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8e7bc0003ff4e7a62e46a017a373c9475a3af232 | # [doc] image dataset 7
This dataset contains 2 jpeg files in the `red` directory and 2 jpeg files in the `green` directory. | polinaeterna/doc-image-7 | [
"size_categories:n<1K",
"region:us"
] | 2023-12-04T17:26:12+00:00 | {"size_categories": ["n<1K"], "configs": [{"config_name": "default", "drop_labels": false, "drop_metadata": false}]} | 2023-12-04T17:33:22+00:00 | [] | [] | TAGS
#size_categories-n<1K #region-us
| # [doc] image dataset 7
This dataset contains 2 jpeg files in the 'red' directory and 2 jpeg files in the 'green' directory. | [
"# [doc] image dataset 7\n\nThis dataset contains 2 jpeg files in the 'red' directory and 2 jpeg files in the 'green' directory."
] | [
"TAGS\n#size_categories-n<1K #region-us \n",
"# [doc] image dataset 7\n\nThis dataset contains 2 jpeg files in the 'red' directory and 2 jpeg files in the 'green' directory."
] | [
16,
37
] | [
"passage: TAGS\n#size_categories-n<1K #region-us \n# [doc] image dataset 7\n\nThis dataset contains 2 jpeg files in the 'red' directory and 2 jpeg files in the 'green' directory."
] |
400ad1089837eb8bae19ac5c3286ae8f75004d38 |
# Dataset Card for Evaluation run of openaccess-ai-collective/DPOpenHermes-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/DPOpenHermes-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 [openaccess-ai-collective/DPOpenHermes-7B](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-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 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_openaccess-ai-collective__DPOpenHermes-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:43:16.068019](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B/blob/main/results_2023-12-04T17-43-16.068019.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": 0.6408628483411186,
"acc_stderr": 0.03219894205377224,
"acc_norm": 0.6438076616391943,
"acc_norm_stderr": 0.032835537242155946,
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"mc1_stderr": 0.017097248285233065,
"mc2": 0.5692137581021863,
"mc2_stderr": 0.015366764842114067
},
"harness|arc:challenge|25": {
"acc": 0.621160409556314,
"acc_stderr": 0.014175915490000324,
"acc_norm": 0.659556313993174,
"acc_norm_stderr": 0.01384746051889298
},
"harness|hellaswag|10": {
"acc": 0.6750647281418044,
"acc_stderr": 0.004673934837150448,
"acc_norm": 0.8589922326229835,
"acc_norm_stderr": 0.0034731828909689687
},
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},
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"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
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},
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"acc_norm_stderr": 0.049431107042371025
},
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},
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},
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},
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"harness|hendrycksTest-professional_accounting|5": {
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},
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"acc_stderr": 0.013708494995677646
}
}
```
### 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_openaccess-ai-collective__DPOpenHermes-7B | [
"region:us"
] | 2023-12-04T17:28:26+00:00 | {"pretty_name": "Evaluation run of openaccess-ai-collective/DPOpenHermes-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [openaccess-ai-collective/DPOpenHermes-7B](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-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 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_openaccess-ai-collective__DPOpenHermes-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T17:43:16.068019](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B/blob/main/results_2023-12-04T17-43-16.068019.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\": 0.6408628483411186,\n \"acc_stderr\": 0.03219894205377224,\n \"acc_norm\": 0.6438076616391943,\n \"acc_norm_stderr\": 0.032835537242155946,\n \"mc1\": 0.3929008567931457,\n \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5692137581021863,\n \"mc2_stderr\": 0.015366764842114067\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.621160409556314,\n \"acc_stderr\": 0.014175915490000324,\n \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.01384746051889298\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6750647281418044,\n \"acc_stderr\": 0.004673934837150448,\n \"acc_norm\": 0.8589922326229835,\n \"acc_norm_stderr\": 0.0034731828909689687\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\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\": 0.6842105263157895,\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\": 0.59,\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\": 0.6754716981132075,\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\": 0.7777777777777778,\n \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.034765901043041336\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\": 0.5,\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\": 0.28,\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\": 0.6589595375722543,\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\": 0.4019607843137255,\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\": 0.76,\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\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\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\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\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\": 0.39,\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\": 0.7806451612903226,\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\": 0.4876847290640394,\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\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.030874145136562076,\n \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.030874145136562076\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\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121437,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121437\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.617948717948718,\n \"acc_stderr\": 0.024635549163908234,\n \"acc_norm\": 0.617948717948718,\n \"acc_norm_stderr\": 0.024635549163908234\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\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\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\": 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099857,\n \"acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099857\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\": 0.7990196078431373,\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\": 0.8227848101265823,\n \"acc_stderr\": 0.02485636418450322,\n \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.02485636418450322\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.03076935200822914\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\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\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\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\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\": 0.8058252427184466,\n \"acc_stderr\": 0.039166677628225836,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.039166677628225836\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.02250903393707781\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\": 0.8250319284802043,\n \"acc_stderr\": 0.013586619219903347,\n \"acc_norm\": 0.8250319284802043,\n \"acc_norm_stderr\": 0.013586619219903347\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3240223463687151,\n \"acc_stderr\": 0.015652542496421114,\n \"acc_norm\": 0.3240223463687151,\n \"acc_norm_stderr\": 0.015652542496421114\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\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\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4595827900912647,\n \"acc_stderr\": 0.012728446067669975,\n \"acc_norm\": 0.4595827900912647,\n \"acc_norm_stderr\": 0.012728446067669975\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396553,\n \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396553\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6552287581699346,\n \"acc_stderr\": 0.01922832201869664,\n \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.01922832201869664\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\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\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\": 0.85,\n \"acc_stderr\": 0.035887028128263686,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263686\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3929008567931457,\n \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5692137581021863,\n \"mc2_stderr\": 0.015366764842114067\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5481425322213799,\n \"acc_stderr\": 0.013708494995677646\n }\n}\n```", "repo_url": "https://huggingface.co/openaccess-ai-collective/DPOpenHermes-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_04T17_25_36.018483", "path": ["**/details_harness|arc:challenge|25_2023-12-04T17-25-36.018483.parquet"]}, {"split": "2023_12_04T17_43_16.068019", "path": ["**/details_harness|arc:challenge|25_2023-12-04T17-43-16.068019.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T17-43-16.068019.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T17_25_36.018483", "path": ["**/details_harness|gsm8k|5_2023-12-04T17-25-36.018483.parquet"]}, {"split": "2023_12_04T17_43_16.068019", "path": ["**/details_harness|gsm8k|5_2023-12-04T17-43-16.068019.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T17-43-16.068019.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T17_25_36.018483", "path": ["**/details_harness|hellaswag|10_2023-12-04T17-25-36.018483.parquet"]}, {"split": "2023_12_04T17_43_16.068019", "path": ["**/details_harness|hellaswag|10_2023-12-04T17-43-16.068019.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T17-43-16.068019.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T17_25_36.018483", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-04T17-25-36.018483.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T17-25-36.018483.parquet", 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#region-us
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# Dataset Card for Evaluation run of openaccess-ai-collective/DPOpenHermes-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 openaccess-ai-collective/DPOpenHermes-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 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 2023-12-04T17:43:16.068019(note that their might be results for other tasks in 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
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] |
0346406d108d77fe5f8faa36d5fa3474c5d67154 | # Dataset Card for "t5_small_test_set_context_len_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yardeny/t5_small_test_set_context_len_512 | [
"region:us"
] | 2023-12-04T17:31:26+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 410880, "num_examples": 160}], "download_size": 178658, "dataset_size": 410880}} | 2023-12-04T17:31:33+00:00 | [] | [] | TAGS
#region-us
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2f9e95b8dfb1214e5ae3d60c81753fb6ea9348f8 |
# Dataset Card for Evaluation run of beberik/Nyxene-11B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beberik/Nyxene-11B
- **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 [beberik/Nyxene-11B](https://huggingface.co/beberik/Nyxene-11B) 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_beberik__Nyxene-11B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:29:47.826048](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-11B/blob/main/results_2023-12-04T17-29-47.826048.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": 0.651058700269482,
"acc_stderr": 0.0320152029210293,
"acc_norm": 0.6547280296636943,
"acc_norm_stderr": 0.03264773034799592,
"mc1": 0.40269277845777235,
"mc1_stderr": 0.01716883093518722,
"mc2": 0.5749990941717074,
"mc2_stderr": 0.015569738564249067
},
"harness|arc:challenge|25": {
"acc": 0.643344709897611,
"acc_stderr": 0.013998056902620199,
"acc_norm": 0.6834470989761092,
"acc_norm_stderr": 0.013592431519068075
},
"harness|hellaswag|10": {
"acc": 0.6625174268074089,
"acc_stderr": 0.004718846448021786,
"acc_norm": 0.8454491137223661,
"acc_norm_stderr": 0.0036073726062951024
},
"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": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"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": 0.04878317312145632,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544074,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544074
},
"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": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"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": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"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": 0.4019607843137255,
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}
```
### 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_beberik__Nyxene-11B | [
"region:us"
] | 2023-12-04T17:32:40+00:00 | {"pretty_name": "Evaluation run of beberik/Nyxene-11B", "dataset_summary": "Dataset automatically created during the evaluation run of model [beberik/Nyxene-11B](https://huggingface.co/beberik/Nyxene-11B) 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_beberik__Nyxene-11B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T17:29:47.826048](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__Nyxene-11B/blob/main/results_2023-12-04T17-29-47.826048.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\": 0.651058700269482,\n \"acc_stderr\": 0.0320152029210293,\n \"acc_norm\": 0.6547280296636943,\n \"acc_norm_stderr\": 0.03264773034799592,\n \"mc1\": 0.40269277845777235,\n \"mc1_stderr\": 0.01716883093518722,\n \"mc2\": 0.5749990941717074,\n \"mc2_stderr\": 0.015569738564249067\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620199,\n \"acc_norm\": 0.6834470989761092,\n \"acc_norm_stderr\": 0.013592431519068075\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6625174268074089,\n \"acc_stderr\": 0.004718846448021786,\n \"acc_norm\": 0.8454491137223661,\n \"acc_norm_stderr\": 0.0036073726062951024\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\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\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\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544074,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544074\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\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n 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["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["**/details_harness|winogrande|5_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T17-29-47.826048.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T17_29_47.826048", "path": ["results_2023-12-04T17-29-47.826048.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T17-29-47.826048.parquet"]}]}]} | 2023-12-04T17:33:28+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of beberik/Nyxene-11B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model beberik/Nyxene-11B 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 2023-12-04T17:29:47.826048(note that their might be results for other tasks in 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 beberik/Nyxene-11B",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model beberik/Nyxene-11B 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 2023-12-04T17:29:47.826048(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model beberik/Nyxene-11B 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 2023-12-04T17:29:47.826048(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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"#### 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"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of beberik/Nyxene-11B## 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 beberik/Nyxene-11B 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 2023-12-04T17:29:47.826048(note that their might be results for other tasks in 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"
] |
8443093e6685aae323f0838b6b1a7ffc484a6f22 |
# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-1b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/Deacon-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/Deacon-1b](https://huggingface.co/KnutJaegersberg/Deacon-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__Deacon-1b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:32:52.596072](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deacon-1b/blob/main/results_2023-12-04T17-32-52.596072.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": 0.2547551700512293,
"acc_stderr": 0.030605522190513053,
"acc_norm": 0.2559364936006559,
"acc_norm_stderr": 0.03137480856769965,
"mc1": 0.2178702570379437,
"mc1_stderr": 0.01445084671412389,
"mc2": 0.35049035383875937,
"mc2_stderr": 0.014299155547047497
},
"harness|arc:challenge|25": {
"acc": 0.3003412969283277,
"acc_stderr": 0.013395909309957004,
"acc_norm": 0.3242320819112628,
"acc_norm_stderr": 0.013678810399518827
},
"harness|hellaswag|10": {
"acc": 0.44722166899024096,
"acc_stderr": 0.004961904949171387,
"acc_norm": 0.5862378012348137,
"acc_norm_stderr": 0.004915003499517835
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3925925925925926,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.3925925925925926,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.16447368421052633,
"acc_stderr": 0.030167533468632702,
"acc_norm": 0.16447368421052633,
"acc_norm_stderr": 0.030167533468632702
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"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": 0.25,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.19,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.19,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.18,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749884,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749884
},
"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": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2425531914893617,
"acc_stderr": 0.028020226271200217,
"acc_norm": 0.2425531914893617,
"acc_norm_stderr": 0.028020226271200217
},
"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": 0.2413793103448276,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.24338624338624337,
"acc_stderr": 0.02210112878741543,
"acc_norm": 0.24338624338624337,
"acc_norm_stderr": 0.02210112878741543
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03718489006818114,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03718489006818114
},
"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": 0.2064516129032258,
"acc_stderr": 0.02302589961718872,
"acc_norm": 0.2064516129032258,
"acc_norm_stderr": 0.02302589961718872
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.18226600985221675,
"acc_stderr": 0.02716334085964515,
"acc_norm": 0.18226600985221675,
"acc_norm_stderr": 0.02716334085964515
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.23030303030303031,
"acc_stderr": 0.03287666758603489,
"acc_norm": 0.23030303030303031,
"acc_norm_stderr": 0.03287666758603489
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.21212121212121213,
"acc_stderr": 0.029126522834586818,
"acc_norm": 0.21212121212121213,
"acc_norm_stderr": 0.029126522834586818
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.21761658031088082,
"acc_stderr": 0.029778663037752937,
"acc_norm": 0.21761658031088082,
"acc_norm_stderr": 0.029778663037752937
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.24871794871794872,
"acc_stderr": 0.021916957709213796,
"acc_norm": 0.24871794871794872,
"acc_norm_stderr": 0.021916957709213796
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26296296296296295,
"acc_stderr": 0.026842057873833706,
"acc_norm": 0.26296296296296295,
"acc_norm_stderr": 0.026842057873833706
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.19327731092436976,
"acc_stderr": 0.02564947026588919,
"acc_norm": 0.19327731092436976,
"acc_norm_stderr": 0.02564947026588919
},
"harness|hendrycksTest-high_school_physics|5": {
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}
```
### 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-1b | [
"region:us"
] | 2023-12-04T17:35:06+00:00 | {"pretty_name": "Evaluation run of KnutJaegersberg/Deacon-1b", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deacon-1b](https://huggingface.co/KnutJaegersberg/Deacon-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__Deacon-1b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T17:32:52.596072](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deacon-1b/blob/main/results_2023-12-04T17-32-52.596072.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\": 0.2547551700512293,\n \"acc_stderr\": 0.030605522190513053,\n \"acc_norm\": 0.2559364936006559,\n \"acc_norm_stderr\": 0.03137480856769965,\n \"mc1\": 0.2178702570379437,\n \"mc1_stderr\": 0.01445084671412389,\n \"mc2\": 0.35049035383875937,\n \"mc2_stderr\": 0.014299155547047497\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.3003412969283277,\n \"acc_stderr\": 0.013395909309957004,\n \"acc_norm\": 0.3242320819112628,\n \"acc_norm_stderr\": 0.013678810399518827\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44722166899024096,\n \"acc_stderr\": 0.004961904949171387,\n \"acc_norm\": 0.5862378012348137,\n \"acc_norm_stderr\": 0.004915003499517835\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.3925925925925926,\n \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.3925925925925926,\n \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.030167533468632702,\n \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.030167533468632702\n },\n \"harness|hendrycksTest-business_ethics|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-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\": 0.25,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749884,\n \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749884\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\": 0.25,\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\": 0.2425531914893617,\n \"acc_stderr\": 0.028020226271200217,\n \"acc_norm\": 0.2425531914893617,\n \"acc_norm_stderr\": 0.028020226271200217\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\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24338624338624337,\n \"acc_stderr\": 0.02210112878741543,\n \"acc_norm\": 0.24338624338624337,\n \"acc_norm_stderr\": 0.02210112878741543\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03718489006818114,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03718489006818114\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\": 0.2064516129032258,\n \"acc_stderr\": 0.02302589961718872,\n \"acc_norm\": 0.2064516129032258,\n \"acc_norm_stderr\": 0.02302589961718872\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.18226600985221675,\n \"acc_stderr\": 0.02716334085964515,\n \"acc_norm\": 0.18226600985221675,\n \"acc_norm_stderr\": 0.02716334085964515\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.23030303030303031,\n \"acc_stderr\": 0.03287666758603489,\n \"acc_norm\": 0.23030303030303031,\n \"acc_norm_stderr\": 0.03287666758603489\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.029126522834586818,\n \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.029126522834586818\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.029778663037752937,\n \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.029778663037752937\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24871794871794872,\n \"acc_stderr\": 0.021916957709213796,\n \"acc_norm\": 0.24871794871794872,\n \"acc_norm_stderr\": 0.021916957709213796\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n },\n 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0.23529411764705882,\n \"acc_norm_stderr\": 0.029771775228145635\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\": 0.36771300448430494,\n \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.36771300448430494,\n \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.22137404580152673,\n \"acc_stderr\": 0.03641297081313729,\n \"acc_norm\": 0.22137404580152673,\n \"acc_norm_stderr\": 0.03641297081313729\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.23140495867768596,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.23140495867768596,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T17-32-52.596072.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T17-32-52.596072.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T17_32_52.596072", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T17-32-52.596072.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T17-32-52.596072.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T17_32_52.596072", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T17-32-52.596072.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T17-32-52.596072.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T17_32_52.596072", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T17-32-52.596072.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T17-32-52.596072.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T17_32_52.596072", "path": ["**/details_harness|winogrande|5_2023-12-04T17-32-52.596072.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T17-32-52.596072.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T17_32_52.596072", "path": ["results_2023-12-04T17-32-52.596072.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T17-32-52.596072.parquet"]}]}]} | 2023-12-04T17:35:52+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-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/Deacon-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 2023-12-04T17:32:52.596072(note that their might be results for other tasks in 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
| [
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"### Social Impact of Dataset",
"### Discussion of Biases",
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Deacon-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/Deacon-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 2023-12-04T17:32:52.596072(note that their might be results for other tasks in 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"
] |
2e235a4d9361599cf623015a01bcac1ab90a8bd0 |
# Dataset Card for Evaluation run of S4sch/zephyr-neural-chat-frankenmerge11b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/S4sch/zephyr-neural-chat-frankenmerge11b
- **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 [S4sch/zephyr-neural-chat-frankenmerge11b](https://huggingface.co/S4sch/zephyr-neural-chat-frankenmerge11b) 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_S4sch__zephyr-neural-chat-frankenmerge11b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:40:46.451568](https://huggingface.co/datasets/open-llm-leaderboard/details_S4sch__zephyr-neural-chat-frankenmerge11b/blob/main/results_2023-12-04T17-40-46.451568.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": 0.6090298979840253,
"acc_stderr": 0.032809646949895625,
"acc_norm": 0.618940969899117,
"acc_norm_stderr": 0.033576053409858746,
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6062876441761156,
"mc2_stderr": 0.0158161206163554
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014356399418009124,
"acc_norm": 0.6151877133105802,
"acc_norm_stderr": 0.014218371065251109
},
"harness|hellaswag|10": {
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"acc_norm": 0.8408683529177454,
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},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
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"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.03842498559395269
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
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"acc_norm": 0.54,
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.6188679245283019,
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},
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},
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},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm_stderr": 0.014583812465862538
