051449, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.03366124489051449 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8727272727272727, "acc_stderr": 0.02602465765165619, "acc_norm": 0.8727272727272727, "acc_norm_stderr": 0.02602465765165619 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9393939393939394, "acc_stderr": 0.01699999492742161, "acc_norm": 0.9393939393939394, "acc_norm_stderr": 0.01699999492742161 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.823076923076923, "acc_stderr": 0.019348070174396985, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.019348070174396985 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.030401786406101507, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.030401786406101507 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8613445378151261, "acc_stderr": 0.022448264476832593, "acc_norm": 0.8613445378151261, "acc_norm_stderr": 0.022448264476832593 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9174311926605505, "acc_stderr": 0.011800361363016569, "acc_norm": 0.9174311926605505, "acc_norm_stderr": 0.011800361363016569 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03214952147802749, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03214952147802749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9411764705882353, "acc_stderr": 0.016514409561025796, "acc_norm": 0.9411764705882353, "acc_norm_stderr": 0.016514409561025796 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9029535864978903, "acc_stderr": 0.019269323025640266, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.019269323025640266 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7982062780269058, "acc_stderr": 0.02693611191280227, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.02693611191280227 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8778625954198473, "acc_stderr": 0.028718776889342323, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.028718776889342323 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9008264462809917, "acc_stderr": 0.027285246312758957, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.027285246312758957 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.9074074074074074, "acc_stderr": 0.02802188803860944, "acc_norm": 0.9074074074074074, "acc_norm_stderr": 0.02802188803860944 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6428571428571429, "acc_stderr": 0.04547960999764376, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.04547960999764376 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8737864077669902, "acc_stderr": 0.03288180278808628, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.03288180278808628 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9316239316239316, "acc_stderr": 0.016534627684311357, "acc_norm": 0.9316239316239316, "acc_norm_stderr": 0.016534627684311357 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9080459770114943, "acc_stderr": 0.010333225570778518, "acc_norm": 0.9080459770114943, "acc_norm_stderr": 0.010333225570778518 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8092485549132948, "acc_stderr": 0.021152676966575277, "acc_norm": 0.8092485549132948, "acc_norm_stderr": 0.021152676966575277 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7642458100558659, "acc_stderr": 0.014196375686290804, "acc_norm": 0.7642458100558659, "acc_norm_stderr": 0.014196375686290804 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8300653594771242, "acc_stderr": 0.02150538312123138, "acc_norm": 0.8300653594771242, "acc_norm_stderr": 0.02150538312123138 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8231511254019293, "acc_stderr": 0.02167005888551079, "acc_norm": 0.8231511254019293, "acc_norm_stderr": 0.02167005888551079 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8672839506172839, "acc_stderr": 0.01887735383957187, "acc_norm": 0.8672839506172839, "acc_norm_stderr": 0.01887735383957187 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6560283687943262, "acc_stderr": 0.02833801742861133, "acc_norm": 0.6560283687943262, "acc_norm_stderr": 0.02833801742861133 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6121251629726207, "acc_stderr": 0.012444998309675633, "acc_norm": 0.6121251629726207, "acc_norm_stderr": 0.012444998309675633 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8308823529411765, "acc_stderr": 0.022770868010112997, "acc_norm": 0.8308823529411765, "acc_norm_stderr": 0.022770868010112997 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8366013071895425, "acc_stderr": 0.014957635756617647, "acc_norm": 0.8366013071895425, "acc_norm_stderr": 0.014957635756617647 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8408163265306122, "acc_stderr": 0.02342097206916633, "acc_norm": 0.8408163265306122, "acc_norm_stderr": 0.02342097206916633 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824664, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824664 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015578, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015578 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.42472460220318237, "mc1_stderr": 0.017304000957167477, "mc2": 0.5784493836096679, "mc2_stderr": 0.015412397526106352 }, "harness\|winogrande\|5": { "acc": 0.8310970797158642, "acc_stderr": 0.010529981411838906 }, "harness\|gsm8k\|5": { "acc": 0.6133434420015162, "acc_stderr": 0.01341395509596531 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity	[ "region:us" ]	2023-12-10T11:12:18+00:00	{"pretty_name": "Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T11:09:27.293582](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity/blob/main/results_2023-12-10T11-09-27.293582.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7687816232153334,\n \"acc_stderr\": 0.027928447952356685,\n \"acc_norm\": 0.7740918392624115,\n \"acc_norm_stderr\": 0.028444170747139307,\n \"mc1\": 0.42472460220318237,\n \"mc1_stderr\": 0.017304000957167477,\n \"mc2\": 0.5784493836096679,\n \"mc2_stderr\": 0.015412397526106352\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6484641638225256,\n \"acc_stderr\": 0.013952413699600933,\n \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693247\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.663114917347142,\n \"acc_stderr\": 0.00471679287443321,\n \"acc_norm\": 0.857697669786895,\n \"acc_norm_stderr\": 0.0034864596026062417\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.725925925925926,\n \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.725925925925926,\n \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8947368421052632,\n \"acc_stderr\": 0.024974533450920697,\n \"acc_norm\": 0.8947368421052632,\n \"acc_norm_stderr\": 0.024974533450920697\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036843,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036843\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.024079995130062246,\n \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.024079995130062246\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9166666666666666,\n \"acc_stderr\": 0.023112508176051236,\n \"acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.023112508176051236\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0261488180184245,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0261488180184245\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04434600701584925,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04434600701584925\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7724137931034483,\n \"acc_stderr\": 0.03493950380131183,\n \"acc_norm\": 0.7724137931034483,\n \"acc_norm_stderr\": 0.03493950380131183\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.7116402116402116,\n \"acc_stderr\": 0.023330654054535892,\n \"acc_norm\": 0.7116402116402116,\n \"acc_norm_stderr\": 0.023330654054535892\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5476190476190477,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.9064516129032258,\n \"acc_stderr\": 0.01656575466827098,\n \"acc_norm\": 0.9064516129032258,\n \"acc_norm_stderr\": 0.01656575466827098\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.645320197044335,\n \"acc_stderr\": 0.03366124489051449,\n \"acc_norm\": 0.645320197044335,\n \"acc_norm_stderr\": 0.03366124489051449\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8727272727272727,\n \"acc_stderr\": 0.02602465765165619,\n \"acc_norm\": 0.8727272727272727,\n \"acc_norm_stderr\": 0.02602465765165619\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9393939393939394,\n \"acc_stderr\": 0.01699999492742161,\n \"acc_norm\": 0.9393939393939394,\n \"acc_norm_stderr\": 0.01699999492742161\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396985,\n \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396985\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.030401786406101507,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.030401786406101507\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.8613445378151261,\n \"acc_stderr\": 0.022448264476832593,\n \"acc_norm\": 0.8613445378151261,\n \"acc_norm_stderr\": 0.022448264476832593\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9174311926605505,\n \"acc_stderr\": 0.011800361363016569,\n \"acc_norm\": 0.9174311926605505,\n \"acc_norm_stderr\": 0.011800361363016569\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03214952147802749,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03214952147802749\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9411764705882353,\n \"acc_stderr\": 0.016514409561025796,\n \"acc_norm\": 0.9411764705882353,\n \"acc_norm_stderr\": 0.016514409561025796\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.9029535864978903,\n \"acc_stderr\": 0.019269323025640266,\n \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.019269323025640266\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7982062780269058,\n \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.028718776889342323,\n \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.028718776889342323\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.9074074074074074,\n \"acc_stderr\": 0.02802188803860944,\n \"acc_norm\": 0.9074074074074074,\n \"acc_norm_stderr\": 0.02802188803860944\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9316239316239316,\n \"acc_stderr\": 0.016534627684311357,\n \"acc_norm\": 0.9316239316239316,\n \"acc_norm_stderr\": 0.016534627684311357\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9080459770114943,\n \"acc_stderr\": 0.010333225570778518,\n \"acc_norm\": 0.9080459770114943,\n \"acc_norm_stderr\": 0.010333225570778518\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.021152676966575277,\n \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.021152676966575277\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7642458100558659,\n \"acc_stderr\": 0.014196375686290804,\n \"acc_norm\": 0.7642458100558659,\n \"acc_norm_stderr\": 0.014196375686290804\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.8300653594771242,\n \"acc_stderr\": 0.02150538312123138,\n \"acc_norm\": 0.8300653594771242,\n \"acc_norm_stderr\": 0.02150538312123138\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8231511254019293,\n \"acc_stderr\": 0.02167005888551079,\n \"acc_norm\": 0.8231511254019293,\n \"acc_norm_stderr\": 0.02167005888551079\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.01887735383957187,\n \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.01887735383957187\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.6560283687943262,\n \"acc_stderr\": 0.02833801742861133,\n \"acc_norm\": 0.6560283687943262,\n \"acc_norm_stderr\": 0.02833801742861133\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6121251629726207,\n \"acc_stderr\": 0.012444998309675633,\n \"acc_norm\": 0.6121251629726207,\n \"acc_norm_stderr\": 0.012444998309675633\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.8308823529411765,\n \"acc_stderr\": 0.022770868010112997,\n \"acc_norm\": 0.8308823529411765,\n \"acc_norm_stderr\": 0.022770868010112997\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.8366013071895425,\n \"acc_stderr\": 0.014957635756617647,\n \"acc_norm\": 0.8366013071895425,\n \"acc_norm_stderr\": 0.014957635756617647\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.02342097206916633,\n \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.02342097206916633\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8905472636815921,\n \"acc_stderr\": 0.022076326101824664,\n \"acc_norm\": 0.8905472636815921,\n \"acc_norm_stderr\": 0.022076326101824664\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5783132530120482,\n \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015578,\n \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015578\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.42472460220318237,\n \"mc1_stderr\": 0.017304000957167477,\n \"mc2\": 0.5784493836096679,\n \"mc2_stderr\": 0.015412397526106352\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.010529981411838906\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6133434420015162,\n \"acc_stderr\": 0.01341395509596531\n }\n}\n```", "repo_url": "https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T11_09_27.293582", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T11-09-27.293582.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T11-09-27.293582.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T11_09_27.293582", "path": ["/details_harness\|gsm8k\|5_2023-12-10T11-09-27.293582.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T11-09-27.293582.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T11_09_27.293582", "path": ["/details_harness\|hellaswag\|10_2023-12-10T11-09-27.293582.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T11-09-27.293582.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_10T11_09_27.293582", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T11-09-27.293582.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T11-09-27.293582.parquet", 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during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T11:09:27.293582(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:09:27.293582(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:09:27.293582(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 44, 31, 193, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T11:09:27.293582(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
b67d2d6864258cf4ce8cb9108a6626751505cc33	# Dataset Card for "rapidapi-example-responses-summaries" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses-summaries	[ "region:us" ]	2023-12-10T11:24:49+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "usage", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "total_tokens", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 667897, "num_examples": 1000}], "download_size": 276970, "dataset_size": 667897}}	2023-12-11T10:06:49+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses-summaries" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses-summaries\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses-summaries\"\n\nMore Information needed" ]	[ 6, 23 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses-summaries\"\n\nMore Information needed" ]
e1b9d335c4d0b6eee4e9134b97141652af25a3e5	dataset_info: features: - name: tat dtype: string - name: rus dtype: string	IPSAN/tatar-russian-parallel-corpora	[ "region:us" ]	2023-12-10T11:30:04+00:00	{}	2023-12-23T03:59:16+00:00	[]	[]	TAGS #region-us	dataset_info: features: - name: tat dtype: string - name: rus dtype: string	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
e04880c650c31e01af4befcb2da03edf968fc2ef	# Dataset Card for "ds_rplan_full_rplanpy_floorplan_to_color" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ekuhn/ds_rplan_full_rplanpy_floorplan_to_color	[ "region:us" ]	2023-12-10T11:42:11+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "img", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}, {"name": "num_rooms", "dtype": "int64"}], "splits": [{"name": "full", "num_bytes": 96780371, "num_examples": 80788}], "download_size": 52010769, "dataset_size": 96780371}}	2023-12-10T11:42:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ds_rplan_full_rplanpy_floorplan_to_color" More Information needed	[ "# Dataset Card for \"ds_rplan_full_rplanpy_floorplan_to_color\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ds_rplan_full_rplanpy_floorplan_to_color\"\n\nMore Information needed" ]	[ 6, 28 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ds_rplan_full_rplanpy_floorplan_to_color\"\n\nMore Information needed" ]
79f94fd7780b93f37aa1100a39bb54e4b99c8236	# Dataset Card for Evaluation run of Q-bert/Merged-AGI-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/Q-bert/Merged-AGI-7B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Q-bert/Merged-AGI-7B](https://huggingface.co/Q-bert/Merged-AGI-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Q-bert__Merged-AGI-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T11:41:40.859542](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Merged-AGI-7B/blob/main/results_2023-12-10T11-41-40.859542.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6531012862827063, "acc_stderr": 0.03195381405127504, "acc_norm": 0.6544274230849765, "acc_norm_stderr": 0.03259816823473359, "mc1": 0.44430844553243576, "mc1_stderr": 0.017394586250743173, "mc2": 0.6023562221172835, "mc2_stderr": 0.015249864323171023 }, "harness\|arc:challenge\|25": { "acc": 0.6578498293515358, "acc_stderr": 0.01386415215917728, "acc_norm": 0.6860068259385665, "acc_norm_stderr": 0.013562691224726302 }, "harness\|hellaswag\|10": { "acc": 0.6772555267874926, "acc_stderr": 0.004665704208339041, "acc_norm": 0.8615813582951604, "acc_norm_stderr": 0.003446330748963712 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952928, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952928 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438662, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438662 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5957446808510638, "acc_stderr": 0.03208115750788684, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.03208115750788684 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778398, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778398 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268545, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268545 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.797979797979798, "acc_stderr": 0.028606204289229872, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.028606204289229872 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524558, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524558 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8587155963302753, "acc_stderr": 0.014933868987028072, "acc_norm": 0.8587155963302753, "acc_norm_stderr": 0.014933868987028072 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8354430379746836, "acc_stderr": 0.024135736240566932, "acc_norm": 0.8354430379746836, "acc_norm_stderr": 0.024135736240566932 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841403, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841403 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608303, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608303 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.41675977653631285, "acc_stderr": 0.016489134962438954, "acc_norm": 0.41675977653631285, "acc_norm_stderr": 0.016489134962438954 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7041800643086816, "acc_stderr": 0.02592237178881876, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.02592237178881876 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.02492200116888633, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.02492200116888633 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4576271186440678, "acc_stderr": 0.012724296550980188, "acc_norm": 0.4576271186440678, "acc_norm_stderr": 0.012724296550980188 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827072, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827072 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.03588702812826368, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826368 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.44430844553243576, "mc1_stderr": 0.017394586250743173, "mc2": 0.6023562221172835, "mc2_stderr": 0.015249864323171023 }, "harness\|winogrande\|5": { "acc": 0.8066298342541437, "acc_stderr": 0.011099796645920524 }, "harness\|gsm8k\|5": { "acc": 0.6338134950720242, "acc_stderr": 0.013270100238748828 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_Q-bert__Merged-AGI-7B	[ "region:us" ]	2023-12-10T11:44:32+00:00	{"pretty_name": "Evaluation run of Q-bert/Merged-AGI-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Q-bert/Merged-AGI-7B](https://huggingface.co/Q-bert/Merged-AGI-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Q-bert__Merged-AGI-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T11:41:40.859542](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Merged-AGI-7B/blob/main/results_2023-12-10T11-41-40.859542.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6531012862827063,\n \"acc_stderr\": 0.03195381405127504,\n \"acc_norm\": 0.6544274230849765,\n \"acc_norm_stderr\": 0.03259816823473359,\n \"mc1\": 0.44430844553243576,\n \"mc1_stderr\": 0.017394586250743173,\n \"mc2\": 0.6023562221172835,\n \"mc2_stderr\": 0.015249864323171023\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6578498293515358,\n \"acc_stderr\": 0.01386415215917728,\n \"acc_norm\": 0.6860068259385665,\n \"acc_norm_stderr\": 0.013562691224726302\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6772555267874926,\n \"acc_stderr\": 0.004665704208339041,\n \"acc_norm\": 0.8615813582951604,\n \"acc_norm_stderr\": 0.003446330748963712\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778398,\n \"acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778398\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268545\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.797979797979798,\n \"acc_stderr\": 0.028606204289229872,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229872\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524558,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524558\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8587155963302753,\n \"acc_stderr\": 0.014933868987028072,\n \"acc_norm\": 0.8587155963302753,\n \"acc_norm_stderr\": 0.014933868987028072\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8354430379746836,\n \"acc_stderr\": 0.024135736240566932,\n \"acc_norm\": 0.8354430379746836,\n \"acc_norm_stderr\": 0.024135736240566932\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.023086635086841403\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8301404853128991,\n \"acc_stderr\": 0.013428186370608303,\n \"acc_norm\": 0.8301404853128991,\n \"acc_norm_stderr\": 0.013428186370608303\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.41675977653631285,\n \"acc_stderr\": 0.016489134962438954,\n \"acc_norm\": 0.41675977653631285,\n \"acc_norm_stderr\": 0.016489134962438954\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7041800643086816,\n \"acc_stderr\": 0.02592237178881876,\n \"acc_norm\": 0.7041800643086816,\n \"acc_norm_stderr\": 0.02592237178881876\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.02492200116888633,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.02492200116888633\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4576271186440678,\n \"acc_stderr\": 0.012724296550980188,\n \"acc_norm\": 0.4576271186440678,\n \"acc_norm_stderr\": 0.012724296550980188\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.025196929874827072,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.025196929874827072\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826368,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826368\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.44430844553243576,\n \"mc1_stderr\": 0.017394586250743173,\n \"mc2\": 0.6023562221172835,\n \"mc2_stderr\": 0.015249864323171023\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8066298342541437,\n \"acc_stderr\": 0.011099796645920524\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6338134950720242,\n \"acc_stderr\": 0.013270100238748828\n }\n}\n```", "repo_url": "https://huggingface.co/Q-bert/Merged-AGI-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|gsm8k\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hellaswag\|10_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T11-41-40.859542.parquet", "/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T11-41-40.859542.parquet", 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["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["/details_harness\|winogrande\|5_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T11-41-40.859542.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T11_41_40.859542", "path": ["results_2023-12-10T11-41-40.859542.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T11-41-40.859542.parquet"]}]}]}	2023-12-10T11:45:13+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Q-bert/Merged-AGI-7B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Q-bert/Merged-AGI-7B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T11:41:40.859542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Q-bert/Merged-AGI-7B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Q-bert/Merged-AGI-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:41:40.859542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Q-bert/Merged-AGI-7B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Q-bert/Merged-AGI-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:41:40.859542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 20, 31, 169, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Q-bert/Merged-AGI-7B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Q-bert/Merged-AGI-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T11:41:40.859542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