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm_stderr": 0.025469770149400175
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm_stderr": 0.016269088663959402
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"harness|hendrycksTest-nutrition|5": {
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"acc_stderr": 0.027305308076274695,
"acc_norm": 0.6503267973856209,
"acc_norm_stderr": 0.027305308076274695
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"harness|hendrycksTest-philosophy|5": {
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"acc_stderr": 0.026527724079528872,
"acc_norm": 0.6784565916398714,
"acc_norm_stderr": 0.026527724079528872
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"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.025483115601195455,
"acc_norm": 0.7006172839506173,
"acc_norm_stderr": 0.025483115601195455
},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_stderr": 0.02982074719142248,
"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.02982074719142248
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"harness|hendrycksTest-professional_law|5": {
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"acc_stderr": 0.01271994954303221,
"acc_norm": 0.4556714471968709,
"acc_norm_stderr": 0.01271994954303221
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.6213235294117647,
"acc_norm_stderr": 0.029465133639776132
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm": 0.6421568627450981,
"acc_norm_stderr": 0.019393058402355435
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"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.04607582090719976
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.6448979591836734,
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"acc_norm": 0.6448979591836734,
"acc_norm_stderr": 0.03063565515038764
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"harness|hendrycksTest-sociology|5": {
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"acc_stderr": 0.02650859065623328,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.02650859065623328
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
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"acc_norm": 0.83,
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"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
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"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835816
},
"harness|hendrycksTest-world_religions|5": {
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"acc_stderr": 0.032180937956023566,
"acc_norm": 0.7719298245614035,
"acc_norm_stderr": 0.032180937956023566
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6062876441761156,
"mc2_stderr": 0.0158161206163554
},
"harness|winogrande|5": {
"acc": 0.7624309392265194,
"acc_stderr": 0.011961298905803159
},
"harness|gsm8k|5": {
"acc": 0.07429871114480667,
"acc_stderr": 0.007223844172845574
}
}
```
### 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_S4sch__zephyr-neural-chat-frankenmerge11b | [
"region:us"
] | 2023-12-04T17:43:36+00:00 | {"pretty_name": "Evaluation run of S4sch/zephyr-neural-chat-frankenmerge11b", "dataset_summary": "Dataset automatically created during the evaluation run of model [S4sch/zephyr-neural-chat-frankenmerge11b](https://huggingface.co/S4sch/zephyr-neural-chat-frankenmerge11b) 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_S4sch__zephyr-neural-chat-frankenmerge11b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T17:40:46.451568](https://huggingface.co/datasets/open-llm-leaderboard/details_S4sch__zephyr-neural-chat-frankenmerge11b/blob/main/results_2023-12-04T17-40-46.451568.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\": 0.6090298979840253,\n \"acc_stderr\": 0.032809646949895625,\n \"acc_norm\": 0.618940969899117,\n \"acc_norm_stderr\": 0.033576053409858746,\n \"mc1\": 0.4418604651162791,\n \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.6062876441761156,\n \"mc2_stderr\": 0.0158161206163554\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5930034129692833,\n \"acc_stderr\": 0.014356399418009124,\n \"acc_norm\": 0.6151877133105802,\n \"acc_norm_stderr\": 0.014218371065251109\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6596295558653654,\n \"acc_stderr\": 0.004728653488866922,\n \"acc_norm\": 0.8408683529177454,\n \"acc_norm_stderr\": 0.0036505121583062794\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\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395269,\n \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395269\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.6188679245283019,\n \"acc_stderr\": 0.02989060968628665,\n \"acc_norm\": 0.6188679245283019,\n \"acc_norm_stderr\": 0.02989060968628665\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\": 0.49,\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\": 0.31,\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\": 0.6242774566473989,\n \"acc_stderr\": 0.03692820767264866,\n \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.03692820767264866\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\": 0.73,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4021164021164021,\n \"acc_stderr\": 0.02525303255499769,\n \"acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.02525303255499769\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\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\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\": 0.4630541871921182,\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\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\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\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6153846153846154,\n \"acc_stderr\": 0.02466674491518721,\n \"acc_norm\": 0.6153846153846154,\n \"acc_norm_stderr\": 0.02466674491518721\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.03068473711513536,\n \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.03068473711513536\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8238532110091743,\n \"acc_stderr\": 0.01633288239343136,\n \"acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.01633288239343136\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588667,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588667\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\": 0.7040358744394619,\n \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.04118438565806298,\n \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.04118438565806298\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\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\": 0.7177914110429447,\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\": 0.4375,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04708567521880525\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\": 0.8589743589743589,\n \"acc_stderr\": 0.022801382534597552,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.022801382534597552\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.789272030651341,\n \"acc_stderr\": 0.014583812465862538,\n \"acc_norm\": 0.789272030651341,\n \"acc_norm_stderr\": 0.014583812465862538\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.661849710982659,\n \"acc_stderr\": 0.025469770149400175,\n \"acc_norm\": 0.661849710982659,\n \"acc_norm_stderr\": 0.025469770149400175\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n \"acc_stderr\": 0.016269088663959402,\n \"acc_norm\": 0.3843575418994413,\n \"acc_norm_stderr\": 0.016269088663959402\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6503267973856209,\n \"acc_stderr\": 0.027305308076274695,\n \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.027305308076274695\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\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195455,\n \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195455\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142248,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142248\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4556714471968709,\n \"acc_stderr\": 0.01271994954303221,\n \"acc_norm\": 0.4556714471968709,\n \"acc_norm_stderr\": 0.01271994954303221\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.029465133639776132,\n \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.029465133639776132\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6421568627450981,\n 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#region-us
|
# Dataset Card for Evaluation run of S4sch/zephyr-neural-chat-frankenmerge11b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model S4sch/zephyr-neural-chat-frankenmerge11b 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 2023-12-04T17:40:46.451568(note that their might be results for other tasks in 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 S4sch/zephyr-neural-chat-frankenmerge11b",
"## 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 S4sch/zephyr-neural-chat-frankenmerge11b 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 2023-12-04T17:40:46.451568(note that their might be results for other tasks in 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",
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] |
bf99f7d2b01019d50bd533088f7bafd4063b403a |
# Dataset Card for Evaluation run of mrfakename/NeuralOrca-7B-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mrfakename/NeuralOrca-7B-v1
- **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 [mrfakename/NeuralOrca-7B-v1](https://huggingface.co/mrfakename/NeuralOrca-7B-v1) 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_mrfakename__NeuralOrca-7B-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:53:31.960115](https://huggingface.co/datasets/open-llm-leaderboard/details_mrfakename__NeuralOrca-7B-v1/blob/main/results_2023-12-04T17-53-31.960115.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": {
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"acc_stderr": 0.032248165389573695,
"acc_norm": 0.6406523603337572,
"acc_norm_stderr": 0.032892154968215216,
"mc1": 0.36964504283965727,
"mc1_stderr": 0.01689818070697389,
"mc2": 0.5457774305208005,
"mc2_stderr": 0.015413416681633433
},
"harness|arc:challenge|25": {
"acc": 0.6194539249146758,
"acc_stderr": 0.014188277712349814,
"acc_norm": 0.6527303754266212,
"acc_norm_stderr": 0.01391303452962045
},
"harness|hellaswag|10": {
"acc": 0.6638119896434973,
"acc_stderr": 0.004714386376337136,
"acc_norm": 0.8507269468233419,
"acc_norm_stderr": 0.0035562912320503525
},
"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": 0.5925925925925926,
"acc_stderr": 0.04244633238353227,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04244633238353227
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.04960449637488583,
"acc_norm": 0.58,
"acc_norm_stderr": 0.04960449637488583
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
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},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.03724249595817731,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.03724249595817731
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.76,
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_statistics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|truthfulqa:mc|0": {
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
"acc": 0.5845337376800607,
"acc_stderr": 0.013574222625031811
}
}
```
### 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_mrfakename__NeuralOrca-7B-v1 | [
"region:us"
] | 2023-12-04T17:56:24+00:00 | {"pretty_name": "Evaluation run of mrfakename/NeuralOrca-7B-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [mrfakename/NeuralOrca-7B-v1](https://huggingface.co/mrfakename/NeuralOrca-7B-v1) 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_mrfakename__NeuralOrca-7B-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T17:53:31.960115](https://huggingface.co/datasets/open-llm-leaderboard/details_mrfakename__NeuralOrca-7B-v1/blob/main/results_2023-12-04T17-53-31.960115.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\": 0.6385330990221446,\n \"acc_stderr\": 0.032248165389573695,\n \"acc_norm\": 0.6406523603337572,\n \"acc_norm_stderr\": 0.032892154968215216,\n \"mc1\": 0.36964504283965727,\n \"mc1_stderr\": 0.01689818070697389,\n \"mc2\": 0.5457774305208005,\n \"mc2_stderr\": 0.015413416681633433\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6194539249146758,\n \"acc_stderr\": 0.014188277712349814,\n \"acc_norm\": 0.6527303754266212,\n \"acc_norm_stderr\": 0.01391303452962045\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6638119896434973,\n \"acc_stderr\": 0.004714386376337136,\n \"acc_norm\": 0.8507269468233419,\n \"acc_norm_stderr\": 0.0035562912320503525\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\": 0.5925925925925926,\n \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n },\n \"harness|hendrycksTest-business_ethics|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-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\": 0.46,\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\": 0.48,\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\": 0.32,\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\": 0.6069364161849711,\n \"acc_stderr\": 0.03724249595817731,\n \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.03724249595817731\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\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\": 0.5087719298245614,\n \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.047028804320496165\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\": 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\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\": 0.35,\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\": 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\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\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945627,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945627\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083015,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083015\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\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\": 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501534,\n \"acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501534\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\": 0.7990196078431373,\n \"acc_stderr\": 0.02812597226565437,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565437\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\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\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\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\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.022801382534597528\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\": 0.8173690932311622,\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\": 0.708092485549133,\n \"acc_stderr\": 0.024476994076247337,\n \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.024476994076247337\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33519553072625696,\n \"acc_stderr\": 0.015788007190185884,\n \"acc_norm\": 0.33519553072625696,\n \"acc_norm_stderr\": 0.015788007190185884\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666789,\n \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666789\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.6881028938906752,\n \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4641460234680574,\n \"acc_stderr\": 0.012737361318730583,\n \"acc_norm\": 0.4641460234680574,\n \"acc_norm_stderr\": 0.012737361318730583\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000318,\n \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000318\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\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128438,\n \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128438\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n \"acc_norm_stderr\": 0.026814951200421603\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\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685516\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\": 0.36964504283965727,\n \"mc1_stderr\": 0.01689818070697389,\n \"mc2\": 0.5457774305208005,\n \"mc2_stderr\": 0.015413416681633433\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.011493384687249784\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5845337376800607,\n \"acc_stderr\": 0.013574222625031811\n }\n}\n```", "repo_url": "https://huggingface.co/mrfakename/NeuralOrca-7B-v1", "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_04T17_53_31.960115", "path": ["**/details_harness|arc:challenge|25_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|gsm8k|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hellaswag|10_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T17-53-31.960115.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T17-53-31.960115.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-human_aging|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["**/details_harness|winogrande|5_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T17-53-31.960115.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T17_53_31.960115", "path": ["results_2023-12-04T17-53-31.960115.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T17-53-31.960115.parquet"]}]}]} | 2023-12-04T17:57:08+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of mrfakename/NeuralOrca-7B-v1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model mrfakename/NeuralOrca-7B-v1 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 2023-12-04T17:53:31.960115(note that their might be results for other tasks in 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 mrfakename/NeuralOrca-7B-v1",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model mrfakename/NeuralOrca-7B-v1 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 2023-12-04T17:53:31.960115(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Languages",
"## Dataset Structure",
"### Data Instances",
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"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
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] | [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model mrfakename/NeuralOrca-7B-v1 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 2023-12-04T17:53:31.960115(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mrfakename/NeuralOrca-7B-v1## 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 mrfakename/NeuralOrca-7B-v1 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 2023-12-04T17:53:31.960115(note that their might be results for other tasks in 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"
] |
50d7f2698d1105a0fe1e7630590f12a691b70a66 |
# Dataset Card for Evaluation run of Felladrin/TinyMistral-248M-SFT-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Felladrin/TinyMistral-248M-SFT-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 [Felladrin/TinyMistral-248M-SFT-v3](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-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_Felladrin__TinyMistral-248M-SFT-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:03:12.401261](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__TinyMistral-248M-SFT-v3/blob/main/results_2023-12-04T18-03-12.401261.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": 0.23016202481388653,
"acc_stderr": 0.029832125302523167,
"acc_norm": 0.22987279360507185,
"acc_norm_stderr": 0.03061582219263556,
"mc1": 0.20563035495716034,
"mc1_stderr": 0.014148482219460962,
"mc2": 0.400307198899101,
"mc2_stderr": 0.014941622020470767
},
"harness|arc:challenge|25": {
"acc": 0.19283276450511946,
"acc_stderr": 0.01152905546566333,
"acc_norm": 0.21928327645051193,
"acc_norm_stderr": 0.012091245787615721
},
"harness|hellaswag|10": {
"acc": 0.27106154152559253,
"acc_stderr": 0.004435993492583857,
"acc_norm": 0.2826130252937662,
"acc_norm_stderr": 0.004493495872000123
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"harness|gsm8k|5": {
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}
}
```
### 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_Felladrin__TinyMistral-248M-SFT-v3 | [
"region:us"
] | 2023-12-04T18:06:04+00:00 | {"pretty_name": "Evaluation run of Felladrin/TinyMistral-248M-SFT-v3", "dataset_summary": "Dataset automatically created during the evaluation run of model [Felladrin/TinyMistral-248M-SFT-v3](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-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_Felladrin__TinyMistral-248M-SFT-v3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:03:12.401261](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__TinyMistral-248M-SFT-v3/blob/main/results_2023-12-04T18-03-12.401261.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\": 0.23016202481388653,\n \"acc_stderr\": 0.029832125302523167,\n \"acc_norm\": 0.22987279360507185,\n \"acc_norm_stderr\": 0.03061582219263556,\n \"mc1\": 0.20563035495716034,\n \"mc1_stderr\": 0.014148482219460962,\n \"mc2\": 0.400307198899101,\n \"mc2_stderr\": 0.014941622020470767\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.19283276450511946,\n \"acc_stderr\": 0.01152905546566333,\n \"acc_norm\": 0.21928327645051193,\n \"acc_norm_stderr\": 0.012091245787615721\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.27106154152559253,\n \"acc_stderr\": 0.004435993492583857,\n \"acc_norm\": 0.2826130252937662,\n \"acc_norm_stderr\": 0.004493495872000123\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.025447863825108618,\n \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.025447863825108618\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|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-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\": 0.2023121387283237,\n \"acc_stderr\": 0.030631145539198813,\n \"acc_norm\": 0.2023121387283237,\n \"acc_norm_stderr\": 0.030631145539198813\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.18783068783068782,\n \"acc_stderr\": 0.0201157341415211,\n \"acc_norm\": 0.18783068783068782,\n \"acc_norm_stderr\": 0.0201157341415211\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 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["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["**/details_harness|winogrande|5_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T18-03-12.401261.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T18_03_12.401261", "path": ["results_2023-12-04T18-03-12.401261.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T18-03-12.401261.parquet"]}]}]} | 2023-12-04T18:06:48+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of Felladrin/TinyMistral-248M-SFT-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 Felladrin/TinyMistral-248M-SFT-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 2023-12-04T18:03:12.401261(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Felladrin/TinyMistral-248M-SFT-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 2023-12-04T18:03:12.401261(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Felladrin/TinyMistral-248M-SFT-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 Felladrin/TinyMistral-248M-SFT-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 2023-12-04T18:03:12.401261(note that their might be results for other tasks in 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"
] |
2e278713b888d03cadbadeabd551273e0d63d78b |
# Dataset Card for Evaluation run of chargoddard/loyal-piano-m7-cdpo
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/loyal-piano-m7-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/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-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 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_chargoddard__loyal-piano-m7-cdpo",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:22:08.333315](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__loyal-piano-m7-cdpo/blob/main/results_2023-12-04T19-22-08.333315.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": 0.6467300740498503,
"acc_stderr": 0.0321481764536421,
"acc_norm": 0.6493966251003455,
"acc_norm_stderr": 0.03278779694207035,
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6154444740630072,
"mc2_stderr": 0.015471878904856169
},
"harness|arc:challenge|25": {
"acc": 0.6416382252559727,
"acc_stderr": 0.014012883334859857,
"acc_norm": 0.6706484641638225,
"acc_norm_stderr": 0.013734057652635473
},
"harness|hellaswag|10": {
"acc": 0.6652061342362079,
"acc_stderr": 0.00470953886491632,
"acc_norm": 0.8542123083051185,
"acc_norm_stderr": 0.0035217202839105555
},
"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": 0.6518518518518519,
"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": 0.6754716981132075,
"acc_stderr": 0.028815615713432104,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.028815615713432104
},
"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": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"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": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"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": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5574468085106383,
"acc_stderr": 0.032469569197899575,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.032469569197899575
},
"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": 0.593103448275862,
"acc_stderr": 0.04093793981266236,
"acc_norm": 0.593103448275862,
"acc_norm_stderr": 0.04093793981266236
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.025010749116137595,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.025010749116137595
},
"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": {
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"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": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.028606204289229865,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.028606204289229865
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364423,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364423
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6820512820512821,
"acc_stderr": 0.023610884308927865,
"acc_norm": 0.6820512820512821,
"acc_norm_stderr": 0.023610884308927865
},
"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": 0.7016806722689075,
"acc_stderr": 0.029719142876342853,
"acc_norm": 0.7016806722689075,
"acc_norm_stderr": 0.029719142876342853
},
"harness|hendrycksTest-high_school_physics|5": {
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"harness|gsm8k|5": {
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}
}
```
### 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__loyal-piano-m7-cdpo | [
"region:us"
] | 2023-12-04T18:09:37+00:00 | {"pretty_name": "Evaluation run of chargoddard/loyal-piano-m7-cdpo", "dataset_summary": "Dataset automatically created during the evaluation run of model [chargoddard/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-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 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_chargoddard__loyal-piano-m7-cdpo\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:22:08.333315](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__loyal-piano-m7-cdpo/blob/main/results_2023-12-04T19-22-08.333315.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\": 0.6467300740498503,\n \"acc_stderr\": 0.0321481764536421,\n \"acc_norm\": 0.6493966251003455,\n \"acc_norm_stderr\": 0.03278779694207035,\n \"mc1\": 0.4418604651162791,\n \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.6154444740630072,\n \"mc2_stderr\": 0.015471878904856169\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6416382252559727,\n \"acc_stderr\": 0.014012883334859857,\n \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635473\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6652061342362079,\n \"acc_stderr\": 0.00470953886491632,\n \"acc_norm\": 0.8542123083051185,\n \"acc_norm_stderr\": 0.0035217202839105555\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\": 0.6518518518518519,\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\": 0.6754716981132075,\n \"acc_stderr\": 0.028815615713432104,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.028815615713432104\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\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\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\": 0.31,\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\": 0.6473988439306358,\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\": 0.4019607843137255,\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\": 0.77,\n \"acc_stderr\": 0.04229525846816507,\n 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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\": 0.4975369458128079,\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\": 0.7575757575757576,\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\": 0.797979797979798,\n \"acc_stderr\": 0.028606204289229865,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229865\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364423,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364423\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6820512820512821,\n \"acc_stderr\": 0.023610884308927865,\n \"acc_norm\": 0.6820512820512821,\n \"acc_norm_stderr\": 0.023610884308927865\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 \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.029719142876342853,\n \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.029719142876342853\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\": 0.8495412844036697,\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\": 0.5138888888888888,\n \"acc_stderr\": 0.034086558679777494,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.034086558679777494\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.02646056956124064,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.02646056956124064\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\": 0.8240740740740741,\n \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of chargoddard/loyal-piano-m7-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/loyal-piano-m7-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 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 2023-12-04T19:22:08.333315(note that their might be results for other tasks in 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/loyal-piano-m7-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/loyal-piano-m7-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 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 2023-12-04T19:22:08.333315(note that their might be results for other tasks in 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"
] | [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/loyal-piano-m7-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 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:",
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"### 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"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of chargoddard/loyal-piano-m7-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/loyal-piano-m7-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 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 2023-12-04T19:22:08.333315(note that their might be results for other tasks in 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"
] |
a1c0a9dce00b17a3139708bcca58516e2994f9e9 |
# Dataset Card for Evaluation run of qiyinmiss/My_GPT2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/qiyinmiss/My_GPT2
- **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 [qiyinmiss/My_GPT2](https://huggingface.co/qiyinmiss/My_GPT2) 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_qiyinmiss__My_GPT2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:10:51.654289](https://huggingface.co/datasets/open-llm-leaderboard/details_qiyinmiss__My_GPT2/blob/main/results_2023-12-04T18-10-51.654289.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": {
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"acc_stderr": 0.030639295152135662,
"acc_norm": 0.2586921012373598,
"acc_norm_stderr": 0.031410248419889694,
"mc1": 0.22766217870257038,
"mc1_stderr": 0.01467925503211107,
"mc2": 0.4073203809998297,
"mc2_stderr": 0.014931113118872399
},
"harness|arc:challenge|25": {
"acc": 0.19880546075085323,
"acc_stderr": 0.011662850198175539,
"acc_norm": 0.21928327645051193,
"acc_norm_stderr": 0.012091245787615723
},
"harness|hellaswag|10": {
"acc": 0.29267078271260705,
"acc_stderr": 0.004540586983229993,
"acc_norm": 0.3158733320055766,
"acc_norm_stderr": 0.004639126951051421
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.03673731683969506
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.16447368421052633,
"acc_norm_stderr": 0.0301675334686327
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.16,
"acc_norm_stderr": 0.03684529491774711
},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
"harness|hendrycksTest-college_biology|5": {
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},
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"acc_norm": 0.19,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-college_computer_science|5": {
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},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.23699421965317918,
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|gsm8k|5": {
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}
}
```
### 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_qiyinmiss__My_GPT2 | [
"region:us"
] | 2023-12-04T18:12:28+00:00 | {"pretty_name": "Evaluation run of qiyinmiss/My_GPT2", "dataset_summary": "Dataset automatically created during the evaluation run of model [qiyinmiss/My_GPT2](https://huggingface.co/qiyinmiss/My_GPT2) 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_qiyinmiss__My_GPT2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:10:51.654289](https://huggingface.co/datasets/open-llm-leaderboard/details_qiyinmiss__My_GPT2/blob/main/results_2023-12-04T18-10-51.654289.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\": 0.2578870030806924,\n \"acc_stderr\": 0.030639295152135662,\n \"acc_norm\": 0.2586921012373598,\n \"acc_norm_stderr\": 0.031410248419889694,\n \"mc1\": 0.22766217870257038,\n \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4073203809998297,\n \"mc2_stderr\": 0.014931113118872399\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.19880546075085323,\n \"acc_stderr\": 0.011662850198175539,\n \"acc_norm\": 0.21928327645051193,\n \"acc_norm_stderr\": 0.012091245787615723\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.29267078271260705,\n \"acc_stderr\": 0.004540586983229993,\n \"acc_norm\": 0.3158733320055766,\n \"acc_norm_stderr\": 0.004639126951051421\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.0301675334686327,\n \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.0301675334686327\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774711,\n \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774711\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756194,\n \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756194\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-college_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-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\": 0.23699421965317918,\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\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2765957446808511,\n \"acc_stderr\": 0.029241883869628834,\n \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.029241883869628834\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\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.14285714285714285,\n \"acc_stderr\": 0.0312984318574381,\n \"acc_norm\": 0.14285714285714285,\n \"acc_norm_stderr\": 0.0312984318574381\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263686,\n \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263686\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.026069362295335137,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.026069362295335137\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114475,\n \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114475\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.35353535353535354,\n \"acc_stderr\": 0.03406086723547153,\n \"acc_norm\": 0.35353535353535354,\n \"acc_norm_stderr\": 0.03406086723547153\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2794871794871795,\n \"acc_stderr\": 0.022752388839776826,\n \"acc_norm\": 0.2794871794871795,\n \"acc_norm_stderr\": 0.022752388839776826\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073838,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073838\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136098,\n \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136098\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.3486238532110092,\n \"acc_stderr\": 0.020431254090714328,\n \"acc_norm\": 0.3486238532110092,\n \"acc_norm_stderr\": 0.020431254090714328\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\": 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604243,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604243\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3004484304932735,\n \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.3004484304932735,\n \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.038808483010823944,\n \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.038808483010823944\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.32231404958677684,\n \"acc_stderr\": 0.04266416363352168,\n \"acc_norm\": 0.32231404958677684,\n \"acc_norm_stderr\": 0.04266416363352168\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.03957835471980981\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\": 0.24107142857142858,\n \"acc_stderr\": 0.04059867246952688,\n \"acc_norm\": 0.24107142857142858,\n \"acc_norm_stderr\": 0.04059867246952688\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.34951456310679613,\n \"acc_stderr\": 0.04721188506097173,\n \"acc_norm\": 0.34951456310679613,\n \"acc_norm_stderr\": 0.04721188506097173\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1752136752136752,\n \"acc_stderr\": 0.02490443909891822,\n \"acc_norm\": 0.1752136752136752,\n \"acc_norm_stderr\": 0.02490443909891822\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.21839080459770116,\n \"acc_stderr\": 0.01477435831993449,\n \"acc_norm\": 0.21839080459770116,\n \"acc_norm_stderr\": 0.01477435831993449\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.02289408248992599,\n \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.02289408248992599\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\": 0.2222222222222222,\n \"acc_stderr\": 0.023805186524888146,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023805186524888146\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24758842443729903,\n \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.24758842443729903,\n \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.023246202647819746,\n \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.023246202647819746\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24771838331160365,\n \"acc_stderr\": 0.011025499291443737,\n \"acc_norm\": 0.24771838331160365,\n \"acc_norm_stderr\": 0.011025499291443737\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.030161911930767102,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.030161911930767102\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.017848089574913222,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.017848089574913222\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.1927710843373494,\n \"acc_stderr\": 0.030709824050565274,\n \"acc_norm\": 0.1927710843373494,\n \"acc_norm_stderr\": 0.030709824050565274\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22766217870257038,\n \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4073203809998297,\n \"mc2_stderr\": 0.014931113118872399\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790513\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \"acc_stderr\": 0.0022675371022544736\n }\n}\n```", "repo_url": "https://huggingface.co/qiyinmiss/My_GPT2", "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_04T18_10_51.654289", "path": ["**/details_harness|arc:challenge|25_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|gsm8k|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hellaswag|10_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-10-51.654289.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-10-51.654289.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["**/details_harness|winogrande|5_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T18-10-51.654289.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T18_10_51.654289", "path": ["results_2023-12-04T18-10-51.654289.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T18-10-51.654289.parquet"]}]}]} | 2023-12-04T18:13:11+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of qiyinmiss/My_GPT2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model qiyinmiss/My_GPT2 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 2023-12-04T18:10:51.654289(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model qiyinmiss/My_GPT2 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 2023-12-04T18:10:51.654289(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Languages",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model qiyinmiss/My_GPT2 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 2023-12-04T18:10:51.654289(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of qiyinmiss/My_GPT2## 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 qiyinmiss/My_GPT2 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 2023-12-04T18:10:51.654289(note that their might be results for other tasks in 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"
] |
923794aca621c16b1fd1a79148103ff36d050c7e |
# Dataset Card for Evaluation run of L-R/LLmRa-1.3B_V2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/L-R/LLmRa-1.3B_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 [L-R/LLmRa-1.3B_V2](https://huggingface.co/L-R/LLmRa-1.3B_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_L-R__LLmRa-1.3B_V2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:13:15.714207](https://huggingface.co/datasets/open-llm-leaderboard/details_L-R__LLmRa-1.3B_V2/blob/main/results_2023-12-04T18-13-15.714207.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": 0.2645874461578066,
"acc_stderr": 0.03098158394706952,
"acc_norm": 0.26593270237799466,
"acc_norm_stderr": 0.031804165627672396,
"mc1": 0.2386780905752754,
"mc1_stderr": 0.014922629695456418,
"mc2": 0.3646013754071996,
"mc2_stderr": 0.014251642555151921
},
"harness|arc:challenge|25": {
"acc": 0.2815699658703072,
"acc_stderr": 0.013143376735009009,
"acc_norm": 0.3046075085324232,
"acc_norm_stderr": 0.01344952210993249
},
"harness|hellaswag|10": {
"acc": 0.41037641904003186,
"acc_stderr": 0.004908967278222486,
"acc_norm": 0.5302728540131448,
"acc_norm_stderr": 0.004980627287147582
},
"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": 0.23703703703703705,
"acc_stderr": 0.03673731683969506,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.03673731683969506
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.19736842105263158,
"acc_stderr": 0.03238981601699397,
"acc_norm": 0.19736842105263158,
"acc_norm_stderr": 0.03238981601699397
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2981132075471698,
"acc_stderr": 0.028152837942493854,
"acc_norm": 0.2981132075471698,
"acc_norm_stderr": 0.028152837942493854
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"harness|hendrycksTest-college_biology|5": {
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}
```
### 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_L-R__LLmRa-1.3B_V2 | [
"region:us"
] | 2023-12-04T18:15:22+00:00 | {"pretty_name": "Evaluation run of L-R/LLmRa-1.3B_V2", "dataset_summary": "Dataset automatically created during the evaluation run of model [L-R/LLmRa-1.3B_V2](https://huggingface.co/L-R/LLmRa-1.3B_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_L-R__LLmRa-1.3B_V2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:13:15.714207](https://huggingface.co/datasets/open-llm-leaderboard/details_L-R__LLmRa-1.3B_V2/blob/main/results_2023-12-04T18-13-15.714207.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\": 0.2645874461578066,\n \"acc_stderr\": 0.03098158394706952,\n \"acc_norm\": 0.26593270237799466,\n \"acc_norm_stderr\": 0.031804165627672396,\n \"mc1\": 0.2386780905752754,\n \"mc1_stderr\": 0.014922629695456418,\n \"mc2\": 0.3646013754071996,\n \"mc2_stderr\": 0.014251642555151921\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.2815699658703072,\n \"acc_stderr\": 0.013143376735009009,\n \"acc_norm\": 0.3046075085324232,\n \"acc_norm_stderr\": 0.01344952210993249\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.41037641904003186,\n \"acc_stderr\": 0.004908967278222486,\n \"acc_norm\": 0.5302728540131448,\n \"acc_norm_stderr\": 0.004980627287147582\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\": 0.23703703703703705,\n \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.19736842105263158,\n \"acc_stderr\": 0.03238981601699397,\n \"acc_norm\": 0.19736842105263158,\n \"acc_norm_stderr\": 0.03238981601699397\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.2981132075471698,\n \"acc_stderr\": 0.028152837942493854,\n \"acc_norm\": 0.2981132075471698,\n \"acc_norm_stderr\": 0.028152837942493854\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3055555555555556,\n \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.3055555555555556,\n \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.19,\n 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["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["**/details_harness|winogrande|5_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T18-13-15.714207.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T18_13_15.714207", "path": ["results_2023-12-04T18-13-15.714207.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T18-13-15.714207.parquet"]}]}]} | 2023-12-04T18:16:07+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of L-R/LLmRa-1.3B_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 L-R/LLmRa-1.3B_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 2023-12-04T18:13:15.714207(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model L-R/LLmRa-1.3B_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 2023-12-04T18:13:15.714207(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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] | [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model L-R/LLmRa-1.3B_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 2023-12-04T18:13:15.714207(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### 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"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of L-R/LLmRa-1.3B_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 L-R/LLmRa-1.3B_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 2023-12-04T18:13:15.714207(note that their might be results for other tasks in 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"
] |
a3ad2ae72a8d4a1a83e62f8b5a8f9a5a175bd614 |
# Dataset Card for Evaluation run of Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B
- **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 [Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B](https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B) 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_Weyaxi__neural-chat-7b-v3-1-OpenHermes-2.5-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:24:21.614365](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__neural-chat-7b-v3-1-OpenHermes-2.5-7B/blob/main/results_2023-12-04T18-24-21.614365.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": 0.6331720228661488,
"acc_stderr": 0.03245949446776424,
"acc_norm": 0.636248593036376,
"acc_norm_stderr": 0.033106781744445986,
"mc1": 0.4565483476132191,
"mc1_stderr": 0.01743728095318369,
"mc2": 0.612310037106096,
"mc2_stderr": 0.015369020754133529
},
"harness|arc:challenge|25": {
"acc": 0.6450511945392492,
"acc_stderr": 0.01398303690409409,
"acc_norm": 0.6612627986348123,
"acc_norm_stderr": 0.013830568927974332
},
"harness|hellaswag|10": {
"acc": 0.6573391754630552,
"acc_stderr": 0.004736292355716402,
"acc_norm": 0.8408683529177454,
"acc_norm_stderr": 0.003650512158306273
},
"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": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998905,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998905
},
"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": 0.6641509433962264,
"acc_stderr": 0.02906722014664483,
"acc_norm": 0.6641509433962264,
"acc_norm_stderr": 0.02906722014664483
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.037161774375660164,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.037161774375660164
},
"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": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"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": 0.6242774566473989,
"acc_stderr": 0.036928207672648664,
"acc_norm": 0.6242774566473989,
"acc_norm_stderr": 0.036928207672648664
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"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": 0.5446808510638298,
"acc_stderr": 0.032555253593403555,
"acc_norm": 0.5446808510638298,
"acc_norm_stderr": 0.032555253593403555
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5175438596491229,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.5175438596491229,
"acc_norm_stderr": 0.04700708033551038
},
"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": 0.3888888888888889,
"acc_stderr": 0.025107425481137285,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.025107425481137285
},
"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": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"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": 0.5073891625615764,
"acc_stderr": 0.0351760354036101,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.0351760354036101
},
"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": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479049,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479049
},
"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": 0.6307692307692307,
"acc_stderr": 0.02446861524147892,
"acc_norm": 0.6307692307692307,
"acc_norm_stderr": 0.02446861524147892
},
"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": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
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"acc_stderr": 0.01377039069700212
}
}
```
### 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__neural-chat-7b-v3-1-OpenHermes-2.5-7B | [
"region:us"
] | 2023-12-04T18:27:12+00:00 | {"pretty_name": "Evaluation run of Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B](https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B) 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_Weyaxi__neural-chat-7b-v3-1-OpenHermes-2.5-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:24:21.614365](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__neural-chat-7b-v3-1-OpenHermes-2.5-7B/blob/main/results_2023-12-04T18-24-21.614365.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\": 0.6331720228661488,\n \"acc_stderr\": 0.03245949446776424,\n \"acc_norm\": 0.636248593036376,\n \"acc_norm_stderr\": 0.033106781744445986,\n \"mc1\": 0.4565483476132191,\n \"mc1_stderr\": 0.01743728095318369,\n \"mc2\": 0.612310037106096,\n \"mc2_stderr\": 0.015369020754133529\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6450511945392492,\n \"acc_stderr\": 0.01398303690409409,\n \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6573391754630552,\n \"acc_stderr\": 0.004736292355716402,\n \"acc_norm\": 0.8408683529177454,\n \"acc_norm_stderr\": 0.003650512158306273\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\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998905,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998905\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\": 0.6641509433962264,\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\": 0.7291666666666666,\n \"acc_stderr\": 0.037161774375660164,\n \"acc_norm\": 0.7291666666666666,\n \"acc_norm_stderr\": 0.037161774375660164\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\": 0.52,\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\": 0.36,\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\": 0.6242774566473989,\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\": 0.04878608714466996,\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\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.032555253593403555,\n \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.032555253593403555\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\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\": 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137285,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137285\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\": 0.32,\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\": 0.7838709677419354,\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\": 0.5073891625615764,\n \"acc_stderr\": 0.0351760354036101,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.0351760354036101\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\": 0.7818181818181819,\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\": 0.02962022787479049,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\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\": 0.6307692307692307,\n \"acc_stderr\": 0.02446861524147892,\n \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.02446861524147892\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\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121622,\n \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121622\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\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\": 0.4583333333333333,\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\": 0.8088235294117647,\n \"acc_stderr\": 0.02759917430064077,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.02759917430064077\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.038448761397852714,\n \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.038448761397852714\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\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n 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#region-us
|
# Dataset Card for Evaluation run of Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Weyaxi/neural-chat-7b-v3-1-OpenHermes-2.5-7B 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 2023-12-04T18:24:21.614365(note that their might be results for other tasks in 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
| [
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"## Latest results\n\nThese are the latest results from run 2023-12-04T18:24:21.614365(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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] |
71334aa1d5ab17dac5b9e24866dc81019a49ac4a |
# Dataset Card for Evaluation run of simonveitner/MathHermes-2.5-Mistral-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/simonveitner/MathHermes-2.5-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 [simonveitner/MathHermes-2.5-Mistral-7B](https://huggingface.co/simonveitner/MathHermes-2.5-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 1 run(s). Each run can be found as a specific 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_simonveitner__MathHermes-2.5-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:25:11.977949](https://huggingface.co/datasets/open-llm-leaderboard/details_simonveitner__MathHermes-2.5-Mistral-7B/blob/main/results_2023-12-04T18-25-11.977949.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": 0.6357052985064087,
"acc_stderr": 0.03227227710982547,
"acc_norm": 0.6396287253937496,
"acc_norm_stderr": 0.032910368232277956,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.519509607840464,
"mc2_stderr": 0.015313445088017108
},
"harness|arc:challenge|25": {
"acc": 0.6075085324232082,
"acc_stderr": 0.01426963463567073,
"acc_norm": 0.6476109215017065,
"acc_norm_stderr": 0.013960142600598677
},
"harness|hellaswag|10": {
"acc": 0.652459669388568,
"acc_stderr": 0.004752158936871871,
"acc_norm": 0.8418641704839673,
"acc_norm_stderr": 0.0036412262941678012
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"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": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