3f7058c21d6a78f9a5c03c618dd5ece155bcc417	# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity ## Dataset Description - Homepage: - Repository: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T11:50:37.068936](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity/blob/main/results_2023-12-10T11-50-37.068936.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7674525732857425, "acc_stderr": 0.028092943162744702, "acc_norm": 0.7731400785419068, "acc_norm_stderr": 0.028608512168230946, "mc1": 0.4259485924112607, "mc1_stderr": 0.01731047190407654, "mc2": 0.5763314615956924, "mc2_stderr": 0.01543636329925335 }, "harness\|arc:challenge\|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620192, "acc_norm": 0.6689419795221843, "acc_norm_stderr": 0.01375206241981783 }, "harness\|hellaswag\|10": { "acc": 0.663612826130253, "acc_stderr": 0.0047150751198345095, "acc_norm": 0.8569010157339175, "acc_norm_stderr": 0.003494581076398526 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.725925925925926, "acc_stderr": 0.03853254836552003, "acc_norm": 0.725925925925926, "acc_norm_stderr": 0.03853254836552003 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8881578947368421, "acc_stderr": 0.025648341251693612, "acc_norm": 0.8881578947368421, "acc_norm_stderr": 0.025648341251693612 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8150943396226416, "acc_stderr": 0.023893351834464317, "acc_norm": 0.8150943396226416, "acc_norm_stderr": 0.023893351834464317 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.04793724854411018, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411018 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.0498887651569859, "acc_norm": 0.44, "acc_norm_stderr": 0.0498887651569859 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.03345036916788991, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.03345036916788991 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.048971049527263666, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.048971049527263666 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7829787234042553, "acc_stderr": 0.026947483121496228, "acc_norm": 0.7829787234042553, "acc_norm_stderr": 0.026947483121496228 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6491228070175439, "acc_stderr": 0.04489539350270698, "acc_norm": 0.6491228070175439, "acc_norm_stderr": 0.04489539350270698 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7517241379310344, "acc_stderr": 0.036001056927277696, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.036001056927277696 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.7195767195767195, "acc_stderr": 0.023135287974325628, "acc_norm": 0.7195767195767195, "acc_norm_stderr": 0.023135287974325628 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9225806451612903, "acc_stderr": 0.015203644420774848, "acc_norm": 0.9225806451612903, "acc_norm_stderr": 0.015203644420774848 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6896551724137931, "acc_stderr": 0.03255086769970104, "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03255086769970104 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9343434343434344, "acc_stderr": 0.01764652667723333, "acc_norm": 0.9343434343434344, "acc_norm_stderr": 0.01764652667723333 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8128205128205128, "acc_stderr": 0.019776601086550032, "acc_norm": 0.8128205128205128, "acc_norm_stderr": 0.019776601086550032 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.44074074074074077, "acc_stderr": 0.030270671157284074, "acc_norm": 0.44074074074074077, "acc_norm_stderr": 0.030270671157284074 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8613445378151261, "acc_stderr": 0.022448264476832597, "acc_norm": 0.8613445378151261, "acc_norm_stderr": 0.022448264476832597 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5165562913907285, "acc_stderr": 0.04080244185628972, "acc_norm": 0.5165562913907285, "acc_norm_stderr": 0.04080244185628972 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9229357798165138, "acc_stderr": 0.011434381698911096, "acc_norm": 0.9229357798165138, "acc_norm_stderr": 0.011434381698911096 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6620370370370371, "acc_stderr": 0.03225941352631295, "acc_norm": 0.6620370370370371, "acc_norm_stderr": 0.03225941352631295 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9313725490196079, "acc_stderr": 0.017744453647073322, "acc_norm": 0.9313725490196079, "acc_norm_stderr": 0.017744453647073322 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9029535864978903, "acc_stderr": 0.019269323025640262, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.019269323025640262 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8071748878923767, "acc_stderr": 0.026478240960489365, "acc_norm": 0.8071748878923767, "acc_norm_stderr": 0.026478240960489365 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8778625954198473, "acc_stderr": 0.028718776889342323, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.028718776889342323 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9090909090909091, "acc_stderr": 0.02624319405407388, "acc_norm": 0.9090909090909091, "acc_norm_stderr": 0.02624319405407388 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8611111111111112, "acc_stderr": 0.03343270062869622, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.03343270062869622 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6517857142857143, "acc_stderr": 0.04521829902833585, "acc_norm": 0.6517857142857143, "acc_norm_stderr": 0.04521829902833585 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8737864077669902, "acc_stderr": 0.03288180278808628, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.03288180278808628 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9358974358974359, "acc_stderr": 0.016046261631673137, "acc_norm": 0.9358974358974359, "acc_norm_stderr": 0.016046261631673137 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.909323116219668, "acc_stderr": 0.010268429662528547, "acc_norm": 0.909323116219668, "acc_norm_stderr": 0.010268429662528547 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8236994219653179, "acc_stderr": 0.020516425672490714, "acc_norm": 0.8236994219653179, "acc_norm_stderr": 0.020516425672490714 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7698324022346369, "acc_stderr": 0.014078339253425814, "acc_norm": 0.7698324022346369, "acc_norm_stderr": 0.014078339253425814 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8366013071895425, "acc_stderr": 0.02117062301121351, "acc_norm": 0.8366013071895425, "acc_norm_stderr": 0.02117062301121351 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8231511254019293, "acc_stderr": 0.02167005888551079, "acc_norm": 0.8231511254019293, "acc_norm_stderr": 0.02167005888551079 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8672839506172839, "acc_stderr": 0.01887735383957187, "acc_norm": 0.8672839506172839, "acc_norm_stderr": 0.01887735383957187 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6560283687943262, "acc_stderr": 0.02833801742861133, "acc_norm": 0.6560283687943262, "acc_norm_stderr": 0.02833801742861133 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6134289439374185, "acc_stderr": 0.012437288868088727, "acc_norm": 0.6134289439374185, "acc_norm_stderr": 0.012437288868088727 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8492647058823529, "acc_stderr": 0.021734235515652848, "acc_norm": 0.8492647058823529, "acc_norm_stderr": 0.021734235515652848 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8349673202614379, "acc_stderr": 0.015017550799247322, "acc_norm": 0.8349673202614379, "acc_norm_stderr": 0.015017550799247322 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.900497512437811, "acc_stderr": 0.021166216304659386, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.021166216304659386 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4259485924112607, "mc1_stderr": 0.01731047190407654, "mc2": 0.5763314615956924, "mc2_stderr": 0.01543636329925335 }, "harness\|winogrande\|5": { "acc": 0.8200473559589582, "acc_stderr": 0.01079646868806868 }, "harness\|gsm8k\|5": { "acc": 0.5981804397270659, "acc_stderr": 0.013504357787494044 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity	[ "region:us" ]	2023-12-10T11:53:26+00:00	{"pretty_name": "Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T11:50:37.068936](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity/blob/main/results_2023-12-10T11-50-37.068936.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7674525732857425,\n \"acc_stderr\": 0.028092943162744702,\n \"acc_norm\": 0.7731400785419068,\n \"acc_norm_stderr\": 0.028608512168230946,\n \"mc1\": 0.4259485924112607,\n \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.5763314615956924,\n \"mc2_stderr\": 0.01543636329925335\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620192,\n \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.01375206241981783\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.663612826130253,\n \"acc_stderr\": 0.0047150751198345095,\n \"acc_norm\": 0.8569010157339175,\n \"acc_norm_stderr\": 0.003494581076398526\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.725925925925926,\n \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.725925925925926,\n \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.025648341251693612,\n \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.025648341251693612\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.8150943396226416,\n \"acc_stderr\": 0.023893351834464317,\n \"acc_norm\": 0.8150943396226416,\n \"acc_norm_stderr\": 0.023893351834464317\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8958333333333334,\n \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411018,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411018\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.03345036916788991,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.03345036916788991\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.026947483121496228,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.026947483121496228\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6491228070175439,\n \"acc_stderr\": 0.04489539350270698,\n \"acc_norm\": 0.6491228070175439,\n \"acc_norm_stderr\": 0.04489539350270698\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.036001056927277696,\n \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.036001056927277696\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.7195767195767195,\n \"acc_stderr\": 0.023135287974325628,\n \"acc_norm\": 0.7195767195767195,\n \"acc_norm_stderr\": 0.023135287974325628\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.9225806451612903,\n \"acc_stderr\": 0.015203644420774848,\n \"acc_norm\": 0.9225806451612903,\n \"acc_norm_stderr\": 0.015203644420774848\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.6896551724137931,\n \"acc_stderr\": 0.03255086769970104,\n \"acc_norm\": 0.6896551724137931,\n \"acc_norm_stderr\": 0.03255086769970104\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.9343434343434344,\n \"acc_stderr\": 0.01764652667723333,\n \"acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.01764652667723333\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.8128205128205128,\n \"acc_stderr\": 0.019776601086550032,\n \"acc_norm\": 0.8128205128205128,\n \"acc_norm_stderr\": 0.019776601086550032\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.44074074074074077,\n \"acc_stderr\": 0.030270671157284074,\n \"acc_norm\": 0.44074074074074077,\n \"acc_norm_stderr\": 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["/details_harness\|winogrande\|5_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T11-50-37.068936.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T11_50_37.068936", "path": ["results_2023-12-10T11-50-37.068936.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T11-50-37.068936.parquet"]}]}]}	2023-12-10T11:54:08+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T11:50:37.068936(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:50:37.068936(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T11:50:37.068936(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 46, 31, 195, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T11:50:37.068936(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