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},
"harness|hendrycksTest-college_chemistry|5": {
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.31,
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},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
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},
"harness|hendrycksTest-college_physics|5": {
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"harness|hendrycksTest-computer_security|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-nutrition|5": {
"acc": 0.7581699346405228,
"acc_stderr": 0.024518195641879334,
"acc_norm": 0.7581699346405228,
"acc_norm_stderr": 0.024518195641879334
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
"acc_stderr": 0.026236965881153266,
"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153266
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7561728395061729,
"acc_stderr": 0.023891879541959607,
"acc_norm": 0.7561728395061729,
"acc_norm_stderr": 0.023891879541959607
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48936170212765956,
"acc_stderr": 0.02982074719142248,
"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.02982074719142248
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4706649282920469,
"acc_stderr": 0.012748238397365549,
"acc_norm": 0.4706649282920469,
"acc_norm_stderr": 0.012748238397365549
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6985294117647058,
"acc_stderr": 0.027875982114273168,
"acc_norm": 0.6985294117647058,
"acc_norm_stderr": 0.027875982114273168
},
"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": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"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": 0.8208955223880597,
"acc_stderr": 0.027113286753111837,
"acc_norm": 0.8208955223880597,
"acc_norm_stderr": 0.027113286753111837
},
"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": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"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": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.519509607840464,
"mc2_stderr": 0.015313445088017108
},
"harness|winogrande|5": {
"acc": 0.77663772691397,
"acc_stderr": 0.011705697565205191
},
"harness|gsm8k|5": {
"acc": 0.4927975739196361,
"acc_stderr": 0.013771055751972868
}
}
```
### 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_simonveitner__MathHermes-2.5-Mistral-7B | [
"region:us"
] | 2023-12-04T18:28:03+00:00 | {"pretty_name": "Evaluation run of simonveitner/MathHermes-2.5-Mistral-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [simonveitner/MathHermes-2.5-Mistral-7B](https://huggingface.co/simonveitner/MathHermes-2.5-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 1 run(s). Each run can be found as a specific 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_simonveitner__MathHermes-2.5-Mistral-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:25:11.977949](https://huggingface.co/datasets/open-llm-leaderboard/details_simonveitner__MathHermes-2.5-Mistral-7B/blob/main/results_2023-12-04T18-25-11.977949.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\": 0.6357052985064087,\n \"acc_stderr\": 0.03227227710982547,\n \"acc_norm\": 0.6396287253937496,\n \"acc_norm_stderr\": 0.032910368232277956,\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.519509607840464,\n \"mc2_stderr\": 0.015313445088017108\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6075085324232082,\n \"acc_stderr\": 0.01426963463567073,\n \"acc_norm\": 0.6476109215017065,\n \"acc_norm_stderr\": 0.013960142600598677\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.652459669388568,\n \"acc_stderr\": 0.004752158936871871,\n \"acc_norm\": 0.8418641704839673,\n \"acc_norm_stderr\": 0.0036412262941678012\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\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\": 0.58,\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\": 0.6830188679245283,\n \"acc_stderr\": 0.0286372356398009,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.0286372356398009\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\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|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_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\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062947,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062947\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.032555253593403555,\n \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.032555253593403555\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\": 0.5241379310344828,\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\": 0.42328042328042326,\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\": 0.47619047619047616,\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\": 0.37,\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\": 0.7903225806451613,\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\": 0.5172413793103449,\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\": 0.64,\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\": 0.7818181818181819,\n \"acc_stderr\": 0.032250781083062896,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.032250781083062896\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\": 0.8756476683937824,\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\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647074,\n \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647074\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\": 0.680672268907563,\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\": 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\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\": 0.5092592592592593,\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\": 0.02812597226565437,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565437\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n \"acc_stderr\": 0.030500283176545847,\n \"acc_norm\": 0.7085201793721974,\n \"acc_norm_stderr\": 0.030500283176545847\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313728,\n \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313728\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\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\": 0.8589743589743589,\n \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.02280138253459753\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\": 0.8237547892720306,\n \"acc_stderr\": 0.013625556907993459,\n \"acc_norm\": 0.8237547892720306,\n \"acc_norm_stderr\": 0.013625556907993459\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\": 0.2994413407821229,\n \"acc_stderr\": 0.01531825774597671,\n \"acc_norm\": 0.2994413407821229,\n \"acc_norm_stderr\": 0.01531825774597671\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n \"acc_norm_stderr\": 0.026236965881153266\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7561728395061729,\n \"acc_stderr\": 0.023891879541959607,\n \"acc_norm\": 0.7561728395061729,\n \"acc_norm_stderr\": 0.023891879541959607\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142248,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142248\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4706649282920469,\n \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\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\": 0.6454545454545455,\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\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n \"acc_norm_stderr\": 0.027113286753111837\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\": 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#region-us
|
# Dataset Card for Evaluation run of simonveitner/MathHermes-2.5-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 simonveitner/MathHermes-2.5-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 1 run(s). Each run can be found as a specific 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 2023-12-04T18:25:11.977949(note that their might be results for other tasks in 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 simonveitner/MathHermes-2.5-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 simonveitner/MathHermes-2.5-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 1 run(s). Each run can be found as a specific 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 2023-12-04T18:25:11.977949(note that their might be results for other tasks in 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 simonveitner/MathHermes-2.5-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 simonveitner/MathHermes-2.5-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 1 run(s). Each run can be found as a specific 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 2023-12-04T18:25:11.977949(note that their might be results for other tasks in 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"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of simonveitner/MathHermes-2.5-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 simonveitner/MathHermes-2.5-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 1 run(s). Each run can be found as a specific 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 2023-12-04T18:25:11.977949(note that their might be results for other tasks in 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"
] |
d72a97ce3bff8c7e55dd064039a25443c511cb09 |
# Dataset Card for Evaluation run of vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
- **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 [vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy](https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy) 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_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:26:43.065214](https://huggingface.co/datasets/open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy/blob/main/results_2023-12-04T18-26-43.065214.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": {
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"acc_stderr": 0.03008501938303984,
"acc_norm": 0.24224538912156782,
"acc_norm_stderr": 0.030747150403453674,
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024643,
"mc2": 0.4692403294958895,
"mc2_stderr": 0.015061938982346217
},
"harness|arc:challenge|25": {
"acc": 0.36945392491467577,
"acc_stderr": 0.014104578366491894,
"acc_norm": 0.38822525597269625,
"acc_norm_stderr": 0.01424161420741405
},
"harness|hellaswag|10": {
"acc": 0.5696076478789086,
"acc_stderr": 0.004941191607317913,
"acc_norm": 0.7276438956383191,
"acc_norm_stderr": 0.004442623590846322
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
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"acc_norm_stderr": 0.031103182383123398
},
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"acc_norm_stderr": 0.0440844002276808
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"acc_norm_stderr": 0.040936018074033256
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"harness|hendrycksTest-college_medicine|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|gsm8k|5": {
"acc": 0.07808946171341925,
"acc_stderr": 0.007390654481108261
}
}
```
### 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_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy | [
"region:us"
] | 2023-12-04T18:29:37+00:00 | {"pretty_name": "Evaluation run of vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy", "dataset_summary": "Dataset automatically created during the evaluation run of model [vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy](https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy) 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_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:26:43.065214](https://huggingface.co/datasets/open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy/blob/main/results_2023-12-04T18-26-43.065214.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\": 0.2434367192235923,\n \"acc_stderr\": 0.03008501938303984,\n \"acc_norm\": 0.24224538912156782,\n \"acc_norm_stderr\": 0.030747150403453674,\n \"mc1\": 0.2778457772337821,\n \"mc1_stderr\": 0.015680929364024643,\n \"mc2\": 0.4692403294958895,\n \"mc2_stderr\": 0.015061938982346217\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.36945392491467577,\n \"acc_stderr\": 0.014104578366491894,\n \"acc_norm\": 0.38822525597269625,\n \"acc_norm_stderr\": 0.01424161420741405\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5696076478789086,\n \"acc_stderr\": 0.004941191607317913,\n \"acc_norm\": 0.7276438956383191,\n \"acc_norm_stderr\": 0.004442623590846322\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\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\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|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-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\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\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\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2778457772337821,\n \"mc1_stderr\": 0.015680929364024643,\n \"mc2\": 0.4692403294958895,\n \"mc2_stderr\": 0.015061938982346217\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6558800315706393,\n \"acc_stderr\": 0.013352121905005941\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07808946171341925,\n \"acc_stderr\": 0.007390654481108261\n }\n}\n```", "repo_url": "https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy", "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_04T18_26_43.065214", "path": ["**/details_harness|arc:challenge|25_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|gsm8k|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hellaswag|10_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-26-43.065214.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-26-43.065214.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["**/details_harness|winogrande|5_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T18-26-43.065214.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T18_26_43.065214", "path": ["results_2023-12-04T18-26-43.065214.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T18-26-43.065214.parquet"]}]}]} | 2023-12-04T18:30:23+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy 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 2023-12-04T18:26:43.065214(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy 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 2023-12-04T18:26:43.065214(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Other Known Limitations",
"## Additional Information",
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"### Licensing Information",
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] | [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy 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 2023-12-04T18:26:43.065214(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy## 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 vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy 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 2023-12-04T18:26:43.065214(note that their might be results for other tasks in 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"
] |
9551c5efdbbf44fd2ce614eee97f2017e18a7619 |
# Dataset Card for Evaluation run of SUSTech/SUS-Chat-34B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/SUSTech/SUS-Chat-34B
- **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 [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B) 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_SUSTech__SUS-Chat-34B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T10:55:45.455909](https://huggingface.co/datasets/open-llm-leaderboard/details_SUSTech__SUS-Chat-34B/blob/main/results_2023-12-10T10-55-45.455909.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": 0.7604055831741483,
"acc_stderr": 0.02829305624294987,
"acc_norm": 0.7636323040929514,
"acc_norm_stderr": 0.028842862208073416,
"mc1": 0.40636474908200737,
"mc1_stderr": 0.017193835812093897,
"mc2": 0.5704122295242341,
"mc2_stderr": 0.014843409183922712
},
"harness|arc:challenge|25": {
"acc": 0.6356655290102389,
"acc_stderr": 0.014063260279882419,
"acc_norm": 0.6629692832764505,
"acc_norm_stderr": 0.013813476652902274
},
"harness|hellaswag|10": {
"acc": 0.6400119498107947,
"acc_stderr": 0.004790155370993446,
"acc_norm": 0.8390758812985462,
"acc_norm_stderr": 0.003667099594023359
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"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": 0.025648341251693605,
"acc_norm": 0.8881578947368421,
"acc_norm_stderr": 0.025648341251693605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
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}
```
### 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_SUSTech__SUS-Chat-34B | [
"region:us"
] | 2023-12-04T18:30:06+00:00 | {"pretty_name": "Evaluation run of SUSTech/SUS-Chat-34B", "dataset_summary": "Dataset automatically created during the evaluation run of model [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B) 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_SUSTech__SUS-Chat-34B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-10T10:55:45.455909](https://huggingface.co/datasets/open-llm-leaderboard/details_SUSTech__SUS-Chat-34B/blob/main/results_2023-12-10T10-55-45.455909.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\": 0.7604055831741483,\n \"acc_stderr\": 0.02829305624294987,\n \"acc_norm\": 0.7636323040929514,\n \"acc_norm_stderr\": 0.028842862208073416,\n \"mc1\": 0.40636474908200737,\n \"mc1_stderr\": 0.017193835812093897,\n \"mc2\": 0.5704122295242341,\n \"mc2_stderr\": 0.014843409183922712\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6356655290102389,\n \"acc_stderr\": 0.014063260279882419,\n \"acc_norm\": 0.6629692832764505,\n \"acc_norm_stderr\": 0.013813476652902274\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6400119498107947,\n \"acc_stderr\": 0.004790155370993446,\n \"acc_norm\": 0.8390758812985462,\n \"acc_norm_stderr\": 0.003667099594023359\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n 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["**/details_harness|hendrycksTest-security_studies|5_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["**/details_harness|winogrande|5_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["**/details_harness|winogrande|5_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-10T10-55-45.455909.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T18_27_20.173218", "path": ["results_2023-12-04T18-27-20.173218.parquet"]}, {"split": "2023_12_10T10_55_45.455909", "path": ["results_2023-12-10T10-55-45.455909.parquet"]}, {"split": "latest", "path": ["results_2023-12-10T10-55-45.455909.parquet"]}]}]} | 2023-12-10T10:58:42+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of SUSTech/SUS-Chat-34B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model SUSTech/SUS-Chat-34B 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 2023-12-10T10:55:45.455909(note that their might be results for other tasks in 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
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] |
b3ef3922debb1fe1cecb28ac8026b55b9969f093 | # Dataset Card for "semeval-task-8-a-mono-v2-test-paraphrase"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kpriyanshu256/semeval-task-8-a-mono-v2-test-paraphrase | [
"region:us"
] | 2023-12-04T18:31:39+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "model", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "paraphrase", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 17577049, "num_examples": 5000}], "download_size": 10064093, "dataset_size": 17577049}} | 2023-12-04T18:31:41+00:00 | [] | [] | TAGS
#region-us
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More Information needed | [
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3abfa80d1f1627bc4e69ffa4e76b0c0e41561ed2 |
# Dataset Card for Evaluation run of openaccess-ai-collective/dpopenhermes-alpha-v0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/dpopenhermes-alpha-v0
- **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 [openaccess-ai-collective/dpopenhermes-alpha-v0](https://huggingface.co/openaccess-ai-collective/dpopenhermes-alpha-v0) 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_openaccess-ai-collective__dpopenhermes-alpha-v0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:36:41.738939](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__dpopenhermes-alpha-v0/blob/main/results_2023-12-04T18-36-41.738939.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": 0.6371837798967929,
"acc_stderr": 0.032261818616969314,
"acc_norm": 0.6403189407162136,
"acc_norm_stderr": 0.03290301230310095,
"mc1": 0.3353733170134639,
"mc1_stderr": 0.01652753403966899,
"mc2": 0.5174503213318207,
"mc2_stderr": 0.014661651601621145
},
"harness|arc:challenge|25": {
"acc": 0.5972696245733788,
"acc_stderr": 0.014332236306790154,
"acc_norm": 0.6501706484641638,
"acc_norm_stderr": 0.013936809212158301
},
"harness|hellaswag|10": {
"acc": 0.6346345349531965,
"acc_stderr": 0.004805483767055348,
"acc_norm": 0.839573790081657,
"acc_norm_stderr": 0.003662508272330896
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"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": 0.028727502957880274,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880274
},
"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": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"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": {
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},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-conceptual_physics|5": {
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"harness|hendrycksTest-econometrics|5": {
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},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.025424835086924
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4523809523809524,
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6307692307692307,
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"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
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"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.558756633813495,
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}
}
```
### 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_openaccess-ai-collective__dpopenhermes-alpha-v0 | [
"region:us"
] | 2023-12-04T18:39:31+00:00 | {"pretty_name": "Evaluation run of openaccess-ai-collective/dpopenhermes-alpha-v0", "dataset_summary": "Dataset automatically created during the evaluation run of model [openaccess-ai-collective/dpopenhermes-alpha-v0](https://huggingface.co/openaccess-ai-collective/dpopenhermes-alpha-v0) 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_openaccess-ai-collective__dpopenhermes-alpha-v0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:36:41.738939](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__dpopenhermes-alpha-v0/blob/main/results_2023-12-04T18-36-41.738939.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\": 0.6371837798967929,\n \"acc_stderr\": 0.032261818616969314,\n \"acc_norm\": 0.6403189407162136,\n \"acc_norm_stderr\": 0.03290301230310095,\n \"mc1\": 0.3353733170134639,\n \"mc1_stderr\": 0.01652753403966899,\n \"mc2\": 0.5174503213318207,\n \"mc2_stderr\": 0.014661651601621145\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5972696245733788,\n \"acc_stderr\": 0.014332236306790154,\n \"acc_norm\": 0.6501706484641638,\n \"acc_norm_stderr\": 0.013936809212158301\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6346345349531965,\n \"acc_stderr\": 0.004805483767055348,\n \"acc_norm\": 0.839573790081657,\n \"acc_norm_stderr\": 0.003662508272330896\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\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\": 0.028727502957880274,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880274\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\": 0.52,\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\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\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\": 0.39215686274509803,\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\": 0.74,\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\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\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\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924\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\": 0.35,\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\": 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642507,\n \"acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642507\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.5270935960591133,\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\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483016,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483016\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\": 0.8756476683937824,\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\": 0.6307692307692307,\n \"acc_stderr\": 0.024468615241478923,\n \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.024468615241478923\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.027940457136228405,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.027940457136228405\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.030489911417673227,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.030489911417673227\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\": 0.8385321100917431,\n \"acc_stderr\": 0.01577623925616323,\n \"acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.01577623925616323\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\": 0.803921568627451,\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\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.03076935200822914\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\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-36-41.738939.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-36-41.738939.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T18_36_41.738939", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-36-41.738939.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T18-36-41.738939.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T18_36_41.738939", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-36-41.738939.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-36-41.738939.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T18_36_41.738939", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-36-41.738939.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T18-36-41.738939.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T18_36_41.738939", "path": ["**/details_harness|winogrande|5_2023-12-04T18-36-41.738939.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T18-36-41.738939.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T18_36_41.738939", "path": ["results_2023-12-04T18-36-41.738939.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T18-36-41.738939.parquet"]}]}]} | 2023-12-04T18:40:15+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of openaccess-ai-collective/dpopenhermes-alpha-v0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model openaccess-ai-collective/dpopenhermes-alpha-v0 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 2023-12-04T18:36:41.738939(note that their might be results for other tasks in 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
| [
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] |
78f86ece55611c1e1b45941ff9015512d1a165c4 |
## Descrição do Dataset Description
Dataset com questões do ENEM (Exame Nacional do Ensino Médio) de diversos anos e áreas de conhecimento.
Dataset utilizado para atividades da disciplina de Processamento de Linguagem Natural em 2023/2 no INF/UFG.
## Estrutura do Dataset
- content (contexto da questão)
- prompt (pergunta)
- A (alternativa A)
- B (alternativa B)
- C (alternativa C)
- D (alternativa D)
- E (alternativa E)
- answer (alternativa correta)
### Splits
- Train (1382)
- Validation (276)
- Test (185) | douglasrolins/enem-sample | [
"region:us"
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#region-us
|
## Descrição do Dataset Description
Dataset com questões do ENEM (Exame Nacional do Ensino Médio) de diversos anos e áreas de conhecimento.
Dataset utilizado para atividades da disciplina de Processamento de Linguagem Natural em 2023/2 no INF/UFG.