a2f89d81cda22ce0fe70717387f9f38de027ec62	# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B ## Dataset Description - Homepage: - Repository: https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Falcon-1B](https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T12:28:40.127971](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B/blob/main/results_2023-12-10T12-28-40.127971.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.2504304926123702, "acc_stderr": 0.03055596076992834, "acc_norm": 0.2520875598433206, "acc_norm_stderr": 0.031361161079445435, "mc1": 0.23255813953488372, "mc1_stderr": 0.014789157531080515, "mc2": 0.38469934472881057, "mc2_stderr": 0.014966198091063187 }, "harness\|arc:challenge\|25": { "acc": 0.28498293515358364, "acc_stderr": 0.013191348179838793, "acc_norm": 0.310580204778157, "acc_norm_stderr": 0.013522292098053054 }, "harness\|hellaswag\|10": { "acc": 0.42381995618402707, "acc_stderr": 0.0049315259610357536, "acc_norm": 0.5491933877713603, "acc_norm_stderr": 0.004965572246803867 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.2518518518518518, "acc_stderr": 0.03749850709174022, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.03749850709174022 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.16447368421052633, "acc_stderr": 0.030167533468632688, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.030167533468632688 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.23773584905660378, "acc_stderr": 0.02619980880756191, "acc_norm": 0.23773584905660378, "acc_norm_stderr": 0.02619980880756191 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2708333333333333, "acc_stderr": 0.037161774375660185, "acc_norm": 0.2708333333333333, "acc_norm_stderr": 0.037161774375660185 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.15, "acc_stderr": 0.03588702812826371, "acc_norm": 0.15, "acc_norm_stderr": 0.03588702812826371 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3021276595744681, "acc_stderr": 0.030017554471880557, "acc_norm": 0.3021276595744681, "acc_norm_stderr": 0.030017554471880557 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2206896551724138, "acc_stderr": 0.03455930201924811, "acc_norm": 0.2206896551724138, "acc_norm_stderr": 0.03455930201924811 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525214, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525214 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.22903225806451613, "acc_stderr": 0.023904914311782644, "acc_norm": 0.22903225806451613, "acc_norm_stderr": 0.023904914311782644 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.26108374384236455, "acc_stderr": 0.030903796952114485, "acc_norm": 0.26108374384236455, "acc_norm_stderr": 0.030903796952114485 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.24242424242424243, "acc_stderr": 0.03346409881055953, "acc_norm": 0.24242424242424243, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.16666666666666666, "acc_stderr": 0.026552207828215286, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.026552207828215286 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.17098445595854922, "acc_stderr": 0.02717121368316453, "acc_norm": 0.17098445595854922, "acc_norm_stderr": 0.02717121368316453 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2128205128205128, "acc_stderr": 0.020752423722128002, "acc_norm": 0.2128205128205128, "acc_norm_stderr": 0.020752423722128002 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.22592592592592592, "acc_stderr": 0.025497532639609542, "acc_norm": 0.22592592592592592, "acc_norm_stderr": 0.025497532639609542 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.23529411764705882, "acc_stderr": 0.027553614467863804, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.027553614467863804 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2582781456953642, "acc_stderr": 0.035737053147634576, "acc_norm": 0.2582781456953642, "acc_norm_stderr": 0.035737053147634576 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.2036697247706422, "acc_stderr": 0.0172667420876308, "acc_norm": 0.2036697247706422, "acc_norm_stderr": 0.0172667420876308 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.21296296296296297, "acc_stderr": 0.027920963147993666, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.027920963147993666 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.03096451792692341, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.03096451792692341 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.29535864978902954, "acc_stderr": 0.029696338713422882, "acc_norm": 0.29535864978902954, "acc_norm_stderr": 0.029696338713422882 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.242152466367713, "acc_stderr": 0.028751392398694755, "acc_norm": 0.242152466367713, "acc_norm_stderr": 0.028751392398694755 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847836, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847836 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2809917355371901, "acc_stderr": 0.041032038305145124, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.041032038305145124 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.28703703703703703, "acc_stderr": 0.043733130409147614, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.043733130409147614 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.2331288343558282, "acc_stderr": 0.0332201579577674, "acc_norm": 0.2331288343558282, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.32142857142857145, "acc_stderr": 0.044328040552915206, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.044328040552915206 }, "harness\|hendrycksTest-management\|5": { "acc": 0.20388349514563106, "acc_stderr": 0.039891398595317706, "acc_norm": 0.20388349514563106, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2948717948717949, "acc_stderr": 0.029872577708891155, "acc_norm": 0.2948717948717949, "acc_norm_stderr": 0.029872577708891155 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2822477650063857, "acc_stderr": 0.016095302969878565, "acc_norm": 0.2822477650063857, "acc_norm_stderr": 0.016095302969878565 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.024630048979824775, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.024630048979824775 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2282958199356913, "acc_stderr": 0.0238393033113982, "acc_norm": 0.2282958199356913, "acc_norm_stderr": 0.0238393033113982 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2191358024691358, "acc_stderr": 0.0230167056402622, "acc_norm": 0.2191358024691358, "acc_norm_stderr": 0.0230167056402622 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843007, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843007 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2470664928292047, "acc_stderr": 0.011015752255279336, "acc_norm": 0.2470664928292047, "acc_norm_stderr": 0.011015752255279336 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.3014705882352941, "acc_stderr": 0.027875982114273168, "acc_norm": 0.3014705882352941, "acc_norm_stderr": 0.027875982114273168 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.24019607843137256, "acc_stderr": 0.01728276069516742, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.01728276069516742 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2, "acc_stderr": 0.03831305140884601, "acc_norm": 0.2, "acc_norm_stderr": 0.03831305140884601 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.24489795918367346, "acc_stderr": 0.02752963744017492, "acc_norm": 0.24489795918367346, "acc_norm_stderr": 0.02752963744017492 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.26865671641791045, "acc_stderr": 0.031343283582089536, "acc_norm": 0.26865671641791045, "acc_norm_stderr": 0.031343283582089536 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553026, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553026 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.25146198830409355, "acc_stderr": 0.033275044238468436, "acc_norm": 0.25146198830409355, "acc_norm_stderr": 0.033275044238468436 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23255813953488372, "mc1_stderr": 0.014789157531080515, "mc2": 0.38469934472881057, "mc2_stderr": 0.014966198091063187 }, "harness\|winogrande\|5": { "acc": 0.5540647198105761, "acc_stderr": 0.01397009348233069 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B	[ "region:us" ]	2023-12-10T12:30:47+00:00	{"pretty_name": "Evaluation run of KnutJaegersberg/Walter-Falcon-1B", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Falcon-1B](https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T12:28:40.127971](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Falcon-1B/blob/main/results_2023-12-10T12-28-40.127971.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2504304926123702,\n \"acc_stderr\": 0.03055596076992834,\n \"acc_norm\": 0.2520875598433206,\n \"acc_norm_stderr\": 0.031361161079445435,\n \"mc1\": 0.23255813953488372,\n \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.38469934472881057,\n \"mc2_stderr\": 0.014966198091063187\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.28498293515358364,\n \"acc_stderr\": 0.013191348179838793,\n \"acc_norm\": 0.310580204778157,\n \"acc_norm_stderr\": 0.013522292098053054\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.42381995618402707,\n \"acc_stderr\": 0.0049315259610357536,\n \"acc_norm\": 0.5491933877713603,\n \"acc_norm_stderr\": 0.004965572246803867\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.2518518518518518,\n \"acc_stderr\": 0.03749850709174022,\n \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.03749850709174022\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.030167533468632688,\n \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.030167533468632688\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756191,\n \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756191\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2708333333333333,\n \"acc_stderr\": 0.037161774375660185,\n \"acc_norm\": 0.2708333333333333,\n \"acc_norm_stderr\": 0.037161774375660185\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.15,\n \"acc_stderr\": 0.03588702812826371,\n \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.03588702812826371\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.3021276595744681,\n \"acc_stderr\": 0.030017554471880557,\n \"acc_norm\": 0.3021276595744681,\n \"acc_norm_stderr\": 0.030017554471880557\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.22807017543859648,\n \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.2206896551724138,\n \"acc_stderr\": 0.03455930201924811,\n \"acc_norm\": 0.2206896551724138,\n \"acc_norm_stderr\": 0.03455930201924811\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.20634920634920634,\n \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.22903225806451613,\n \"acc_stderr\": 0.023904914311782644,\n \"acc_norm\": 0.22903225806451613,\n \"acc_norm_stderr\": 0.023904914311782644\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114485,\n \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114485\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.16666666666666666,\n \"acc_stderr\": 0.026552207828215286,\n \"acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.026552207828215286\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.17098445595854922,\n \"acc_stderr\": 0.02717121368316453,\n \"acc_norm\": 0.17098445595854922,\n \"acc_norm_stderr\": 0.02717121368316453\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.2128205128205128,\n \"acc_stderr\": 0.020752423722128002,\n \"acc_norm\": 0.2128205128205128,\n \"acc_norm_stderr\": 0.020752423722128002\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.027553614467863804,\n \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.027553614467863804\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.2582781456953642,\n \"acc_stderr\": 0.035737053147634576,\n \"acc_norm\": 0.2582781456953642,\n \"acc_norm_stderr\": 0.035737053147634576\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.2036697247706422,\n \"acc_stderr\": 0.0172667420876308,\n \"acc_norm\": 0.2036697247706422,\n \"acc_norm_stderr\": 0.0172667420876308\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.21296296296296297,\n \"acc_stderr\": 0.027920963147993666,\n \"acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.027920963147993666\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.03096451792692341,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.03096451792692341\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.29535864978902954,\n \"acc_stderr\": 0.029696338713422882,\n \"acc_norm\": 0.29535864978902954,\n \"acc_norm_stderr\": 0.029696338713422882\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.242152466367713,\n \"acc_stderr\": 0.028751392398694755,\n \"acc_norm\": 0.242152466367713,\n \"acc_norm_stderr\": 0.028751392398694755\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847836,\n \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847836\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.2809917355371901,\n \"acc_stderr\": 0.041032038305145124,\n \"acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.041032038305145124\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.2331288343558282,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.2331288343558282,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.32142857142857145,\n \"acc_stderr\": 0.044328040552915206,\n \"acc_norm\": 0.32142857142857145,\n \"acc_norm_stderr\": 0.044328040552915206\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2948717948717949,\n \"acc_stderr\": 0.029872577708891155,\n \"acc_norm\": 0.2948717948717949,\n \"acc_norm_stderr\": 0.029872577708891155\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2822477650063857,\n \"acc_stderr\": 0.016095302969878565,\n \"acc_norm\": 0.2822477650063857,\n \"acc_norm_stderr\": 0.016095302969878565\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.024630048979824775,\n 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\"acc_stderr\": 0.027875982114273168,\n \"acc_norm\": 0.3014705882352941,\n \"acc_norm_stderr\": 0.027875982114273168\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.24019607843137256,\n \"acc_stderr\": 0.01728276069516742,\n \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.01728276069516742\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.03831305140884601,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.03831305140884601\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.02752963744017492,\n \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.02752963744017492\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.26865671641791045,\n \"acc_stderr\": 0.031343283582089536,\n \"acc_norm\": 0.26865671641791045,\n \"acc_norm_stderr\": 0.031343283582089536\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.2710843373493976,\n \"acc_stderr\": 0.03460579907553026,\n \"acc_norm\": 0.2710843373493976,\n \"acc_norm_stderr\": 0.03460579907553026\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.25146198830409355,\n \"acc_stderr\": 0.033275044238468436,\n \"acc_norm\": 0.25146198830409355,\n \"acc_norm_stderr\": 0.033275044238468436\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23255813953488372,\n \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.38469934472881057,\n \"mc2_stderr\": 0.014966198091063187\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.5540647198105761,\n \"acc_stderr\": 0.01397009348233069\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/KnutJaegersberg/Walter-Falcon-1B", "leaderboard_url": 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["/details_harness\|truthfulqa:mc\|0_2023-12-10T12-28-40.127971.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-10T12-28-40.127971.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_10T12_28_40.127971", "path": ["/details_harness\|winogrande\|5_2023-12-10T12-28-40.127971.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-10T12-28-40.127971.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_10T12_28_40.127971", "path": ["results_2023-12-10T12-28-40.127971.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T12-28-40.127971.parquet"]}]}]}	2023-12-10T12:31:32+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Falcon-1B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-10T12:28:40.127971(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Falcon-1B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T12:28:40.127971(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Falcon-1B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-10T12:28:40.127971(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 23, 31, 172, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Falcon-1B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Falcon-1B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-10T12:28:40.127971(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
8bc090fb12dd2221b7cf0d79155112b0cb8a0f1e	<p align="center"> <img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> ## 📰 News * [2024.01.27] 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters. * [2024.01.16] 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy&noteId=OgsxQxAleA). * [2023.12.15] 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have 10M video-text data. We launch LanguageBind_Video 1.5, checking our [model zoo](#-model-zoo). * [2023.12.10] We expand the 💥💥💥 VIDAL dataset and now have 10M depth and 10M thermal data. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months. * [2023.11.27] 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results). * [2023.11.26] 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md). * [2023.11.26] 📣📣📣 We have open-sourced fully fine-tuned Video & Audio, achieving improved performance once again, checking our [model zoo](#-model-zoo). * [2023.11.22] We are about to release a fully fine-tuned version, and the HUGE version is currently undergoing training. * [2023.11.21] 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data. * [2023.11.20] 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders. * [2023.10.23] 🎶 LanguageBind-Audio achieves 🎉🎉🎉state-of-the-art (SOTA) performance on 5 datasets, checking our ✨ [results](#multiple-modalities)! * [2023.10.14] 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint have updated on Huggingface Model Hub! * [2023.10.10] We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described. * [2023.10.07] The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind). * [2023.10.04] Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to watch 👀 this repository for the latest updates. ## 😮 Highlights ### 💡 High performance, but NO intermediate modality required LanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. * The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. ### ⚡️ A multimodal, fully aligned and voluminous dataset We propose VIDAL-10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities. * The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language. ### 🔥 Multi-view enhanced description for training We make multi-view enhancements to language. We produce multi-view description that combines meta-data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language. ## 🤗 Demo * Local demo. Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind. ```bash python gradio_app.py ``` * Online demo. We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image. ## 🛠️ Requirements and Installation * Python >= 3.8 * Pytorch >= 1.13.1 * CUDA Version >= 11.6 * Install required packages: ```bash git clone https://github.com/PKU-YuanGroup/LanguageBind cd LanguageBind pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt ``` ## 🐳 Model Zoo The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version. You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance. <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th> </tr> <tr align="center"> <td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td> </tr> <tr align="center"> <td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td> </tr> <tr align="center"> <td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td> </tr> <tr align="center"> <td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td> </tr> </table> </div> <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th> </tr> <tr align="center"> <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td> </tr> <tr align="center"> <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td> </tr> </table> </div> ## 🤖 API We open source all modalities preprocessing code. If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets! ### Inference for Multi-modal Binding We have provided some sample datasets in [assets](assets) to quickly see how languagebind works. ```python import torch from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer if __name__ == '__main__': device = 'cuda:0' device = torch.device(device) clip_type = { 'video': 'LanguageBind_Video_FT', # also LanguageBind_Video 'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio 'thermal': 'LanguageBind_Thermal', 'image': 'LanguageBind_Image', 'depth': 'LanguageBind_Depth', } model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir') model = model.to(device) model.eval() pretrained_ckpt = f'lb203/LanguageBind_Image' tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir') modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()} image = ['assets/image/0.jpg', 'assets/image/1.jpg'] audio = ['assets/audio/0.wav', 'assets/audio/1.wav'] video = ['assets/video/0.mp4', 'assets/video/1.mp4'] depth = ['assets/depth/0.png', 'assets/depth/1.png'] thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg'] language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.'] inputs = { 'image': to_device(modality_transform['image'](image), device), 'video': to_device(modality_transform['video'](video), device), 'audio': to_device(modality_transform['audio'](audio), device), 'depth': to_device(modality_transform['depth'](depth), device), 'thermal': to_device(modality_transform['thermal'](thermal), device), } inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length', truncation=True, return_tensors='pt'), device) with torch.no_grad(): embeddings = model(inputs) print("Video x Text: \n", torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Image x Text: \n", torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Depth x Text: \n", torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Audio x Text: \n", torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Thermal x Text: \n", torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) ``` Then returns the following result. ```bash Video x Text: [[9.9989331e-01 1.0667283e-04] [1.3255903e-03 9.9867439e-01]] Image x Text: [[9.9990666e-01 9.3292067e-05] [4.6132666e-08 1.0000000e+00]] Depth x Text: [[0.9954276 0.00457235] [0.12042473 0.8795753 ]] Audio x Text: [[0.97634876 0.02365119] [0.02917843 0.97082156]] Thermal x Text: [[0.9482511 0.0517489 ] [0.48746133 0.5125386 ]] ``` ### Emergency zero-shot Since languagebind binds each modality together, we also found the emergency zero-shot. It's very simple to use. ```python print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy()) print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy()) print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) ``` Then, you will get: ``` Video x Audio: [[1.0000000e+00 0.0000000e+00] [3.1150486e-32 1.0000000e+00]] Image x Depth: [[1. 0.] [0. 1.]] Image x Thermal: [[1. 0.] [0. 1.]] ``` ### Different branches for X-Language task Additionally, LanguageBind can be disassembled into different branches to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP. #### Thermal ```python import torch from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal' model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') thermal_process = LanguageBindThermalProcessor(model.config, tokenizer) model.eval() data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt') with torch.no_grad(): out = model(data) print(out.text_embeds @ out.image_embeds.T) ``` #### Depth ```python import torch from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Depth' model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') depth_process = LanguageBindDepthProcessor(model.config, tokenizer) model.eval() data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(data) print(out.text_embeds @ out.image_embeds.T) ``` #### Video ```python import torch from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video' model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') video_process = LanguageBindVideoProcessor(model.config, tokenizer) model.eval() data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(data) print(out.text_embeds @ out.image_embeds.T) ``` #### Audio ```python import torch from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio' model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') audio_process = LanguageBindAudioProcessor(model.config, tokenizer) model.eval() data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt') with torch.no_grad(): out = model(data) print(out.text_embeds @ out.image_embeds.T) ``` #### Image Note that our image encoder is the same as OpenCLIP. Not as fine-tuned as other modalities. ```python import torch from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Image' model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') image_process = LanguageBindImageProcessor(model.config, tokenizer) model.eval() data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(*data) print(out.text_embeds @ out.image_embeds.T) ``` ## 💥 VIDAL-10M The datasets is in [DATASETS.md](DATASETS.md). ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework. * [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework. * [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images. * [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images. ## 🔒 License * The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file. * The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @misc{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan}, year={2023}, eprint={2310.01852}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## ✨ Star History [![Star History](https://api.star-history.com/svg?repos=PKU-YuanGroup/LanguageBind&type=Date)](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" /> </a>	LanguageBind/VIDAL-Depth-Thermal	[ "license:mit", "arxiv:2310.01852", "region:us" ]	2023-12-10T12:37:49+00:00	{"license": "mit"}	2024-02-01T06:58:31+00:00	[ "2310.01852" ]	[]	TAGS #license-mit #arxiv-2310.01852 #region-us	<p align="center"> <img src="URL width="250" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="URL【ICLR 2024 】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> ## News * [2024.01.27] Our MoE-LLaVA is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters. * [2024.01.16] Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) here. * [2023.12.15] We expand the VIDAL dataset and now have 10M video-text data. We launch LanguageBind_Video 1.5, checking our model zoo. * [2023.12.10] We expand the VIDAL dataset and now have 10M depth and 10M thermal data. We are in the process of uploading thermal and depth data on Hugging Face and expect the whole process to last 1-2 months. * [2023.11.27] We have updated our paper with emergency zero-shot results., checking our results. * [2023.11.26] We have open-sourced all textual sources and corresponding YouTube IDs here. * [2023.11.26] We have open-sourced fully fine-tuned Video & Audio, achieving improved performance once again, checking our model zoo. * [2023.11.22] We are about to release a fully fine-tuned version, and the HUGE version is currently undergoing training. * [2023.11.21] We are releasing sample data in URL so that individuals who are interested can further modify the code to train it on their own data. * [2023.11.20] Video-LLaVA builds a large visual-language model to achieve SOTA performances based on LanguageBind encoders. * [2023.10.23] LanguageBind-Audio achieves state-of-the-art (SOTA) performance on 5 datasets, checking our results! * [2023.10.14] Released a stronger LanguageBind-Video, checking our results! The video checkpoint have updated on Huggingface Model Hub! * [2023.10.10] We provide sample data, which can be found in assets, and emergency zero-shot usage is described. * [2023.10.07] The checkpoints are available on Huggingface Model. * [2023.10.04] Code and demo are available now! Welcome to watch this repository for the latest updates. ## Highlights ### High performance, but NO intermediate modality required LanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. * The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. ### ️ A multimodal, fully aligned and voluminous dataset We propose VIDAL-10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities. * The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language. ### Multi-view enhanced description for training We make multi-view enhancements to language. We produce multi-view description that combines meta-data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language. ## Demo * Local demo. Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind. * Online demo. We provide the online demo in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image. ## ️ Requirements and Installation * Python >= 3.8 * Pytorch >= 1.13.1 * CUDA Version >= 11.6 * Install required packages: ## Model Zoo The names in the table represent different encoder models. For example, 'LanguageBind/LanguageBind_Video_FT' represents the fully fine-tuned version, while 'LanguageBind/LanguageBind_Video' represents the LoRA-tuned version. You can freely replace them in the recommended API usage. We recommend using the fully fine-tuned version, as it offers stronger performance. <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th> </tr> <tr align="center"> <td>Video</td><td><a href="URL href="URL </tr> <tr align="center"> <td>Audio</td><td><a href="URL href="URL </tr> <tr align="center"> <td>Depth</td><td><a href="URL </tr> <tr align="center"> <td>Thermal</td><td><a href="URL </tr> </table> </div> <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th> </tr> <tr align="center"> <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="URL </tr> <tr align="center"> <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="URL </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="URL </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="URL </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td> </tr> </table> </div> ## API We open source all modalities preprocessing code. If you want to load the model (e.g. ) from the model hub on Huggingface or on local, you can use the following code snippets! ### Inference for Multi-modal Binding We have provided some sample datasets in assets to quickly see how languagebind works. Then returns the following result. ### Emergency zero-shot Since languagebind binds each modality together, we also found the emergency zero-shot. It's very simple to use. Then, you will get: ### Different branches for X-Language task Additionally, LanguageBind can be disassembled into different branches to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP. #### Thermal #### Depth #### Video #### Audio #### Image Note that our image encoder is the same as OpenCLIP. Not as fine-tuned as other modalities. ## VIDAL-10M The datasets is in URL. ## ️ Training & Validating The training & validating instruction is in TRAIN_AND_VALIDATE.md. ## Acknowledgement * OpenCLIP An open source pretraining framework. * CLIP4Clip An open source Video-Text retrieval framework. * sRGB-TIR An open source framework to generate infrared (thermal) images. * GLPN An open source framework to generate depth images. ## License * The majority of this project is released under the MIT license as found in the LICENSE file. * The dataset of this project is released under the CC-BY-NC 4.0 license as found in the DATASET_LICENSE file. ## ️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ## Star History ![Star History](URL ## Contributors <a href="URL <img src="URL /> </a>	[ "## News\n* [2024.01.27] Our MoE-LLaVA is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters.\n* [2024.01.16] Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) here.\n* [2023.12.15] We expand the VIDAL dataset and now have 10M video-text data. We launch LanguageBind_Video 1.5, checking our model zoo. \n* [2023.12.10] We expand the VIDAL dataset and now have 10M depth and 10M thermal data. We are in the process of uploading thermal and depth data on Hugging Face and expect the whole process to last 1-2 months.\n* [2023.11.27] We have updated our paper with emergency zero-shot results., checking our results.\n* [2023.11.26] We have open-sourced all textual sources and corresponding YouTube IDs here.\n* [2023.11.26] We have open-sourced fully fine-tuned Video & Audio, achieving improved performance once again, checking our model zoo. \n* [2023.11.22] We are about to release a fully fine-tuned version, and the HUGE version is currently undergoing training.\n* [2023.11.21] We are releasing sample data in URL so that individuals who are interested can further modify the code to train it on their own data.\n* [2023.11.20] Video-LLaVA builds a large visual-language model to achieve SOTA performances based on LanguageBind encoders.\n* [2023.10.23] LanguageBind-Audio achieves state-of-the-art (SOTA) performance on 5 datasets, checking our results!\n* [2023.10.14] Released a stronger LanguageBind-Video, checking our results! The video checkpoint have updated on Huggingface Model Hub!\n* [2023.10.10] We provide sample data, which can be found in assets, and emergency zero-shot usage is described. \n* [2023.10.07] The checkpoints are available on Huggingface Model.\n* [2023.10.04] Code and demo are available now! Welcome to watch this repository for the latest updates.", "## Highlights", "### High performance, but NO intermediate modality required\nLanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. \n* The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.", "### ️ A multimodal, fully aligned and voluminous dataset\nWe propose VIDAL-10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities.\n* The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.", "### Multi-view enhanced description for training\nWe make multi-view enhancements to language. We produce multi-view description that combines meta-data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language.", "## Demo\n\n* Local demo. Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.\n\n\n* Online demo. We provide the online demo in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.", "## ️ Requirements and Installation\n* Python >= 3.8\n* Pytorch >= 1.13.1\n* CUDA Version >= 11.6\n* Install required packages:", "## Model Zoo\n\nThe names in the table represent different encoder models. For example, 'LanguageBind/LanguageBind_Video_FT' represents the fully fine-tuned version, while 'LanguageBind/LanguageBind_Video' represents the LoRA-tuned version. \n\nYou can freely replace them in the recommended API usage. We recommend using the fully fine-tuned version, as it offers stronger performance.\n\n<div align=\"center\">\n<table border=\"1\" width=\"100%\">\n <tr align=\"center\">\n <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th>\n </tr>\n <tr align=\"center\">\n <td>Video</td><td><a href=\"URL href=\"URL\n </tr>\n <tr align=\"center\">\n <td>Audio</td><td><a href=\"URL href=\"URL\n </tr>\n <tr align=\"center\">\n <td>Depth</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>Thermal</td><td><a href=\"URL\n </tr>\n</table>\n</div>\n\n\n<div align=\"center\">\n<table border=\"1\" width=\"100%\">\n <tr align=\"center\">\n <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th>\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td>\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td>\n </tr>\n</table>\n</div>", "## API\nWe open source all modalities preprocessing code. If you want to load the model (e.g. ) from the model hub on Huggingface or on local, you can use the following code snippets!", "### Inference for Multi-modal Binding \nWe have provided some sample datasets in assets to quickly see how languagebind works.\n\nThen returns the following result.", "### Emergency zero-shot\nSince languagebind binds each modality together, we also found the emergency zero-shot. It's very simple to use.\n\nThen, you will get:", "### Different branches for X-Language task\nAdditionally, LanguageBind can be disassembled into different branches to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP.", "#### Thermal", "#### Depth", "#### Video", "#### Audio", "#### Image\nNote that our image encoder is the same as OpenCLIP. Not as fine-tuned as other modalities.", "## VIDAL-10M\nThe datasets is in URL.", "## ️ Training & Validating\nThe training & validating instruction is in TRAIN_AND_VALIDATE.md.", "## Acknowledgement\n* OpenCLIP An open source pretraining framework.\n* CLIP4Clip An open source Video-Text retrieval framework.\n* sRGB-TIR An open source framework to generate infrared (thermal) images.\n* GLPN An open source framework to generate depth images.", "## License\n* The majority of this project is released under the MIT license as found in the LICENSE file.\n* The dataset of this project is released under the CC-BY-NC 4.0 license as found in the DATASET_LICENSE file.", "## ️ Citation\nIf you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.", "## Star History\n\n![Star History](URL", "## Contributors\n\n<a href=\"URL\n <img src=\"URL />\n</a>" ]	[ "TAGS\n#license-mit #arxiv-2310.01852 #region-us \n", "## News\n* [2024.01.27] Our MoE-LLaVA is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters.\n* [2024.01.16] Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) here.\n* [2023.12.15] We expand the VIDAL dataset and now have 10M video-text data. We launch LanguageBind_Video 1.5, checking our model zoo. \n* [2023.12.10] We expand the VIDAL dataset and now have 10M depth and 10M thermal data. We are in the process of uploading thermal and depth data on Hugging Face and expect the whole process to last 1-2 months.\n* [2023.11.27] We have updated our paper with emergency zero-shot results., checking our results.\n* [2023.11.26] We have open-sourced all textual sources and corresponding YouTube IDs here.\n* [2023.11.26] We have open-sourced fully fine-tuned Video & Audio, achieving improved performance once again, checking our model zoo. \n* [2023.11.22] We are about to release a fully fine-tuned version, and the HUGE version is currently undergoing training.\n* [2023.11.21] We are releasing sample data in URL so that individuals who are interested can further modify the code to train it on their own data.\n* [2023.11.20] Video-LLaVA builds a large visual-language model to achieve SOTA performances based on LanguageBind encoders.\n* [2023.10.23] LanguageBind-Audio achieves state-of-the-art (SOTA) performance on 5 datasets, checking our results!\n* [2023.10.14] Released a stronger LanguageBind-Video, checking our results! The video checkpoint have updated on Huggingface Model Hub!\n* [2023.10.10] We provide sample data, which can be found in assets, and emergency zero-shot usage is described. \n* [2023.10.07] The checkpoints are available on Huggingface Model.\n* [2023.10.04] Code and demo are available now! Welcome to watch this repository for the latest updates.", "## Highlights", "### High performance, but NO intermediate modality required\nLanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. \n* The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.", "### ️ A multimodal, fully aligned and voluminous dataset\nWe propose VIDAL-10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities.\n* The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.", "### Multi-view enhanced description for training\nWe make multi-view enhancements to language. We produce multi-view description that combines meta-data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language.", "## Demo\n\n* Local demo. Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.\n\n\n* Online demo. We provide the online demo in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.", "## ️ Requirements and Installation\n* Python >= 3.8\n* Pytorch >= 1.13.1\n* CUDA Version >= 11.6\n* Install required packages:", "## Model Zoo\n\nThe names in the table represent different encoder models. For example, 'LanguageBind/LanguageBind_Video_FT' represents the fully fine-tuned version, while 'LanguageBind/LanguageBind_Video' represents the LoRA-tuned version. \n\nYou can freely replace them in the recommended API usage. We recommend using the fully fine-tuned version, as it offers stronger performance.\n\n<div align=\"center\">\n<table border=\"1\" width=\"100%\">\n <tr align=\"center\">\n <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th>\n </tr>\n <tr align=\"center\">\n <td>Video</td><td><a href=\"URL href=\"URL\n </tr>\n <tr align=\"center\">\n <td>Audio</td><td><a href=\"URL href=\"URL\n </tr>\n <tr align=\"center\">\n <td>Depth</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>Thermal</td><td><a href=\"URL\n </tr>\n</table>\n</div>\n\n\n<div align=\"center\">\n<table border=\"1\" width=\"100%\">\n <tr align=\"center\">\n <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th>\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td>\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href=\"URL\n </tr>\n <tr align=\"center\">\n <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td>\n </tr>\n</table>\n</div>", "## API\nWe open source all modalities preprocessing code. If you want to load the model (e.g. ) from the model hub on Huggingface or on local, you can use the following code snippets!", "### Inference for Multi-modal Binding \nWe have provided some sample datasets in assets to quickly see how languagebind works.\n\nThen returns the following result.", "### Emergency zero-shot\nSince languagebind binds each modality together, we also found the emergency zero-shot. It's very simple to use.\n\nThen, you will get:", "### Different branches for X-Language task\nAdditionally, LanguageBind can be disassembled into different branches to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP.", "#### Thermal", "#### Depth", "#### Video", "#### Audio", "#### Image\nNote that our image encoder is the same as OpenCLIP. Not as fine-tuned as other modalities.", "## VIDAL-10M\nThe datasets is in URL.", "## ️ Training & Validating\nThe training & validating instruction is in TRAIN_AND_VALIDATE.md.", "## Acknowledgement\n* OpenCLIP An open source pretraining framework.\n* CLIP4Clip An open source Video-Text retrieval framework.\n* sRGB-TIR An open source framework to generate infrared (thermal) images.\n* GLPN An open source framework to generate depth images.", "## License\n* The majority of this project is released under the MIT license as found in the LICENSE file.\n* The dataset of this project is released under the CC-BY-NC 4.0 license as found in the DATASET_LICENSE file.", "## ️ Citation\nIf you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.", "## Star History\n\n![Star History](URL", "## Contributors\n\n<a href=\"URL\n <img src=\"URL />\n</a>" ]	[ 20, 506, 3, 95, 91, 77, 83, 37, 801, 49, 37, 41, 52, 5, 5, 3, 3, 29, 13, 27, 66, 53, 33, 10, 22 ]	[ "passage: TAGS\n#license-mit #arxiv-2310.01852 #region-us \n", "passage: ## News\n* [2024.01.27] Our MoE-LLaVA is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters.\n* [2024.01.16] Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) here.\n* [2023.12.15] We expand the VIDAL dataset and now have 10M video-text data. We launch LanguageBind_Video 1.5, checking our model zoo. \n* [2023.12.10] We expand the VIDAL dataset and now have 10M depth and 10M thermal data. We are in the process of uploading thermal and depth data on Hugging Face and expect the whole process to last 1-2 months.\n* [2023.11.27] We have updated our paper with emergency zero-shot results., checking our results.\n* [2023.11.26] We have open-sourced all textual sources and corresponding YouTube IDs here.\n* [2023.11.26] We have open-sourced fully fine-tuned Video & Audio, achieving improved performance once again, checking our model zoo. \n* [2023.11.22] We are about to release a fully fine-tuned version, and the HUGE version is currently undergoing training.\n* [2023.11.21] We are releasing sample data in URL so that individuals who are interested can further modify the code to train it on their own data.\n* [2023.11.20] Video-LLaVA builds a large visual-language model to achieve SOTA performances based on LanguageBind encoders.\n* [2023.10.23] LanguageBind-Audio achieves state-of-the-art (SOTA) performance on 5 datasets, checking our results!\n* [2023.10.14] Released a stronger LanguageBind-Video, checking our results! The video checkpoint have updated on Huggingface Model Hub!\n* [2023.10.10] We provide sample data, which can be found in assets, and emergency zero-shot usage is described. \n* [2023.10.07] The checkpoints are available on Huggingface Model.\n* [2023.10.04] Code and demo are available now! Welcome to watch this repository for the latest updates.## Highlights### High performance, but NO intermediate modality required\nLanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. \n* The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.### ️ A multimodal, fully aligned and voluminous dataset\nWe propose VIDAL-10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities.\n* The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.### Multi-view enhanced description for training\nWe make multi-view enhancements to language. We produce multi-view description that combines meta-data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language.## Demo\n\n* Local demo. Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.\n\n\n* Online demo. We provide the online demo in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.## ️ Requirements and Installation\n* Python >= 3.8\n* Pytorch >= 1.13.1\n* CUDA Version >= 11.6\n* Install required packages:" ]
5f920a709473cc51bff3900755d930aa9c943a36	# Dataset Card for "ds_rplan_full_rplanpy_category" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ekuhn/ds_rplan_full_rplanpy_category	[ "region:us" ]	2023-12-10T12:42:49+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "img", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}, {"name": "num_rooms", "dtype": "int64"}], "splits": [{"name": "full", "num_bytes": 68462137, "num_examples": 80788}], "download_size": 36343949, "dataset_size": 68462137}}	2023-12-10T12:42:58+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ds_rplan_full_rplanpy_category" More Information needed	[ "# Dataset Card for \"ds_rplan_full_rplanpy_category\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ds_rplan_full_rplanpy_category\"\n\nMore Information needed" ]	[ 6, 24 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ds_rplan_full_rplanpy_category\"\n\nMore Information needed" ]
5fb51c15be815b98663501737c9aa3f69bb106f1	## Factorio Blueprint Visualizations SDXL Lora Examples Examples of the usage of https://huggingface.co/piebro/factorio-blueprint-visualizations-sdxl-lora. The images are generated using 25 inference steps and a guidance_scale of 7. The filenames are composed like this: {counter}\_{seed}\_{prompt}.png.	piebro/factorio-blueprint-visualizations-sdxl-lora-examples	[ "license:cc0-1.0", "region:us" ]	2023-12-10T13:25:51+00:00	{"license": "cc0-1.0", "pretty_name": "Factorio Blueprint Visualizations SDXL Lora Examples"}	2023-12-10T13:52:02+00:00	[]	[]	TAGS #license-cc0-1.0 #region-us	## Factorio Blueprint Visualizations SDXL Lora Examples Examples of the usage of URL The images are generated using 25 inference steps and a guidance_scale of 7. The filenames are composed like this: {counter}\_{seed}\_{prompt}.png.	[ "## Factorio Blueprint Visualizations SDXL Lora Examples\n\nExamples of the usage of URL The images are generated using 25 inference steps and a guidance_scale of 7. The filenames are composed like this: {counter}\\_{seed}\\_{prompt}.png." ]	[ "TAGS\n#license-cc0-1.0 #region-us \n", "## Factorio Blueprint Visualizations SDXL Lora Examples\n\nExamples of the usage of URL The images are generated using 25 inference steps and a guidance_scale of 7. The filenames are composed like this: {counter}\\_{seed}\\_{prompt}.png." ]	[ 14, 69 ]	[ "passage: TAGS\n#license-cc0-1.0 #region-us \n## Factorio Blueprint Visualizations SDXL Lora Examples\n\nExamples of the usage of URL The images are generated using 25 inference steps and a guidance_scale of 7. The filenames are composed like this: {counter}\\_{seed}\\_{prompt}.png." ]
f08c14a945425c57b91aff03c0c4707a53262068	# Dataset Card for "alpaca-gpt4" This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. The dataset was originaly shared in this repository: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM. This is just a wraper for compatibility with huggingface's datasets library. ## Dataset Description - Homepage: https://instruction-tuning-with-gpt-4.github.io - Repository: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM - Paper: https://arxiv.org/abs/2304.03277 ## Dataset structure It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca. The dataset has the same format as Alpaca data, except the output is generated by GPT-4: - `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique. - `input`: `str`, optional context or input for the task. - `output`: `str`, the answer to the instruction as generated by `GPT-4`. - `text`: `str`, all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig. ## Difference with the original Alpaca dataset The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example: #### Example from Alpaca-GPT4: ```bash {'instruction': 'Identify the odd one out.', 'input': 'Twitter, Instagram, Telegram', 'output': 'The odd one out is Telegram. Twitter and Instagram are social media platforms mainly for sharing information, images and videos while Telegram is a cloud-based instant messaging and voice-over-IP service.', 'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nIdentify the odd one out.\n\n### Input:\nTwitter, Instagram, Telegram\n\n### Response:\nThe odd one out is Telegram. Twitter and Instagram are social media platforms mainly for sharing information, images and videos while Telegram is a cloud-based instant messaging and voice-over-IP service.'} ``` #### Same example from original Alpaca: ```bash {'instruction': 'Identify the odd one out.', 'input': 'Twitter, Instagram, Telegram', 'output': 'Telegram', 'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nIdentify the odd one out.\n\n### Input:\nTwitter, Instagram, Telegram\n\n### Response:\nTelegram'} ``` ## Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).	