## Estrutura do Dataset
- content (contexto da questão)
- prompt (pergunta)
- A (alternativa A)
- B (alternativa B)
- C (alternativa C)
- D (alternativa D)
- E (alternativa E)
- answer (alternativa correta)
### Splits
- Train (1382)
- Validation (276)
- Test (185) | [
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] |
336d63bb470c28dd016d02776453366a347770b4 |
# Dataset Card for Evaluation run of mergedlm/zephyrnotus-11b-alpha
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mergedlm/zephyrnotus-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 [mergedlm/zephyrnotus-11b-alpha](https://huggingface.co/mergedlm/zephyrnotus-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_mergedlm__zephyrnotus-11b-alpha",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:58:32.292259](https://huggingface.co/datasets/open-llm-leaderboard/details_mergedlm__zephyrnotus-11b-alpha/blob/main/results_2023-12-04T18-58-32.292259.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": 0.6022156893041962,
"acc_stderr": 0.03336144237047965,
"acc_norm": 0.6105212360689912,
"acc_norm_stderr": 0.03409373060010593,
"mc1": 0.401468788249694,
"mc1_stderr": 0.017160273901693654,
"mc2": 0.5721680885718956,
"mc2_stderr": 0.015636158796667236
},
"harness|arc:challenge|25": {
"acc": 0.5853242320819113,
"acc_stderr": 0.014397070564409174,
"acc_norm": 0.613481228668942,
"acc_norm_stderr": 0.014230084761910478
},
"harness|hellaswag|10": {
"acc": 0.6352320254929297,
"acc_stderr": 0.004803812631994954,
"acc_norm": 0.8280223063134834,
"acc_norm_stderr": 0.003765898364938865
},
"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": 0.562962962962963,
"acc_stderr": 0.04284958639753401,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.04284958639753401
},
"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": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.660377358490566,
"acc_stderr": 0.029146904747798335,
"acc_norm": 0.660377358490566,
"acc_norm_stderr": 0.029146904747798335
},
"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": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"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": 0.6127167630057804,
"acc_stderr": 0.03714325906302065,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.03714325906302065
},
"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": {
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"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5361702127659574,
"acc_stderr": 0.03260038511835772,
"acc_norm": 0.5361702127659574,
"acc_norm_stderr": 0.03260038511835772
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04697085136647863
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.04161808503501531,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.04161808503501531
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.3915343915343915,
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"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.42857142857142855,
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.7741935483870968,
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"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.4876847290640394,
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"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_stderr": 0.03567969772268049,
"acc_norm": 0.703030303030303,
"acc_norm_stderr": 0.03567969772268049
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365907,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365907
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8186528497409327,
"acc_stderr": 0.02780703236068609,
"acc_norm": 0.8186528497409327,
"acc_norm_stderr": 0.02780703236068609
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6153846153846154,
"acc_stderr": 0.024666744915187208,
"acc_norm": 0.6153846153846154,
"acc_norm_stderr": 0.024666744915187208
},
"harness|hendrycksTest-high_school_mathematics|5": {
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}
```
### 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_mergedlm__zephyrnotus-11b-alpha | [
"region:us"
] | 2023-12-04T19:01:27+00:00 | {"pretty_name": "Evaluation run of mergedlm/zephyrnotus-11b-alpha", "dataset_summary": "Dataset automatically created during the evaluation run of model [mergedlm/zephyrnotus-11b-alpha](https://huggingface.co/mergedlm/zephyrnotus-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_mergedlm__zephyrnotus-11b-alpha\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:58:32.292259](https://huggingface.co/datasets/open-llm-leaderboard/details_mergedlm__zephyrnotus-11b-alpha/blob/main/results_2023-12-04T18-58-32.292259.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\": 0.6022156893041962,\n \"acc_stderr\": 0.03336144237047965,\n \"acc_norm\": 0.6105212360689912,\n \"acc_norm_stderr\": 0.03409373060010593,\n \"mc1\": 0.401468788249694,\n \"mc1_stderr\": 0.017160273901693654,\n \"mc2\": 0.5721680885718956,\n \"mc2_stderr\": 0.015636158796667236\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5853242320819113,\n \"acc_stderr\": 0.014397070564409174,\n \"acc_norm\": 0.613481228668942,\n \"acc_norm_stderr\": 0.014230084761910478\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6352320254929297,\n \"acc_stderr\": 0.004803812631994954,\n \"acc_norm\": 0.8280223063134834,\n \"acc_norm_stderr\": 0.003765898364938865\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\": 0.562962962962963,\n \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n \"acc_norm_stderr\": 0.04284958639753401\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\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.029146904747798335,\n \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.029146904747798335\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\": 0.51,\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\": 0.39,\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\": 0.6127167630057804,\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\": 0.4117647058823529,\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n 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0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181015\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365907,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365907\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6153846153846154,\n \"acc_stderr\": 0.024666744915187208,\n \"acc_norm\": 0.6153846153846154,\n \"acc_norm_stderr\": 0.024666744915187208\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524572,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524572\n },\n 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0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145628\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7046413502109705,\n \"acc_stderr\": 0.029696338713422876,\n \"acc_norm\": 0.7046413502109705,\n \"acc_norm_stderr\": 0.029696338713422876\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\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\": 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n 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#region-us
|
# Dataset Card for Evaluation run of mergedlm/zephyrnotus-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 mergedlm/zephyrnotus-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 2023-12-04T18:58:32.292259(note that their might be results for other tasks in 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
| [
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"## Latest results\n\nThese are the latest results from run 2023-12-04T18:58:32.292259(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mergedlm/zephyrnotus-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 mergedlm/zephyrnotus-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 2023-12-04T18:58:32.292259(note that their might be results for other tasks in 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"
] |
04902f7292d4e59aba3187da0dcfdef95975fc6e |
# Dataset Card for Evaluation run of Q-bert/Optimus-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Q-bert/Optimus-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/Optimus-7B](https://huggingface.co/Q-bert/Optimus-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__Optimus-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:59:49.207215](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Optimus-7B/blob/main/results_2023-12-04T18-59-49.207215.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": 0.6392766919486776,
"acc_stderr": 0.03229323039450184,
"acc_norm": 0.6401046235645558,
"acc_norm_stderr": 0.032947292086342644,
"mc1": 0.3953488372093023,
"mc1_stderr": 0.017115815632418197,
"mc2": 0.5578912654610536,
"mc2_stderr": 0.015509983004926231
},
"harness|arc:challenge|25": {
"acc": 0.6271331058020477,
"acc_stderr": 0.01413117676013117,
"acc_norm": 0.6544368600682594,
"acc_norm_stderr": 0.013896938461145673
},
"harness|hellaswag|10": {
"acc": 0.6683927504481179,
"acc_stderr": 0.004698285350019212,
"acc_norm": 0.8541127265484963,
"acc_norm_stderr": 0.003522717499524299
},
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"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
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"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": 0.6,
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"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
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},
"harness|hendrycksTest-college_biology|5": {
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},
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},
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},
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},
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},
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},
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},
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"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm_stderr": 0.02425790170532338
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.39553072625698327,
"acc_norm_stderr": 0.016353415410075775
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
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"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818737
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153266
},
"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.024748624490537368,
"acc_norm": 0.7283950617283951,
"acc_norm_stderr": 0.024748624490537368
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.43617021276595747,
"acc_stderr": 0.02958345203628407,
"acc_norm": 0.43617021276595747,
"acc_norm_stderr": 0.02958345203628407
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"harness|hendrycksTest-professional_law|5": {
"acc": 0.4621903520208605,
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"acc_norm": 0.4621903520208605,
"acc_norm_stderr": 0.012733671880342506
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.6691176470588235,
"acc_norm_stderr": 0.02858270975389845
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.018975427920507208
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"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644286
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.746938775510204,
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"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.02783302387139968
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"harness|hendrycksTest-sociology|5": {
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"acc_stderr": 0.02484575321230604,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.02484575321230604
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
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"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
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"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": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3953488372093023,
"mc1_stderr": 0.017115815632418197,
"mc2": 0.5578912654610536,
"mc2_stderr": 0.015509983004926231
},
"harness|winogrande|5": {
"acc": 0.7876874506708761,
"acc_stderr": 0.01149338468724978
},
"harness|gsm8k|5": {
"acc": 0.6550416982562547,
"acc_stderr": 0.013093630133666235
}
}
```
### 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__Optimus-7B | [
"region:us"
] | 2023-12-04T19:02:47+00:00 | {"pretty_name": "Evaluation run of Q-bert/Optimus-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Q-bert/Optimus-7B](https://huggingface.co/Q-bert/Optimus-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__Optimus-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T18:59:49.207215](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Optimus-7B/blob/main/results_2023-12-04T18-59-49.207215.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\": 0.6392766919486776,\n \"acc_stderr\": 0.03229323039450184,\n \"acc_norm\": 0.6401046235645558,\n \"acc_norm_stderr\": 0.032947292086342644,\n \"mc1\": 0.3953488372093023,\n \"mc1_stderr\": 0.017115815632418197,\n \"mc2\": 0.5578912654610536,\n \"mc2_stderr\": 0.015509983004926231\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.01413117676013117,\n \"acc_norm\": 0.6544368600682594,\n \"acc_norm_stderr\": 0.013896938461145673\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6683927504481179,\n \"acc_stderr\": 0.004698285350019212,\n \"acc_norm\": 0.8541127265484963,\n \"acc_norm_stderr\": 0.003522717499524299\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\": 0.6907894736842105,\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\": 0.6,\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\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\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\": 0.47,\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\": 0.5,\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\": 0.34,\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\": 0.630057803468208,\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\": 0.4117647058823529,\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\": 0.8,\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\": 0.03227834510146267,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146267\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\": 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778394,\n \"acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778394\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\": 0.38,\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\": 0.7677419354838709,\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\": 0.4975369458128079,\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\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\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\": 0.7676767676767676,\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\": 0.8860103626943006,\n \"acc_stderr\": 0.02293514405391945,\n \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.02293514405391945\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094767,\n \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094767\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8220183486238533,\n \"acc_stderr\": 0.01639943636661292,\n \"acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.01639943636661292\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\": 0.8137254901960784,\n \"acc_stderr\": 0.02732547096671631,\n \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02732547096671631\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069432,\n \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069432\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\": 0.7709923664122137,\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\": 0.7870370370370371,\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\": 0.7914110429447853,\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\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.022801382534597528\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\": 0.8212005108556832,\n \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39553072625698327,\n \"acc_stderr\": 0.016353415410075775,\n \"acc_norm\": 0.39553072625698327,\n \"acc_norm_stderr\": 0.016353415410075775\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n \"acc_norm_stderr\": 0.026236965881153266\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\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4621903520208605,\n \"acc_stderr\": 0.012733671880342506,\n \"acc_norm\": 0.4621903520208605,\n \"acc_norm_stderr\": 0.012733671880342506\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\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507208,\n \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507208\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04389311454644286,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04389311454644286\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n \"acc_stderr\": 0.02484575321230604,\n \"acc_norm\": 0.8557213930348259,\n \"acc_norm_stderr\": 0.02484575321230604\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\": 0.5240963855421686,\n \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n \"acc_norm_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of Q-bert/Optimus-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/Optimus-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 2023-12-04T18:59:49.207215(note that their might be results for other tasks in 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/Optimus-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/Optimus-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 2023-12-04T18:59:49.207215(note that their might be results for other tasks in 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/Optimus-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/Optimus-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 2023-12-04T18:59:49.207215(note that their might be results for other tasks in 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"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Q-bert/Optimus-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/Optimus-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 2023-12-04T18:59:49.207215(note that their might be results for other tasks in 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"
] |
5fa5b832f764eaa830c9138937d5a575350e72a6 |
# Dataset Card for Evaluation run of Q-bert/Bumblebee-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Q-bert/Bumblebee-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/Bumblebee-7B](https://huggingface.co/Q-bert/Bumblebee-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__Bumblebee-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:02:58.959245](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Bumblebee-7B/blob/main/results_2023-12-04T19-02-58.959245.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": 0.6423559079822854,
"acc_stderr": 0.03212728779652208,
"acc_norm": 0.6433195276283713,
"acc_norm_stderr": 0.032776334270389784,
"mc1": 0.35495716034271724,
"mc1_stderr": 0.0167508623813759,
"mc2": 0.5095643475017586,
"mc2_stderr": 0.015572340523512473
},
"harness|arc:challenge|25": {
"acc": 0.6092150170648464,
"acc_stderr": 0.014258563880513778,
"acc_norm": 0.6339590443686007,
"acc_norm_stderr": 0.014077223108470137
},
"harness|hellaswag|10": {
"acc": 0.6554471220872337,
"acc_stderr": 0.0047425103547779025,
"acc_norm": 0.8415654252141008,
"acc_norm_stderr": 0.0036440173837115923
},
"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": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"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": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"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": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"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": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
"acc_stderr": 0.03703851193099521,
"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.03703851193099521
},
"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": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
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"acc": 0.6,
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"acc_norm": 0.6,
"acc_norm_stderr": 0.03202563076101736
},
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.8963730569948186,
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.031041941304059288
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"harness|hendrycksTest-high_school_physics|5": {
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"acc_stderr": 0.03684881521389023,
"acc_norm": 0.2847682119205298,
"acc_norm_stderr": 0.03684881521389023
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm_stderr": 0.03259177392742178
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"acc_norm": 0.8675213675213675,
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"acc_norm": 0.37094972067039106,
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7254901960784313,
"acc_norm_stderr": 0.025553169991826524
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"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
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"acc_norm": 0.7041800643086816,
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"acc_norm": 0.7222222222222222,
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"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.4858156028368794,
"acc_norm_stderr": 0.02981549448368206
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"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.845771144278607,
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"harness|truthfulqa:mc|0": {
"mc1": 0.35495716034271724,
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"mc2": 0.5095643475017586,
"mc2_stderr": 0.015572340523512473
},
"harness|winogrande|5": {
"acc": 0.7821625887924231,
"acc_stderr": 0.011601066079939324
},
"harness|gsm8k|5": {
"acc": 0.6565579984836998,
"acc_stderr": 0.013079933811800304
}
}
```
### 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__Bumblebee-7B | [
"region:us"
] | 2023-12-04T19:05:58+00:00 | {"pretty_name": "Evaluation run of Q-bert/Bumblebee-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Q-bert/Bumblebee-7B](https://huggingface.co/Q-bert/Bumblebee-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__Bumblebee-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:02:58.959245](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Bumblebee-7B/blob/main/results_2023-12-04T19-02-58.959245.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\": 0.6423559079822854,\n \"acc_stderr\": 0.03212728779652208,\n \"acc_norm\": 0.6433195276283713,\n \"acc_norm_stderr\": 0.032776334270389784,\n \"mc1\": 0.35495716034271724,\n \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5095643475017586,\n \"mc2_stderr\": 0.015572340523512473\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6092150170648464,\n \"acc_stderr\": 0.014258563880513778,\n \"acc_norm\": 0.6339590443686007,\n \"acc_norm_stderr\": 0.014077223108470137\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6554471220872337,\n \"acc_stderr\": 0.0047425103547779025,\n \"acc_norm\": 0.8415654252141008,\n \"acc_norm_stderr\": 0.0036440173837115923\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\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\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\": 0.6,\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\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\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\": 0.47,\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\": 0.53,\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\": 0.35,\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\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\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\": 0.8,\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\": 0.6,\n \"acc_stderr\": 0.03202563076101736,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03202563076101736\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\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\": 0.02544636563440679,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440679\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.044444444444444495\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\": 0.7645161290322581,\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\": 0.4975369458128079,\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\": 0.7575757575757576,\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\": 0.03008862949021749,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.03008862949021749\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\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\": 0.37037037037037035,\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\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059288,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059288\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8275229357798165,\n \"acc_stderr\": 0.01619780795684805,\n \"acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.01619780795684805\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977748,\n \"acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977748\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.02704462171947409,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.02704462171947409\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\": 0.6905829596412556,\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\": 0.03547771004159464,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159464\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.047427623612430116\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\": 0.8675213675213675,\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\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n \"acc_stderr\": 0.013547415658662253,\n \"acc_norm\": 0.8263090676883781,\n \"acc_norm_stderr\": 0.013547415658662253\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\": 0.37094972067039106,\n \"acc_stderr\": 0.016155910721341767,\n \"acc_norm\": 0.37094972067039106,\n \"acc_norm_stderr\": 0.016155910721341767\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\": 0.7041800643086816,\n \"acc_stderr\": 0.025922371788818767,\n \"acc_norm\": 0.7041800643086816,\n \"acc_norm_stderr\": 0.025922371788818767\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\": 0.4858156028368794,\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\": 0.4634941329856584,\n \"acc_stderr\": 0.012736153390214963,\n \"acc_norm\": 0.4634941329856584,\n \"acc_norm_stderr\": 0.012736153390214963\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\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\": 0.6363636363636364,\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\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.02553843336857833\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\": 0.536144578313253,\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\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5095643475017586,\n \"mc2_stderr\": 0.015572340523512473\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6565579984836998,\n \"acc_stderr\": 0.013079933811800304\n }\n}\n```", "repo_url": "https://huggingface.co/Q-bert/Bumblebee-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_04T19_02_58.959245", "path": ["**/details_harness|arc:challenge|25_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|gsm8k|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hellaswag|10_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T19-02-58.959245.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T19-02-58.959245.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-human_aging|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T19-02-58.959245.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T19-02-58.959245.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T19_02_58.959245", "path": 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#region-us
|
# Dataset Card for Evaluation run of Q-bert/Bumblebee-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/Bumblebee-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 2023-12-04T19:02:58.959245(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Q-bert/Bumblebee-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 2023-12-04T19:02:58.959245(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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] | [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Q-bert/Bumblebee-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 2023-12-04T19:02:58.959245(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Q-bert/Bumblebee-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/Bumblebee-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 2023-12-04T19:02:58.959245(note that their might be results for other tasks in 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"
] |
7b50036b20f141b65097e3c4afaa38f1d7fa186b |
# Dataset Card for Evaluation run of rufjdk5480/llama-7b-ludwig-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rufjdk5480/llama-7b-ludwig-alpaca
- **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 [rufjdk5480/llama-7b-ludwig-alpaca](https://huggingface.co/rufjdk5480/llama-7b-ludwig-alpaca) 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_rufjdk5480__llama-7b-ludwig-alpaca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:02:43.278164](https://huggingface.co/datasets/open-llm-leaderboard/details_rufjdk5480__llama-7b-ludwig-alpaca/blob/main/results_2023-12-04T19-02-43.278164.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": 0.46060379742069524,
"acc_stderr": 0.03441125676761374,
"acc_norm": 0.46498436629120626,
"acc_norm_stderr": 0.03518871840894695,
"mc1": 0.2864137086903305,
"mc1_stderr": 0.015826142439502353,
"mc2": 0.4191386468811098,
"mc2_stderr": 0.014271816029328676
},
"harness|arc:challenge|25": {
"acc": 0.5085324232081911,
"acc_stderr": 0.014609263165632182,
"acc_norm": 0.5401023890784983,
"acc_norm_stderr": 0.01456431885692485
},
"harness|hellaswag|10": {
"acc": 0.5903206532563234,
"acc_stderr": 0.004907694727935688,
"acc_norm": 0.7872933678550089,
"acc_norm_stderr": 0.004083855139469325
},
"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": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4276315789473684,
"acc_stderr": 0.04026097083296558,
"acc_norm": 0.4276315789473684,
"acc_norm_stderr": 0.04026097083296558
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.44150943396226416,
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"harness|gsm8k|5": {
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}
```
### 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_rufjdk5480__llama-7b-ludwig-alpaca | [
"region:us"
] | 2023-12-04T19:05:58+00:00 | {"pretty_name": "Evaluation run of rufjdk5480/llama-7b-ludwig-alpaca", "dataset_summary": "Dataset automatically created during the evaluation run of model [rufjdk5480/llama-7b-ludwig-alpaca](https://huggingface.co/rufjdk5480/llama-7b-ludwig-alpaca) 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_rufjdk5480__llama-7b-ludwig-alpaca\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:02:43.278164](https://huggingface.co/datasets/open-llm-leaderboard/details_rufjdk5480__llama-7b-ludwig-alpaca/blob/main/results_2023-12-04T19-02-43.278164.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\": 0.46060379742069524,\n \"acc_stderr\": 0.03441125676761374,\n \"acc_norm\": 0.46498436629120626,\n \"acc_norm_stderr\": 0.03518871840894695,\n \"mc1\": 0.2864137086903305,\n \"mc1_stderr\": 0.015826142439502353,\n \"mc2\": 0.4191386468811098,\n \"mc2_stderr\": 0.014271816029328676\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5085324232081911,\n \"acc_stderr\": 0.014609263165632182,\n \"acc_norm\": 0.5401023890784983,\n \"acc_norm_stderr\": 0.01456431885692485\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5903206532563234,\n \"acc_stderr\": 0.004907694727935688,\n \"acc_norm\": 0.7872933678550089,\n \"acc_norm_stderr\": 0.004083855139469325\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\": 0.4666666666666667,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.04026097083296558,\n \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.04026097083296558\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.44150943396226416,\n \"acc_stderr\": 0.03056159042673184,\n \"acc_norm\": 0.44150943396226416,\n \"acc_norm_stderr\": 0.03056159042673184\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04148415739394154\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\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\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\": 0.42196531791907516,\n \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.42196531791907516,\n \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n 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["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["**/details_harness|winogrande|5_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T19-02-43.278164.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T19_02_43.278164", "path": ["results_2023-12-04T19-02-43.278164.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T19-02-43.278164.parquet"]}]}]} | 2023-12-04T19:06:50+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of rufjdk5480/llama-7b-ludwig-alpaca
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model rufjdk5480/llama-7b-ludwig-alpaca 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 2023-12-04T19:02:43.278164(note that their might be results for other tasks in 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
| [
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of rufjdk5480/llama-7b-ludwig-alpaca## 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 rufjdk5480/llama-7b-ludwig-alpaca 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 2023-12-04T19:02:43.278164(note that their might be results for other tasks in 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"
] |
87622b7ed76f2cd4ed258db52b1f643c39643ed4 |
# Dataset Card for Evaluation run of KnutJaegersberg/falcon-1b-t-sft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/falcon-1b-t-sft
- **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/falcon-1b-t-sft](https://huggingface.co/KnutJaegersberg/falcon-1b-t-sft) 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__falcon-1b-t-sft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:05:57.412781](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__falcon-1b-t-sft/blob/main/results_2023-12-04T19-05-57.412781.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": 0.2571482773686455,
"acc_stderr": 0.030827593737624604,
"acc_norm": 0.2593690477702816,
"acc_norm_stderr": 0.03161954080445179,
"mc1": 0.23255813953488372,
"mc1_stderr": 0.014789157531080508,
"mc2": 0.38486056709707445,
"mc2_stderr": 0.015385392751923936
},
"harness|arc:challenge|25": {
"acc": 0.2841296928327645,
"acc_stderr": 0.013179442447653887,
"acc_norm": 0.3293515358361775,
"acc_norm_stderr": 0.013734057652635474
},
"harness|hellaswag|10": {
"acc": 0.43905596494722166,
"acc_stderr": 0.004952576863315219,
"acc_norm": 0.5723959370643298,
"acc_norm_stderr": 0.004937199759947685
},
"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": 0.34074074074074073,
"acc_stderr": 0.04094376269996793,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.04094376269996793
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.28289473684210525,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.28289473684210525,
"acc_norm_stderr": 0.03665349695640767
},
"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": 0.2037735849056604,
"acc_stderr": 0.0247907845017754,
"acc_norm": 0.2037735849056604,
"acc_norm_stderr": 0.0247907845017754
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.25,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03621034121889507
},
"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": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2254335260115607,
"acc_stderr": 0.031862098516411426,
"acc_norm": 0.2254335260115607,
"acc_norm_stderr": 0.031862098516411426
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.20588235294117646,
"acc_stderr": 0.04023382273617746,
"acc_norm": 0.20588235294117646,
"acc_norm_stderr": 0.04023382273617746
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2,
"acc_stderr": 0.026148818018424495,
"acc_norm": 0.2,
"acc_norm_stderr": 0.026148818018424495
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.21929824561403508,
"acc_stderr": 0.03892431106518754,
"acc_norm": 0.21929824561403508,
"acc_norm_stderr": 0.03892431106518754
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2827586206896552,
"acc_stderr": 0.03752833958003336,
"acc_norm": 0.2827586206896552,
"acc_norm_stderr": 0.03752833958003336
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.023068188848261117,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.023068188848261117
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.1984126984126984,
"acc_stderr": 0.03567016675276861,
"acc_norm": 0.1984126984126984,
"acc_norm_stderr": 0.03567016675276861
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
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"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.23225806451612904,
"acc_stderr": 0.024022256130308235,
"acc_norm": 0.23225806451612904,
"acc_norm_stderr": 0.024022256130308235
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.2561576354679803,
"acc_norm_stderr": 0.030712730070982592
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.2787878787878788,
"acc_stderr": 0.03501438706296781,
"acc_norm": 0.2787878787878788,
"acc_norm_stderr": 0.03501438706296781
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.029620227874790482,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.029620227874790482
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.21243523316062177,
"acc_stderr": 0.029519282616817258,
"acc_norm": 0.21243523316062177,
"acc_norm_stderr": 0.029519282616817258
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2076923076923077,
"acc_stderr": 0.020567539567246797,
"acc_norm": 0.2076923076923077,
"acc_norm_stderr": 0.020567539567246797
},
"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": 0.22268907563025211,
"acc_stderr": 0.027025433498882374,
"acc_norm": 0.22268907563025211,
"acc_norm_stderr": 0.027025433498882374
},
"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|winogrande|5": {
"acc": 0.5588003157063931,
"acc_stderr": 0.013954975072834738
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245449
}
}
```
### 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__falcon-1b-t-sft | [
"region:us"
] | 2023-12-04T19:08:04+00:00 | {"pretty_name": "Evaluation run of KnutJaegersberg/falcon-1b-t-sft", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/falcon-1b-t-sft](https://huggingface.co/KnutJaegersberg/falcon-1b-t-sft) 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__falcon-1b-t-sft\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:05:57.412781](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__falcon-1b-t-sft/blob/main/results_2023-12-04T19-05-57.412781.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\": 0.2571482773686455,\n \"acc_stderr\": 0.030827593737624604,\n \"acc_norm\": 0.2593690477702816,\n \"acc_norm_stderr\": 0.03161954080445179,\n \"mc1\": 0.23255813953488372,\n \"mc1_stderr\": 0.014789157531080508,\n \"mc2\": 0.38486056709707445,\n \"mc2_stderr\": 0.015385392751923936\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.2841296928327645,\n \"acc_stderr\": 0.013179442447653887,\n \"acc_norm\": 0.3293515358361775,\n \"acc_norm_stderr\": 0.013734057652635474\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.43905596494722166,\n \"acc_stderr\": 0.004952576863315219,\n \"acc_norm\": 0.5723959370643298,\n \"acc_norm_stderr\": 0.004937199759947685\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\": 0.34074074074074073,\n \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.28289473684210525,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.28289473684210525,\n \"acc_norm_stderr\": 0.03665349695640767\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\": 0.2037735849056604,\n \"acc_stderr\": 0.0247907845017754,\n \"acc_norm\": 0.2037735849056604,\n \"acc_norm_stderr\": 0.0247907845017754\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03621034121889507\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\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.2254335260115607,\n \"acc_stderr\": 0.031862098516411426,\n \"acc_norm\": 0.2254335260115607,\n \"acc_norm_stderr\": 0.031862098516411426\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617746,\n \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617746\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.026148818018424495,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.026148818018424495\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2827586206896552,\n \"acc_stderr\": 0.03752833958003336,\n \"acc_norm\": 0.2827586206896552,\n \"acc_norm_stderr\": 0.03752833958003336\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.023068188848261117,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.023068188848261117\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1984126984126984,\n \"acc_stderr\": 0.03567016675276861,\n \"acc_norm\": 0.1984126984126984,\n \"acc_norm_stderr\": 0.03567016675276861\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\": 0.23225806451612904,\n \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.23225806451612904,\n \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.030712730070982592,\n \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.030712730070982592\n },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\": {\n \"acc\": 0.2787878787878788,\n \"acc_stderr\": 0.03501438706296781,\n \"acc_norm\": 0.2787878787878788,\n \"acc_norm_stderr\": 0.03501438706296781\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.029620227874790482,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.029620227874790482\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.029519282616817258,\n \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.029519282616817258\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2076923076923077,\n \"acc_stderr\": 0.020567539567246797,\n \"acc_norm\": 0.2076923076923077,\n \"acc_norm_stderr\": 0.020567539567246797\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\": 0.22268907563025211,\n \"acc_stderr\": 0.027025433498882374,\n \"acc_norm\": 0.22268907563025211,\n \"acc_norm_stderr\": 0.027025433498882374\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.26490066225165565,\n \"acc_stderr\": 0.03603038545360384,\n \"acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.03603038545360384\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22201834862385322,\n \"acc_stderr\": 0.01781884956479661,\n \"acc_norm\": 0.22201834862385322,\n \"acc_norm_stderr\": 0.01781884956479661\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.19907407407407407,\n \"acc_stderr\": 0.027232298462690225,\n \"acc_norm\": 0.19907407407407407,\n \"acc_norm_stderr\": 0.027232298462690225\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.03132179803083292,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.03132179803083292\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2645739910313901,\n \"acc_stderr\": 0.029605103217038325,\n \"acc_norm\": 0.2645739910313901,\n \"acc_norm_stderr\": 0.029605103217038325\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.25190839694656486,\n \"acc_stderr\": 0.03807387116306086,\n \"acc_norm\": 0.25190839694656486,\n \"acc_norm_stderr\": 0.03807387116306086\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.3140495867768595,\n \"acc_stderr\": 0.04236964753041018,\n \"acc_norm\": 0.3140495867768595,\n \"acc_norm_stderr\": 0.04236964753041018\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.2222222222222222,\n 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["**/details_harness|truthfulqa:mc|0_2023-12-04T19-05-57.412781.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T19-05-57.412781.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T19_05_57.412781", "path": ["**/details_harness|winogrande|5_2023-12-04T19-05-57.412781.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T19-05-57.412781.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T19_05_57.412781", "path": ["results_2023-12-04T19-05-57.412781.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T19-05-57.412781.parquet"]}]}]} | 2023-12-04T19:08:51+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/falcon-1b-t-sft
## 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/falcon-1b-t-sft 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 2023-12-04T19:05:57.412781(note that their might be results for other tasks in 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
| [
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"## Latest results\n\nThese are the latest results from run 2023-12-04T19:05:57.412781(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/falcon-1b-t-sft## 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/falcon-1b-t-sft 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 2023-12-04T19:05:57.412781(note that their might be results for other tasks in 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"
] |
65505e7ed6640e7dc8592e74715b23091e703ce0 |
# Dataset Card for Evaluation run of beomi/Yi-Ko-6B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beomi/Yi-Ko-6B
- **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 [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B) 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_beomi__Yi-Ko-6B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:08:16.844680](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__Yi-Ko-6B/blob/main/results_2023-12-04T19-08-16.844680.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": 0.5511585458954897,
"acc_stderr": 0.03372606124688932,
"acc_norm": 0.5592593520510756,
"acc_norm_stderr": 0.034493739566800206,
"mc1": 0.24479804161566707,
"mc1_stderr": 0.015051869486715014,
"mc2": 0.37094583310302975,
"mc2_stderr": 0.013820249952756731
},
"harness|arc:challenge|25": {
"acc": 0.454778156996587,
"acc_stderr": 0.014551507060836355,
"acc_norm": 0.48890784982935154,
"acc_norm_stderr": 0.01460779491401305
},
"harness|hellaswag|10": {
"acc": 0.5488946425014938,
"acc_stderr": 0.004965866098318173,
"acc_norm": 0.7447719577773352,
"acc_norm_stderr": 0.004350982826580606
},
"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": 0.5185185185185185,
"acc_stderr": 0.04316378599511324,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.04316378599511324
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.04017901275981749,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.04017901275981749
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5849056603773585,
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"acc_norm": 0.5849056603773585,
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},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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},
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},
"harness|hendrycksTest-college_medicine|5": {
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},
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},
"harness|hendrycksTest-computer_security|5": {
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},
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},
"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5864197530864198,
"acc_stderr": 0.027402042040269966,
"acc_norm": 0.5864197530864198,
"acc_norm_stderr": 0.027402042040269966
},
"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": 0.41395045632333766,
"acc_stderr": 0.012579699631289265,
"acc_norm": 0.41395045632333766,
"acc_norm_stderr": 0.012579699631289265
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5661764705882353,
"acc_stderr": 0.030105636570016636,
"acc_norm": 0.5661764705882353,
"acc_norm_stderr": 0.030105636570016636
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5375816993464052,
"acc_stderr": 0.020170614974969768,
"acc_norm": 0.5375816993464052,
"acc_norm_stderr": 0.020170614974969768
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302505,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302505
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6285714285714286,
"acc_stderr": 0.03093285879278985,
"acc_norm": 0.6285714285714286,
"acc_norm_stderr": 0.03093285879278985
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7611940298507462,
"acc_stderr": 0.030147775935409217,
"acc_norm": 0.7611940298507462,
"acc_norm_stderr": 0.030147775935409217
},
"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": 0.4397590361445783,
"acc_stderr": 0.03864139923699121,
"acc_norm": 0.4397590361445783,
"acc_norm_stderr": 0.03864139923699121
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7134502923976608,
"acc_stderr": 0.03467826685703826,
"acc_norm": 0.7134502923976608,
"acc_norm_stderr": 0.03467826685703826
},
"harness|truthfulqa:mc|0": {
"mc1": 0.24479804161566707,
"mc1_stderr": 0.015051869486715014,
"mc2": 0.37094583310302975,
"mc2_stderr": 0.013820249952756731
},
"harness|winogrande|5": {
"acc": 0.7292817679558011,
"acc_stderr": 0.012487904760626304
},
"harness|gsm8k|5": {
"acc": 0.12509476876421532,
"acc_stderr": 0.009112601439849625
}
}
```
### 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_beomi__Yi-Ko-6B | [
"region:us"
] | 2023-12-04T19:10:25+00:00 | {"pretty_name": "Evaluation run of beomi/Yi-Ko-6B", "dataset_summary": "Dataset automatically created during the evaluation run of model [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B) 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_beomi__Yi-Ko-6B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:08:16.844680](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__Yi-Ko-6B/blob/main/results_2023-12-04T19-08-16.844680.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\": 0.5511585458954897,\n \"acc_stderr\": 0.03372606124688932,\n \"acc_norm\": 0.5592593520510756,\n \"acc_norm_stderr\": 0.034493739566800206,\n \"mc1\": 0.24479804161566707,\n \"mc1_stderr\": 0.015051869486715014,\n \"mc2\": 0.37094583310302975,\n \"mc2_stderr\": 0.013820249952756731\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.454778156996587,\n \"acc_stderr\": 0.014551507060836355,\n \"acc_norm\": 0.48890784982935154,\n \"acc_norm_stderr\": 0.01460779491401305\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5488946425014938,\n \"acc_stderr\": 0.004965866098318173,\n \"acc_norm\": 0.7447719577773352,\n \"acc_norm_stderr\": 0.004350982826580606\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\": 0.5185185185185185,\n \"acc_stderr\": 0.04316378599511324,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.04316378599511324\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.04017901275981749,\n \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.04017901275981749\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\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\": 0.6180555555555556,\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\": 0.39,\n \"acc_stderr\": 0.049020713000019756,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.049020713000019756\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\": 0.33,\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\": 0.5433526011560693,\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\": 0.3137254901960784,\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\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.03267862331014063,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.03267862331014063\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.044346007015849245\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\": 0.35714285714285715,\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\": 0.25396825396825395,\n \"acc_stderr\": 0.03893259610604674,\n \"acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.03893259610604674\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.635483870967742,\n \"acc_stderr\": 0.02737987122994325,\n \"acc_norm\": 0.635483870967742,\n \"acc_norm_stderr\": 0.02737987122994325\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\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.037131580674819135,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.037131580674819135\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.702020202020202,\n \"acc_stderr\": 0.03258630383836556,\n \"acc_norm\": 0.702020202020202,\n \"acc_norm_stderr\": 0.03258630383836556\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8134715025906736,\n \"acc_stderr\": 0.028112091210117467,\n \"acc_norm\": 0.8134715025906736,\n \"acc_norm_stderr\": 0.028112091210117467\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5897435897435898,\n \"acc_stderr\": 0.02493931390694079,\n \"acc_norm\": 0.5897435897435898,\n \"acc_norm_stderr\": 0.02493931390694079\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114986,\n \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114986\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7779816513761468,\n \"acc_stderr\": 0.017818849564796648,\n \"acc_norm\": 0.7779816513761468,\n \"acc_norm_stderr\": 0.017818849564796648\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\": 0.7205882352941176,\n \"acc_stderr\": 0.031493281045079556,\n \"acc_norm\": 0.7205882352941176,\n \"acc_norm_stderr\": 0.031493281045079556\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955924,\n \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955924\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5964125560538116,\n \"acc_stderr\": 0.03292802819330314,\n \"acc_norm\": 0.5964125560538116,\n \"acc_norm_stderr\": 0.03292802819330314\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\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\": 0.6574074074074074,\n \"acc_stderr\": 0.045879047413018105,\n \"acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.045879047413018105\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\": 0.3482142857142857,\n \"acc_stderr\": 0.04521829902833587,\n \"acc_norm\": 0.3482142857142857,\n \"acc_norm_stderr\": 0.04521829902833587\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\": 0.782051282051282,\n \"acc_stderr\": 0.027046857630716667,\n \"acc_norm\": 0.782051282051282,\n \"acc_norm_stderr\": 0.027046857630716667\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7177522349936143,\n \"acc_stderr\": 0.016095302969878537,\n \"acc_norm\": 0.7177522349936143,\n \"acc_norm_stderr\": 0.016095302969878537\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.025906632631016117,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.025906632631016117\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27262569832402234,\n \"acc_stderr\": 0.014893391735249624,\n \"acc_norm\": 0.27262569832402234,\n \"acc_norm_stderr\": 0.014893391735249624\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5980392156862745,\n \"acc_stderr\": 0.02807415894760065,\n \"acc_norm\": 0.5980392156862745,\n \"acc_norm_stderr\": 0.02807415894760065\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6655948553054662,\n \"acc_stderr\": 0.026795422327893934,\n \"acc_norm\": 0.6655948553054662,\n \"acc_norm_stderr\": 0.026795422327893934\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5864197530864198,\n \"acc_stderr\": 0.027402042040269966,\n \"acc_norm\": 0.5864197530864198,\n \"acc_norm_stderr\": 0.027402042040269966\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41395045632333766,\n \"acc_stderr\": 0.012579699631289265,\n \"acc_norm\": 0.41395045632333766,\n \"acc_norm_stderr\": 0.012579699631289265\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5661764705882353,\n \"acc_stderr\": 0.030105636570016636,\n \"acc_norm\": 0.5661764705882353,\n \"acc_norm_stderr\": 0.030105636570016636\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5375816993464052,\n \"acc_stderr\": 0.020170614974969768,\n \"acc_norm\": 0.5375816993464052,\n \"acc_norm_stderr\": 0.020170614974969768\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6285714285714286,\n \"acc_stderr\": 0.03093285879278985,\n \"acc_norm\": 0.6285714285714286,\n \"acc_norm_stderr\": 0.03093285879278985\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7611940298507462,\n \"acc_stderr\": 0.030147775935409217,\n \"acc_norm\": 0.7611940298507462,\n \"acc_norm_stderr\": 0.030147775935409217\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 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#region-us