walkernet/test	[ "task_categories:text-generation", "task_categories:conversational", "task_categories:question-answering", "size_categories:10K<n<100K", "language:ru", "license:cc-by-nc-4.0", "gpt4", "alpaca", "instruction-finetuning", "arxiv:2304.03277", "region:us" ]	2023-12-10T13:53:58+00:00	{"language": ["ru"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "conversational", "question-answering"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 88566301, "num_examples": 52002}], "download_size": 48393562, "dataset_size": 88566301}, "tags": ["gpt4", "alpaca", "instruction-finetuning"]}	2023-12-10T18:07:52+00:00	[ "2304.03277" ]	[ "ru" ]	TAGS #task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-Russian #license-cc-by-nc-4.0 #gpt4 #alpaca #instruction-finetuning #arxiv-2304.03277 #region-us	# Dataset Card for "alpaca-gpt4" This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. The dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library. ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL ## Dataset structure It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca. The dataset has the same format as Alpaca data, except the output is generated by GPT-4: - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique. - 'input': 'str', optional context or input for the task. - 'output': 'str', the answer to the instruction as generated by 'GPT-4'. - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig. ## Difference with the original Alpaca dataset The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example: #### Example from Alpaca-GPT4: #### Same example from original Alpaca: ## Licensing Information The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).	[ "# Dataset Card for \"alpaca-gpt4\"\n\nThis dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.\n\nThe dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library.", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "## Dataset structure\n\nIt contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.\nThe dataset has the same format as Alpaca data, except the output is generated by GPT-4:\n\n - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.\n - 'input': 'str', optional context or input for the task. \n - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.\n - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.", "## Difference with the original Alpaca dataset\n\nThe original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:", "#### Example from Alpaca-GPT4:", "#### Same example from original Alpaca:", "## Licensing Information\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0)." ]	[ "TAGS\n#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-Russian #license-cc-by-nc-4.0 #gpt4 #alpaca #instruction-finetuning #arxiv-2304.03277 #region-us \n", "# Dataset Card for \"alpaca-gpt4\"\n\nThis dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.\n\nThe dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library.", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "## Dataset structure\n\nIt contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.\nThe dataset has the same format as Alpaca data, except the output is generated by GPT-4:\n\n - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.\n - 'input': 'str', optional context or input for the task. \n - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.\n - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.", "## Difference with the original Alpaca dataset\n\nThe original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:", "#### Example from Alpaca-GPT4:", "#### Same example from original Alpaca:", "## Licensing Information\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0)." ]	[ 89, 80, 18, 159, 73, 12, 9, 25 ]	[ "passage: TAGS\n#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-Russian #license-cc-by-nc-4.0 #gpt4 #alpaca #instruction-finetuning #arxiv-2304.03277 #region-us \n# Dataset Card for \"alpaca-gpt4\"\n\nThis dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.\n\nThe dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library.## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL## Dataset structure\n\nIt contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.\nThe dataset has the same format as Alpaca data, except the output is generated by GPT-4:\n\n - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.\n - 'input': 'str', optional context or input for the task. \n - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.\n - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.## Difference with the original Alpaca dataset\n\nThe original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:#### Example from Alpaca-GPT4:#### Same example from original Alpaca:## Licensing Information\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0)." ]
3d1d81399092a46968b946271198cdecdc171209	# Dataset Card for Dataset Name ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> Each instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Repository: [https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024/tree/main]	kietnt0603/SemEval2024-STR	[ "size_categories:10K<n<100K", "language:am", "language:ha", "language:en", "language:es", "language:te", "language:ar", "language:af", "license:mit", "Semantic Textual Relatedness", "region:us" ]	2023-12-10T14:10:33+00:00	{"language": ["am", "ha", "en", "es", "te", "ar", "af"], "license": "mit", "size_categories": ["10K<n<100K"], "tags": ["Semantic Textual Relatedness"], "dataset_info": {"features": [{"name": "PairID", "dtype": "string"}, {"name": "Language", "dtype": "string"}, {"name": "Sentence1", "dtype": "string"}, {"name": "Sentence2", "dtype": "string"}, {"name": "Length", "dtype": "int64"}, {"name": "Score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 4248215, "num_examples": 15123}, {"name": "dev", "num_bytes": 460985, "num_examples": 1390}], "download_size": 2400795, "dataset_size": 4709200}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "dev", "path": "data/dev-"}]}]}	2023-12-20T11:05:00+00:00	[]	[ "am", "ha", "en", "es", "te", "ar", "af" ]	TAGS #size_categories-10K<n<100K #language-Amharic #language-Hausa #language-English #language-Spanish #language-Telugu #language-Arabic #language-Afrikaans #license-mit #Semantic Textual Relatedness #region-us	# Dataset Card for Dataset Name ## Dataset Details ### Dataset Description Each instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper. ### Dataset Sources - Repository: [URL	[ "# Dataset Card for Dataset Name", "## Dataset Details", "### Dataset Description\n\n\nEach instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper.", "### Dataset Sources\n\n\n\n- Repository: [URL" ]	[ "TAGS\n#size_categories-10K<n<100K #language-Amharic #language-Hausa #language-English #language-Spanish #language-Telugu #language-Arabic #language-Afrikaans #license-mit #Semantic Textual Relatedness #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Details", "### Dataset Description\n\n\nEach instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper.", "### Dataset Sources\n\n\n\n- Repository: [URL" ]	[ 65, 8, 4, 185, 13 ]	[ "passage: TAGS\n#size_categories-10K<n<100K #language-Amharic #language-Hausa #language-English #language-Spanish #language-Telugu #language-Arabic #language-Afrikaans #license-mit #Semantic Textual Relatedness #region-us \n# Dataset Card for Dataset Name## Dataset Details### Dataset Description\n\n\nEach instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper.### Dataset Sources\n\n\n\n- Repository: [URL" ]
234777868459a272a26a04bb8882764b6b873dc5	![logo](images/logo_v3.png) Il s'agit des pdfs preparsés qui peuvent être ensuite utilisé dans des appli autour du NLP / LLMs dans un soucis de collaborations. Les différents codes ont été extrait en format XML ici : https://codes.droit.org/ Les formats XML permet de faire un meilleurs preprocessing des codes de loi. La structure des données : - dans raw/ on retrouve les différents codes en format xml. - dans notebooks_preprocess/ on retrouve les différents notebooks qui ont permis de constitué le dataset final.	Forbu14/LoiLibre	[ "language:fr", "license:apache-2.0", "legal", "region:us" ]	2023-12-10T14:14:54+00:00	{"language": ["fr"], "license": "apache-2.0", "pretty_name": "LoiLibre", "tags": ["legal"]}	2023-12-10T19:11:24+00:00	[]	[ "fr" ]	TAGS #language-French #license-apache-2.0 #legal #region-us	!logo Il s'agit des pdfs preparsés qui peuvent être ensuite utilisé dans des appli autour du NLP / LLMs dans un soucis de collaborations. Les différents codes ont été extrait en format XML ici : URL Les formats XML permet de faire un meilleurs preprocessing des codes de loi. La structure des données : - dans raw/ on retrouve les différents codes en format xml. - dans notebooks_preprocess/ on retrouve les différents notebooks qui ont permis de constitué le dataset final.	[]	[ "TAGS\n#language-French #license-apache-2.0 #legal #region-us \n" ]	[ 22 ]	[ "passage: TAGS\n#language-French #license-apache-2.0 #legal #region-us \n" ]
a7956e63162d4a550e188ccf486ff4abb2b13523	# Dataset Card for "rapidapi-example-responses-workflows" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses-workflows	[ "region:us" ]	2023-12-10T14:19:02+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "steps", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3227895, "num_examples": 1000}], "download_size": 1173873, "dataset_size": 3227895}}	2023-12-11T22:19:14+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses-workflows" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses-workflows\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses-workflows\"\n\nMore Information needed" ]	[ 6, 24 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses-workflows\"\n\nMore Information needed" ]
80e8d667d5cc28e827d880f6723ed373d8bcc3f0	# InstructImages Following dataset created in Dalle3 paper style 1. Caption all images with LVM(Llava13b in my case) 2. Improve captions with GPT4 Also i have a plans to open source RLAIF pipeline with these images.	AlexWortega/InstructCaptions2	[ "language:en", "license:apache-2.0", "region:us" ]	2023-12-10T14:33:07+00:00	{"language": ["en"], "license": "apache-2.0", "pretty_name": "InstructImages", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33059118217.928, "num_examples": 22776}], "download_size": 33273147003, "dataset_size": 33059118217.928}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-10T15:07:33+00:00	[]	[ "en" ]	TAGS #language-English #license-apache-2.0 #region-us	# InstructImages Following dataset created in Dalle3 paper style 1. Caption all images with LVM(Llava13b in my case) 2. Improve captions with GPT4 Also i have a plans to open source RLAIF pipeline with these images.	[ "# InstructImages\nFollowing dataset created in Dalle3 paper style \n1. Caption all images with LVM(Llava13b in my case)\n2. Improve captions with GPT4\n\n\nAlso i have a plans to open source RLAIF pipeline with these images." ]	[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "# InstructImages\nFollowing dataset created in Dalle3 paper style \n1. Caption all images with LVM(Llava13b in my case)\n2. Improve captions with GPT4\n\n\nAlso i have a plans to open source RLAIF pipeline with these images." ]	[ 18, 59 ]	[ "passage: TAGS\n#language-English #license-apache-2.0 #region-us \n# InstructImages\nFollowing dataset created in Dalle3 paper style \n1. Caption all images with LVM(Llava13b in my case)\n2. Improve captions with GPT4\n\n\nAlso i have a plans to open source RLAIF pipeline with these images." ]
48b8fbf7e914957b5fd58ae98047de795333a5aa	Vtuber tachi-e dataset sclaped from official site Nijisanji Hololive Vspo Noripro 774inc	junjuice0/vtuber-tachi-e	[ "size_categories:n<1K", "art", "region:us" ]	2023-12-10T14:37:48+00:00	{"size_categories": ["n<1K"], "tags": ["art"]}	2023-12-10T15:21:07+00:00	[]	[]	TAGS #size_categories-n<1K #art #region-us	Vtuber tachi-e dataset sclaped from official site Nijisanji Hololive Vspo Noripro 774inc	[]	[ "TAGS\n#size_categories-n<1K #art #region-us \n" ]	[ 18 ]	[ "passage: TAGS\n#size_categories-n<1K #art #region-us \n" ]
f1e5d865006c5422ae47bc6bd5eb6715d78f2004	# Dataset Card for "rapidapi-example-responses-workflow-steps" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidfant/rapidapi-example-responses-workflow-steps	[ "region:us" ]	2023-12-10T14:42:05+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "workflow", "dtype": "string"}, {"name": "plan", "dtype": "string"}, {"name": "reasoning", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "data", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24204115, "num_examples": 6018}], "download_size": 2084270, "dataset_size": 24204115}}	2023-12-12T01:18:02+00:00	[]	[]	TAGS #region-us	# Dataset Card for "rapidapi-example-responses-workflow-steps" More Information needed	[ "# Dataset Card for \"rapidapi-example-responses-workflow-steps\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"rapidapi-example-responses-workflow-steps\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"rapidapi-example-responses-workflow-steps\"\n\nMore Information needed" ]

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