|
# Dataset Card for Evaluation run of beomi/Yi-Ko-6B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model beomi/Yi-Ko-6B 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 2023-12-04T19:08:16.844680(note that their might be results for other tasks in 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 beomi/Yi-Ko-6B",
"## 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 beomi/Yi-Ko-6B 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 2023-12-04T19:08:16.844680(note that their might be results for other tasks in 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 beomi/Yi-Ko-6B",
"## 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 beomi/Yi-Ko-6B 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:",
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] |
412dcf914d90535fa6c07198eb76c5b1bc7dc026 | # Dataset Card for "twitter-year-splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | KaiNylund/twitter-year-splits | [
"license:cc0-1.0",
"region:us"
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More Information needed | [
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0dc6090bb7c094496fca17f3e0be013b19b0b8bc | # Dataset Card for "arxiv-year-splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | KaiNylund/arxiv-year-splits | [
"region:us"
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#region-us
| # Dataset Card for "arxiv-year-splits"
More Information needed | [
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6752e503bb5e56e4c05a17e02327caf3d67857ca |
# Dataset Card for Evaluation run of rishiraj/smol-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rishiraj/smol-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 [rishiraj/smol-7b](https://huggingface.co/rishiraj/smol-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_rishiraj__smol-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:19:39.463418](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__smol-7b/blob/main/results_2023-12-04T19-19-39.463418.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
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},
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},
"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": 0.30599755201958384,
"mc1_stderr": 0.016132229728155045,
"mc2": 0.4617167162027618,
"mc2_stderr": 0.015041171351243195
},
"harness|winogrande|5": {
"acc": 0.8066298342541437,
"acc_stderr": 0.011099796645920524
},
"harness|gsm8k|5": {
"acc": 0.623199393479909,
"acc_stderr": 0.013347858757829158
}
}
```
### 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_rishiraj__smol-7b | [
"region:us"
] | 2023-12-04T19:22:31+00:00 | {"pretty_name": "Evaluation run of rishiraj/smol-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [rishiraj/smol-7b](https://huggingface.co/rishiraj/smol-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_rishiraj__smol-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:19:39.463418](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__smol-7b/blob/main/results_2023-12-04T19-19-39.463418.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\": 0.6514323841472758,\n \"acc_stderr\": 0.03191453823895794,\n \"acc_norm\": 0.6531744958038254,\n \"acc_norm_stderr\": 0.032557792933231744,\n \"mc1\": 0.30599755201958384,\n \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.4617167162027618,\n \"mc2_stderr\": 0.015041171351243195\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5981228668941979,\n \"acc_stderr\": 0.014327268614578276,\n \"acc_norm\": 0.6373720136518771,\n \"acc_norm_stderr\": 0.014049106564955009\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.657239593706433,\n \"acc_stderr\": 0.004736621698861175,\n \"acc_norm\": 0.8477394941246763,\n \"acc_norm_stderr\": 0.0035853896364723818\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\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\": 0.61,\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\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\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\": 0.47,\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\": 0.38,\n \"acc_stderr\": 0.04878317312145634,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145634\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\": 0.04878608714466996,\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\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\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\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\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\": 0.5079365079365079,\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\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188723,\n \"acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188723\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\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\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\": 0.797979797979798,\n \"acc_stderr\": 0.028606204289229865,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229865\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328974,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328974\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\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113114,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113114\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126255,\n \"acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126255\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\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\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503234,\n \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503234\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.726457399103139,\n \"acc_stderr\": 0.029918586707798827,\n \"acc_norm\": 0.726457399103139,\n \"acc_norm_stderr\": 0.029918586707798827\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\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\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\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n \"acc_norm_stderr\": 0.046840993210771065\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\": 0.8803418803418803,\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\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545546,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545546\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42569832402234636,\n \"acc_stderr\": 0.01653682964899712,\n \"acc_norm\": 0.42569832402234636,\n \"acc_norm_stderr\": 0.01653682964899712\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\": 0.729903536977492,\n \"acc_stderr\": 0.02521804037341063,\n \"acc_norm\": 0.729903536977492,\n \"acc_norm_stderr\": 0.02521804037341063\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.02465968518596728,\n \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.02465968518596728\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\": 0.4726205997392438,\n \"acc_stderr\": 0.012751075788015064,\n \"acc_norm\": 0.4726205997392438,\n \"acc_norm_stderr\": 0.012751075788015064\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\": 0.6781045751633987,\n \"acc_stderr\": 0.01890101532209309,\n \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.01890101532209309\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\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\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\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30599755201958384,\n \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.4617167162027618,\n \"mc2_stderr\": 0.015041171351243195\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8066298342541437,\n \"acc_stderr\": 0.011099796645920524\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.623199393479909,\n \"acc_stderr\": 0.013347858757829158\n }\n}\n```", "repo_url": "https://huggingface.co/rishiraj/smol-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_04T19_19_39.463418", "path": ["**/details_harness|arc:challenge|25_2023-12-04T19-19-39.463418.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T19-19-39.463418.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T19_19_39.463418", "path": ["**/details_harness|gsm8k|5_2023-12-04T19-19-39.463418.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T19-19-39.463418.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T19_19_39.463418", "path": ["**/details_harness|hellaswag|10_2023-12-04T19-19-39.463418.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T19-19-39.463418.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T19_19_39.463418", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T19-19-39.463418.parquet", 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#region-us
|
# Dataset Card for Evaluation run of rishiraj/smol-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 rishiraj/smol-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 2023-12-04T19:19:39.463418(note that their might be results for other tasks in 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 rishiraj/smol-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 rishiraj/smol-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 2023-12-04T19:19:39.463418(note that their might be results for other tasks in 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 rishiraj/smol-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 rishiraj/smol-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 2023-12-04T19:19:39.463418(note that their might be results for other tasks in 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"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of rishiraj/smol-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 rishiraj/smol-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 2023-12-04T19:19:39.463418(note that their might be results for other tasks in 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"
] |
c24faf0be3c16f9d019c4e03bcbadc36b6b15521 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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] | ItsRedux/CyberSecDat | [
"region:us"
] | 2023-12-04T19:32:46+00:00 | {} | 2023-12-04T19:35:07+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## 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]
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d0e07b18f8a736ba9c866c190768462bb9048eea |
# Dataset Card for Evaluation run of meta-math/MetaMath-Mistral-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-math/MetaMath-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 [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-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 1 run(s). Each run can be found as a specific 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-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T19:35:59.251082](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-math__MetaMath-Mistral-7B/blob/main/results_2023-12-04T19-35-59.251082.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
{
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"acc_norm": 0.6227799225969178,
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"mc1": 0.3047735618115055,
"mc1_stderr": 0.016114124156882455,
"mc2": 0.4489052122445318,
"mc2_stderr": 0.01547532303838066
},
"harness|arc:challenge|25": {
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"acc_norm": 0.606655290102389,
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},
"harness|hellaswag|10": {
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"acc_norm": 0.8258315076677952,
"acc_norm_stderr": 0.0037847921724660652
},
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},
"harness|hendrycksTest-anatomy|5": {
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},
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"acc_stderr": 0.039397364351956274,
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},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.4491525423728814,
"acc_norm_stderr": 0.012704030518851488
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6213235294117647,
"acc_stderr": 0.02946513363977613,
"acc_norm": 0.6213235294117647,
"acc_norm_stderr": 0.02946513363977613
},
"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": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6857142857142857,
"acc_stderr": 0.029719329422417475,
"acc_norm": 0.6857142857142857,
"acc_norm_stderr": 0.029719329422417475
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454132,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454132
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774711,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774711
},
"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": 0.8128654970760234,
"acc_stderr": 0.029913127232368043,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.029913127232368043
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3047735618115055,
"mc1_stderr": 0.016114124156882455,
"mc2": 0.4489052122445318,
"mc2_stderr": 0.01547532303838066
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174787
},
"harness|gsm8k|5": {
"acc": 0.6884003032600455,
"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_meta-math__MetaMath-Mistral-7B | [
"region:us"
] | 2023-12-04T19:38:52+00:00 | {"pretty_name": "Evaluation run of meta-math/MetaMath-Mistral-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-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 1 run(s). Each run can be found as a specific 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-Mistral-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T19:35:59.251082](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-math__MetaMath-Mistral-7B/blob/main/results_2023-12-04T19-35-59.251082.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\": 0.6224817411296446,\n \"acc_stderr\": 0.03262551509185562,\n \"acc_norm\": 0.6227799225969178,\n \"acc_norm_stderr\": 0.033291016555049055,\n \"mc1\": 0.3047735618115055,\n \"mc1_stderr\": 0.016114124156882455,\n \"mc2\": 0.4489052122445318,\n \"mc2_stderr\": 0.01547532303838066\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5699658703071673,\n \"acc_stderr\": 0.01446763155913799,\n \"acc_norm\": 0.606655290102389,\n \"acc_norm_stderr\": 0.014275101465693024\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6437960565624378,\n \"acc_stderr\": 0.004778978031389641,\n \"acc_norm\": 0.8258315076677952,\n \"acc_norm_stderr\": 0.0037847921724660652\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\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\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n \"acc_norm_stderr\": 0.038009680605548594\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\": 0.54,\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\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383887,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383887\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\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\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\": 0.5379310344827586,\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\": 0.40476190476190477,\n \"acc_stderr\": 0.0252798503974049,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.0252798503974049\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|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-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\": 0.03514528562175007,\n \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175007\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932022,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932022\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\": 0.6076923076923076,\n \"acc_stderr\": 0.024756000382130956,\n \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.024756000382130956\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.029723278961476664,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.029723278961476664\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\": 0.3509933774834437,\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\": 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154343,\n \"acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154343\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\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588667,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588667\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7721518987341772,\n \"acc_stderr\": 0.02730348459906943,\n \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.02730348459906943\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n \"acc_norm_stderr\": 0.031708824268455\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\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\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\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\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\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.020930193185179333\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\": 0.7943805874840357,\n \"acc_stderr\": 0.01445250045678583,\n \"acc_norm\": 0.7943805874840357,\n \"acc_norm_stderr\": 0.01445250045678583\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7023121387283237,\n \"acc_stderr\": 0.024617055388677,\n \"acc_norm\": 0.7023121387283237,\n \"acc_norm_stderr\": 0.024617055388677\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.36089385474860336,\n \"acc_stderr\": 0.01606229067111046,\n \"acc_norm\": 0.36089385474860336,\n \"acc_norm_stderr\": 0.01606229067111046\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824775,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824775\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.6881028938906752,\n \"acc_norm_stderr\": 0.02631185807185416\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\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4491525423728814,\n \"acc_stderr\": 0.012704030518851488,\n \"acc_norm\": 0.4491525423728814,\n \"acc_norm_stderr\": 0.012704030518851488\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6486928104575164,\n \"acc_stderr\": 0.01931267606578655,\n \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.01931267606578655\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\": 0.6857142857142857,\n \"acc_stderr\": 0.029719329422417475,\n \"acc_norm\": 0.6857142857142857,\n \"acc_norm_stderr\": 0.029719329422417475\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454132,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454132\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774711,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774711\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\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368043,\n \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368043\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3047735618115055,\n \"mc1_stderr\": 0.016114124156882455,\n \"mc2\": 0.4489052122445318,\n \"mc2_stderr\": 0.01547532303838066\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174787\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6884003032600455,\n \"acc_stderr\": 0.012757375376754941\n }\n}\n```", "repo_url": "https://huggingface.co/meta-math/MetaMath-Mistral-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", 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#region-us
|
# Dataset Card for Evaluation run of meta-math/MetaMath-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 meta-math/MetaMath-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 1 run(s). Each run can be found as a specific 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 2023-12-04T19:35:59.251082(note that their might be results for other tasks in 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-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 meta-math/MetaMath-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 1 run(s). Each run can be found as a specific 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 2023-12-04T19:35:59.251082(note that their might be results for other tasks in 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-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 meta-math/MetaMath-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 1 run(s). Each run can be found as a specific 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 2023-12-04T19:35:59.251082(note that their might be results for other tasks in 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"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of meta-math/MetaMath-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 meta-math/MetaMath-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 1 run(s). Each run can be found as a specific 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 2023-12-04T19:35:59.251082(note that their might be results for other tasks in 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"
] |
2b971d9ea2f1075c245cbab045dc44a1e1570bf2 | # Dataset Card for "fashion-mnist-interview"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | daloopa/fashion-mnist-interview | [
"region:us"
] | 2023-12-04T19:52:02+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "T - shirt / top", "1": "Trouser", "2": "Pullover", "3": "Dress", "4": "Coat", "5": "Sandal", "6": "Shirt", "7": "Sneaker", "8": "Bag", "9": "Ankle boot"}}}}], "splits": [{"name": "train", "num_bytes": 31049107.0, "num_examples": 60000}, {"name": "test", "num_bytes": 4150316.0, "num_examples": 8000}], "download_size": 33099036, "dataset_size": 35199423.0}} | 2023-12-04T19:52:08+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "fashion-mnist-interview"
More Information needed | [
"# Dataset Card for \"fashion-mnist-interview\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"fashion-mnist-interview\"\n\nMore Information needed"
] | [
6,
17
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"fashion-mnist-interview\"\n\nMore Information needed"
] |
086d77b439d29d4eb817065b5b1d3d2ef2086db9 | # Dataset Card for "fashion-mnist-interview-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | daloopa/fashion-mnist-interview-test | [
"region:us"
] | 2023-12-04T19:53:27+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 1026244.0, "num_examples": 2000}], "download_size": 982702, "dataset_size": 1026244.0}} | 2023-12-04T21:53:36+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "fashion-mnist-interview-test"
More Information needed | [
"# Dataset Card for \"fashion-mnist-interview-test\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"fashion-mnist-interview-test\"\n\nMore Information needed"
] | [
6,
19
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"fashion-mnist-interview-test\"\n\nMore Information needed"
] |
7788c0e0ee38bdd2ab211109da8a9c3117614866 |
# Dataset Card for Evaluation run of tlphams/zoyllm-7b-slimorca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tlphams/zoyllm-7b-slimorca
- **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 [tlphams/zoyllm-7b-slimorca](https://huggingface.co/tlphams/zoyllm-7b-slimorca) 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_tlphams__zoyllm-7b-slimorca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T20:19:06.813924](https://huggingface.co/datasets/open-llm-leaderboard/details_tlphams__zoyllm-7b-slimorca/blob/main/results_2023-12-04T20-19-06.813924.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": 0.4870010988384523,
"acc_stderr": 0.03455949361884823,
"acc_norm": 0.4920391497879656,
"acc_norm_stderr": 0.03531050289249056,
"mc1": 0.32313341493268055,
"mc1_stderr": 0.016371836286454604,
"mc2": 0.4913166366572656,
"mc2_stderr": 0.0160517163595852
},
"harness|arc:challenge|25": {
"acc": 0.4726962457337884,
"acc_stderr": 0.014589589101985993,
"acc_norm": 0.5059726962457338,
"acc_norm_stderr": 0.014610348300255795
},
"harness|hellaswag|10": {
"acc": 0.5509858593905597,
"acc_stderr": 0.004963771168672079,
"acc_norm": 0.7211710814578769,
"acc_norm_stderr": 0.004475067344626756
},
"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": 0.4444444444444444,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.45394736842105265,
"acc_stderr": 0.04051646342874142,
"acc_norm": 0.45394736842105265,
"acc_norm_stderr": 0.04051646342874142
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5358490566037736,
"acc_stderr": 0.030693675018458,
"acc_norm": 0.5358490566037736,
"acc_norm_stderr": 0.030693675018458
},
"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": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"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": 0.4393063583815029,
"acc_stderr": 0.037842719328874674,
"acc_norm": 0.4393063583815029,
"acc_norm_stderr": 0.037842719328874674
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929776
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.61,
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},
"harness|hendrycksTest-conceptual_physics|5": {
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},
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},
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"acc_norm_stderr": 0.04158632762097828
},
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},
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},
"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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},
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
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},
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm_stderr": 0.02696242432507382
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.46638655462184875,
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"acc_norm": 0.46638655462184875,
"acc_norm_stderr": 0.03240501447690071
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
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},
"harness|hendrycksTest-high_school_psychology|5": {
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},
"harness|hendrycksTest-high_school_statistics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.6374269005847953,
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},
"harness|truthfulqa:mc|0": {
"mc1": 0.32313341493268055,
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"mc2": 0.4913166366572656,
"mc2_stderr": 0.0160517163595852
},
"harness|winogrande|5": {
"acc": 0.6732438831886346,
"acc_stderr": 0.013181997302131362
},
"harness|gsm8k|5": {
"acc": 0.20697498104624715,
"acc_stderr": 0.011159498164891766
}
}
```
### 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_tlphams__zoyllm-7b-slimorca | [
"region:us"
] | 2023-12-04T20:21:57+00:00 | {"pretty_name": "Evaluation run of tlphams/zoyllm-7b-slimorca", "dataset_summary": "Dataset automatically created during the evaluation run of model [tlphams/zoyllm-7b-slimorca](https://huggingface.co/tlphams/zoyllm-7b-slimorca) 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_tlphams__zoyllm-7b-slimorca\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T20:19:06.813924](https://huggingface.co/datasets/open-llm-leaderboard/details_tlphams__zoyllm-7b-slimorca/blob/main/results_2023-12-04T20-19-06.813924.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\": 0.4870010988384523,\n \"acc_stderr\": 0.03455949361884823,\n \"acc_norm\": 0.4920391497879656,\n \"acc_norm_stderr\": 0.03531050289249056,\n \"mc1\": 0.32313341493268055,\n \"mc1_stderr\": 0.016371836286454604,\n \"mc2\": 0.4913166366572656,\n \"mc2_stderr\": 0.0160517163595852\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4726962457337884,\n \"acc_stderr\": 0.014589589101985993,\n \"acc_norm\": 0.5059726962457338,\n \"acc_norm_stderr\": 0.014610348300255795\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5509858593905597,\n \"acc_stderr\": 0.004963771168672079,\n \"acc_norm\": 0.7211710814578769,\n \"acc_norm_stderr\": 0.004475067344626756\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\": 0.4444444444444444,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.45394736842105265,\n \"acc_stderr\": 0.04051646342874142,\n \"acc_norm\": 0.45394736842105265,\n \"acc_norm_stderr\": 0.04051646342874142\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.5358490566037736,\n \"acc_stderr\": 0.030693675018458,\n \"acc_norm\": 0.5358490566037736,\n \"acc_norm_stderr\": 0.030693675018458\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\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_computer_science|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_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\": 0.4393063583815029,\n \"acc_stderr\": 0.037842719328874674,\n \"acc_norm\": 0.4393063583815029,\n \"acc_norm_stderr\": 0.037842719328874674\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929776,\n \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929776\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001974\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\": 0.2543859649122807,\n \"acc_stderr\": 0.04096985139843672,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.04096985139843672\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159788,\n \"acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159788\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n \"acc_stderr\": 0.04240799327574924,\n \"acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.04240799327574924\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.5741935483870968,\n \"acc_stderr\": 0.028129112709165904,\n \"acc_norm\": 0.5741935483870968,\n \"acc_norm_stderr\": 0.028129112709165904\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.33004926108374383,\n \"acc_stderr\": 0.03308530426228257,\n \"acc_norm\": 0.33004926108374383,\n \"acc_norm_stderr\": 0.03308530426228257\n },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.037131580674819135,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.037131580674819135\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.6414141414141414,\n \"acc_stderr\": 0.034169036403915214,\n \"acc_norm\": 0.6414141414141414,\n \"acc_norm_stderr\": 0.034169036403915214\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6528497409326425,\n \"acc_stderr\": 0.03435696168361356,\n \"acc_norm\": 0.6528497409326425,\n \"acc_norm_stderr\": 0.03435696168361356\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4230769230769231,\n \"acc_stderr\": 0.025049197876042338,\n \"acc_norm\": 0.4230769230769231,\n \"acc_norm_stderr\": 0.025049197876042338\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507382,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507382\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.46638655462184875,\n \"acc_stderr\": 0.03240501447690071,\n \"acc_norm\": 0.46638655462184875,\n \"acc_norm_stderr\": 0.03240501447690071\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\": 0.671559633027523,\n \"acc_stderr\": 0.02013590279729841,\n \"acc_norm\": 0.671559633027523,\n \"acc_norm_stderr\": 0.02013590279729841\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.35648148148148145,\n \"acc_stderr\": 0.03266478331527272,\n \"acc_norm\": 0.35648148148148145,\n \"acc_norm_stderr\": 0.03266478331527272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6225490196078431,\n \"acc_stderr\": 0.03402272044340703,\n \"acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.03402272044340703\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.679324894514768,\n \"acc_stderr\": 0.030381931949990407,\n \"acc_norm\": 0.679324894514768,\n \"acc_norm_stderr\": 0.030381931949990407\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.547085201793722,\n \"acc_stderr\": 0.03340867501923324,\n \"acc_norm\": 0.547085201793722,\n \"acc_norm_stderr\": 0.03340867501923324\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.48091603053435117,\n \"acc_stderr\": 0.04382094705550988,\n \"acc_norm\": 0.48091603053435117,\n \"acc_norm_stderr\": 0.04382094705550988\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.5867768595041323,\n \"acc_stderr\": 0.04495087843548408,\n \"acc_norm\": 0.5867768595041323,\n \"acc_norm_stderr\": 0.04495087843548408\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.04750077341199984,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.04750077341199984\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.5766871165644172,\n \"acc_stderr\": 0.03881891213334384,\n \"acc_norm\": 0.5766871165644172,\n \"acc_norm_stderr\": 0.03881891213334384\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\": 0.6893203883495146,\n \"acc_stderr\": 0.04582124160161551,\n \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.04582124160161551\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.02934311479809447,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.02934311479809447\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.648786717752235,\n \"acc_stderr\": 0.017069982051499434,\n \"acc_norm\": 0.648786717752235,\n \"acc_norm_stderr\": 0.017069982051499434\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5375722543352601,\n \"acc_stderr\": 0.02684298551961537,\n \"acc_norm\": 0.5375722543352601,\n \"acc_norm_stderr\": 0.02684298551961537\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27150837988826815,\n \"acc_stderr\": 0.014874252168095266,\n \"acc_norm\": 0.27150837988826815,\n \"acc_norm_stderr\": 0.014874252168095266\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4869281045751634,\n \"acc_stderr\": 0.028620130800700246,\n \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.028620130800700246\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5337620578778135,\n \"acc_stderr\": 0.028333277109562793,\n \"acc_norm\": 0.5337620578778135,\n \"acc_norm_stderr\": 0.028333277109562793\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5339506172839507,\n \"acc_stderr\": 0.027756535257347666,\n \"acc_norm\": 0.5339506172839507,\n \"acc_norm_stderr\": 0.027756535257347666\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4148936170212766,\n \"acc_stderr\": 0.029392236584612503,\n \"acc_norm\": 0.4148936170212766,\n \"acc_norm_stderr\": 0.029392236584612503\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.35984354628422427,\n \"acc_stderr\": 0.0122582604836898,\n \"acc_norm\": 0.35984354628422427,\n \"acc_norm_stderr\": 0.0122582604836898\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4338235294117647,\n \"acc_stderr\": 0.030105636570016633,\n \"acc_norm\": 0.4338235294117647,\n \"acc_norm_stderr\": 0.030105636570016633\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.45588235294117646,\n \"acc_stderr\": 0.020148939420415738,\n \"acc_norm\": 0.45588235294117646,\n \"acc_norm_stderr\": 0.020148939420415738\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5181818181818182,\n \"acc_stderr\": 0.04785964010794916,\n \"acc_norm\": 0.5181818181818182,\n \"acc_norm_stderr\": 0.04785964010794916\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03136250240935893,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03136250240935893\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6766169154228856,\n \"acc_stderr\": 0.03307615947979035,\n \"acc_norm\": 0.6766169154228856,\n \"acc_norm_stderr\": 0.03307615947979035\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.39156626506024095,\n \"acc_stderr\": 0.03799857454479636,\n \"acc_norm\": 0.39156626506024095,\n \"acc_norm_stderr\": 0.03799857454479636\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.6374269005847953,\n \"acc_stderr\": 0.036871306155620606,\n \"acc_norm\": 0.6374269005847953,\n \"acc_norm_stderr\": 0.036871306155620606\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32313341493268055,\n \"mc1_stderr\": 0.016371836286454604,\n \"mc2\": 0.4913166366572656,\n \"mc2_stderr\": 0.0160517163595852\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6732438831886346,\n \"acc_stderr\": 0.013181997302131362\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.20697498104624715,\n \"acc_stderr\": 0.011159498164891766\n }\n}\n```", "repo_url": "https://huggingface.co/tlphams/zoyllm-7b-slimorca", "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_04T20_19_06.813924", "path": ["**/details_harness|arc:challenge|25_2023-12-04T20-19-06.813924.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-04T20-19-06.813924.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_04T20_19_06.813924", "path": ["**/details_harness|gsm8k|5_2023-12-04T20-19-06.813924.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T20-19-06.813924.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_04T20_19_06.813924", "path": ["**/details_harness|hellaswag|10_2023-12-04T20-19-06.813924.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-04T20-19-06.813924.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_04T20_19_06.813924", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T20-19-06.813924.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T20-19-06.813924.parquet", 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#region-us
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# Dataset Card for Evaluation run of tlphams/zoyllm-7b-slimorca
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model tlphams/zoyllm-7b-slimorca 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 2023-12-04T20:19:06.813924(note that their might be results for other tasks in 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
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90bba42b973268514b701001346c230504232ee3 | # Dataset Card for "thestack_python_threading"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | celinelee/thestack_python_threading | [
"region:us"
] | 2023-12-04T20:29:38+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "python", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3869477993.4417205, "num_examples": 168964}, {"name": "valid", "num_bytes": 483696199.7791398, "num_examples": 21121}, {"name": "test", "num_bytes": 483696199.7791398, "num_examples": 21121}], "download_size": 1685764929, "dataset_size": 4836870393.0}} | 2023-12-04T20:44:03+00:00 | [] | [] | TAGS
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More Information needed | [
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14809f94ff8891c009f7a03c71f402f589c36804 |
# Dataset Card for Evaluation run of jebcarter/psyonic-cetacean-20B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jebcarter/psyonic-cetacean-20B
- **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 [jebcarter/psyonic-cetacean-20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B) 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_jebcarter__psyonic-cetacean-20B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T20:41:51.584700](https://huggingface.co/datasets/open-llm-leaderboard/details_jebcarter__psyonic-cetacean-20B/blob/main/results_2023-12-04T20-41-51.584700.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": 0.5935200283760108,
"acc_stderr": 0.03289023551450696,
"acc_norm": 0.6017961208576313,
"acc_norm_stderr": 0.03361696714318325,
"mc1": 0.397796817625459,
"mc1_stderr": 0.01713393424855964,
"mc2": 0.5754737295645932,
"mc2_stderr": 0.01561942525764945
},
"harness|arc:challenge|25": {
"acc": 0.5895904436860068,
"acc_stderr": 0.014374922192642664,
"acc_norm": 0.6356655290102389,
"acc_norm_stderr": 0.014063260279882419
},
"harness|hellaswag|10": {
"acc": 0.6783509261103365,
"acc_stderr": 0.0046615449915830345,
"acc_norm": 0.861979685321649,
"acc_norm_stderr": 0.0034421638433628794
},
"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": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"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": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6528301886792452,
"acc_stderr": 0.029300101705549655,
"acc_norm": 0.6528301886792452,
"acc_norm_stderr": 0.029300101705549655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6736111111111112,
"acc_stderr": 0.03921067198982266,
"acc_norm": 0.6736111111111112,
"acc_norm_stderr": 0.03921067198982266
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"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": 0.3431372549019608,
"acc_stderr": 0.04724007352383888,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.04724007352383888
},
"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": 0.5191489361702127,
"acc_stderr": 0.032662042990646796,
"acc_norm": 0.5191489361702127,
"acc_norm_stderr": 0.032662042990646796
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.34210526315789475,
"acc_stderr": 0.04462917535336937,
"acc_norm": 0.34210526315789475,
"acc_norm_stderr": 0.04462917535336937
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878151,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878151
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36507936507936506,
"acc_stderr": 0.024796060602699968,
"acc_norm": 0.36507936507936506,
"acc_norm_stderr": 0.024796060602699968
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3492063492063492,
"acc_stderr": 0.04263906892795133,
"acc_norm": 0.3492063492063492,
"acc_norm_stderr": 0.04263906892795133
},
"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": 0.7064516129032258,
"acc_stderr": 0.025906087021319295,
"acc_norm": 0.7064516129032258,
"acc_norm_stderr": 0.025906087021319295
},
"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": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.03453131801885415,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.03453131801885415
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.03191178226713547,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.03191178226713547
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8860103626943006,
"acc_stderr": 0.022935144053919436,
"acc_norm": 0.8860103626943006,
"acc_norm_stderr": 0.022935144053919436
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_stderr": 0.024503472557110946,
"acc_norm": 0.6282051282051282,
"acc_norm_stderr": 0.024503472557110946
},
"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": 0.6512605042016807,
"acc_stderr": 0.030956636328566545,
"acc_norm": 0.6512605042016807,
"acc_norm_stderr": 0.030956636328566545
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526732,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526732
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7981651376146789,
"acc_stderr": 0.017208579357787572,
"acc_norm": 0.7981651376146789,
"acc_norm_stderr": 0.017208579357787572
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4861111111111111,
"acc_stderr": 0.03408655867977748,
"acc_norm": 0.4861111111111111,
"acc_norm_stderr": 0.03408655867977748
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7647058823529411,
"acc_stderr": 0.029771775228145628,
"acc_norm": 0.7647058823529411,
"acc_norm_stderr": 0.029771775228145628
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.025955020841621112,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.025955020841621112
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6188340807174888,
"acc_stderr": 0.032596251184168284,
"acc_norm": 0.6188340807174888,
"acc_norm_stderr": 0.032596251184168284
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.732824427480916,
"acc_stderr": 0.03880848301082396,
"acc_norm": 0.732824427480916,
"acc_norm_stderr": 0.03880848301082396
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070417
},
"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": 0.6993865030674846,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04547960999764376,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04547960999764376
},
"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": 0.8632478632478633,
"acc_stderr": 0.022509033937077812,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.022509033937077812
},
"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": 0.7879948914431673,
"acc_stderr": 0.014616099385833688,
"acc_norm": 0.7879948914431673,
"acc_norm_stderr": 0.014616099385833688
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.02494679222527231,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.02494679222527231
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.22346368715083798,
"acc_stderr": 0.013932068638579773,
"acc_norm": 0.22346368715083798,
"acc_norm_stderr": 0.013932068638579773
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.02736359328468497,
"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.02736359328468497
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
"acc_stderr": 0.026082700695399662,
"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.026082700695399662
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.02540719779889017,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.02540719779889017
},
"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": 0.45697522816166886,
"acc_stderr": 0.012722869501611419,
"acc_norm": 0.45697522816166886,
"acc_norm_stderr": 0.012722869501611419
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6066176470588235,
"acc_stderr": 0.029674288281311155,
"acc_norm": 0.6066176470588235,
"acc_norm_stderr": 0.029674288281311155
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6356209150326797,
"acc_stderr": 0.019469518221573702,
"acc_norm": 0.6356209150326797,
"acc_norm_stderr": 0.019469518221573702
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6272727272727273,
"acc_stderr": 0.04631381319425465,
"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.04631381319425465
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6857142857142857,
"acc_stderr": 0.029719329422417475,
"acc_norm": 0.6857142857142857,
"acc_norm_stderr": 0.029719329422417475
},
"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": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4819277108433735,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7719298245614035,
"acc_stderr": 0.032180937956023566,
"acc_norm": 0.7719298245614035,
"acc_norm_stderr": 0.032180937956023566
},
"harness|truthfulqa:mc|0": {
"mc1": 0.397796817625459,
"mc1_stderr": 0.01713393424855964,
"mc2": 0.5754737295645932,
"mc2_stderr": 0.01561942525764945
},
"harness|winogrande|5": {
"acc": 0.7813733228097869,
"acc_stderr": 0.01161619821577323
},
"harness|gsm8k|5": {
"acc": 0.1470811220621683,
"acc_stderr": 0.009756063660359868
}
}
```
### 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_jebcarter__psyonic-cetacean-20B | [
"region:us"
] | 2023-12-04T20:44:46+00:00 | {"pretty_name": "Evaluation run of jebcarter/psyonic-cetacean-20B", "dataset_summary": "Dataset automatically created during the evaluation run of model [jebcarter/psyonic-cetacean-20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B) 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_jebcarter__psyonic-cetacean-20B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T20:41:51.584700](https://huggingface.co/datasets/open-llm-leaderboard/details_jebcarter__psyonic-cetacean-20B/blob/main/results_2023-12-04T20-41-51.584700.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\": 0.5935200283760108,\n \"acc_stderr\": 0.03289023551450696,\n \"acc_norm\": 0.6017961208576313,\n \"acc_norm_stderr\": 0.03361696714318325,\n \"mc1\": 0.397796817625459,\n \"mc1_stderr\": 0.01713393424855964,\n \"mc2\": 0.5754737295645932,\n \"mc2_stderr\": 0.01561942525764945\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5895904436860068,\n \"acc_stderr\": 0.014374922192642664,\n \"acc_norm\": 0.6356655290102389,\n \"acc_norm_stderr\": 0.014063260279882419\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6783509261103365,\n \"acc_stderr\": 0.0046615449915830345,\n \"acc_norm\": 0.861979685321649,\n \"acc_norm_stderr\": 0.0034421638433628794\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\": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["**/details_harness|winogrande|5_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T20-41-51.584700.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T20_41_51.584700", "path": ["results_2023-12-04T20-41-51.584700.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T20-41-51.584700.parquet"]}]}]} | 2023-12-04T20:45:31+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of jebcarter/psyonic-cetacean-20B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model jebcarter/psyonic-cetacean-20B 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 2023-12-04T20:41:51.584700(note that their might be results for other tasks in 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 jebcarter/psyonic-cetacean-20B",
"## 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 jebcarter/psyonic-cetacean-20B 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 2023-12-04T20:41:51.584700(note that their might be results for other tasks in 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",
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"### Data Fields",
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"### Social Impact of Dataset",
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"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] | [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model jebcarter/psyonic-cetacean-20B 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 2023-12-04T20:41:51.584700(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of jebcarter/psyonic-cetacean-20B## 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 jebcarter/psyonic-cetacean-20B 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 2023-12-04T20:41:51.584700(note that their might be results for other tasks in 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"
] |
e74aff31a09a57375e24fdcfe60f6616620b19ee |
I created these images using a p5.js. I first rendered them in a GIF, uploaded to Google Drive, and then extracted the individual frames
from the GIF.
The light background gears images were generated in p5.js using this [sketch](https://editor.p5js.org/kfahn/sketches/mJ4FOnPy5).
The dark background gears images were generated in p5.js using this [sketch](https://editor.p5js.org/kfahn/sketches/mJ4FOnPy5). | kfahn/3d_gears | [
"license:mit",
"region:us"
] | 2023-12-04T20:54:14+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "dark", "1": "light"}}}}], "splits": [{"name": "train", "num_bytes": 296821064, "num_examples": 4000}], "download_size": 264360785, "dataset_size": 296821064}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-04T21:03:23+00:00 | [] | [] | TAGS
#license-mit #region-us
|
I created these images using a URL. I first rendered them in a GIF, uploaded to Google Drive, and then extracted the individual frames
from the GIF.
The light background gears images were generated in URL using this sketch.
The dark background gears images were generated in URL using this sketch. | [] | [
"TAGS\n#license-mit #region-us \n"
] | [
11
] | [
"passage: TAGS\n#license-mit #region-us \n"
] |
520f49e689bd35047970db0708b3efb1039d2f7b |
# Dataset Card for Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1
- **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 [adamo1139/Yi-34B-AEZAKMI-v1](https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1) 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_adamo1139__Yi-34B-AEZAKMI-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T22:17:18.926595](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1/blob/main/results_2023-12-04T22-17-18.926595.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": 0.733063777345197,
"acc_stderr": 0.02911576095445218,
"acc_norm": 0.7392718490739228,
"acc_norm_stderr": 0.029657906091365063,
"mc1": 0.401468788249694,
"mc1_stderr": 0.01716027390169365,
"mc2": 0.557340774150812,
"mc2_stderr": 0.015053849366752348
},
"harness|arc:challenge|25": {
"acc": 0.606655290102389,
"acc_stderr": 0.014275101465693024,
"acc_norm": 0.643344709897611,
"acc_norm_stderr": 0.01399805690262019
},
"harness|hellaswag|10": {
"acc": 0.6422027484564827,
"acc_stderr": 0.004783723798286501,
"acc_norm": 0.8430591515634336,
"acc_norm_stderr": 0.0036300159898964017
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6888888888888889,
"acc_stderr": 0.03999262876617721,
"acc_norm": 0.6888888888888889,
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"harness|gsm8k|5": {
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}
```
### 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_adamo1139__Yi-34B-AEZAKMI-v1 | [
"region:us"
] | 2023-12-04T22:20:07+00:00 | {"pretty_name": "Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [adamo1139/Yi-34B-AEZAKMI-v1](https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1) 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_adamo1139__Yi-34B-AEZAKMI-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T22:17:18.926595](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1/blob/main/results_2023-12-04T22-17-18.926595.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\": 0.733063777345197,\n \"acc_stderr\": 0.02911576095445218,\n \"acc_norm\": 0.7392718490739228,\n \"acc_norm_stderr\": 0.029657906091365063,\n \"mc1\": 0.401468788249694,\n \"mc1_stderr\": 0.01716027390169365,\n \"mc2\": 0.557340774150812,\n \"mc2_stderr\": 0.015053849366752348\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.606655290102389,\n \"acc_stderr\": 0.014275101465693024,\n \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.01399805690262019\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6422027484564827,\n \"acc_stderr\": 0.004783723798286501,\n \"acc_norm\": 0.8430591515634336,\n \"acc_norm_stderr\": 0.0036300159898964017\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n \"acc_stderr\": 0.03999262876617721,\n \"acc_norm\": 0.6888888888888889,\n \"acc_norm_stderr\": 0.03999262876617721\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549912,\n \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549912\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.7735849056603774,\n \"acc_stderr\": 0.025757559893106737,\n \"acc_norm\": 0.7735849056603774,\n \"acc_norm_stderr\": 0.025757559893106737\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8402777777777778,\n \"acc_stderr\": 0.030635578972093278,\n \"acc_norm\": 0.8402777777777778,\n \"acc_norm_stderr\": 0.030635578972093278\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\": 0.62,\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\": 0.44,\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\": 0.7052023121387283,\n \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.03476599607516478\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\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T22-17-18.926595.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_04T22_17_18.926595", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T22-17-18.926595.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T22-17-18.926595.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_04T22_17_18.926595", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T22-17-18.926595.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-04T22-17-18.926595.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_04T22_17_18.926595", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-04T22-17-18.926595.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-04T22-17-18.926595.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T22-17-18.926595.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T22_17_18.926595", "path": ["**/details_harness|winogrande|5_2023-12-04T22-17-18.926595.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T22-17-18.926595.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T22_17_18.926595", "path": ["results_2023-12-04T22-17-18.926595.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T22-17-18.926595.parquet"]}]}]} | 2023-12-04T22:20:51+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model adamo1139/Yi-34B-AEZAKMI-v1 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 2023-12-04T22:17:18.926595(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model adamo1139/Yi-34B-AEZAKMI-v1 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:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1## 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 adamo1139/Yi-34B-AEZAKMI-v1 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 2023-12-04T22:17:18.926595(note that their might be results for other tasks in 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"
] |
28f5bf3e2fa6637980a200943393cdb81a490d08 |
Another clone of openbmb/UltraFeedback, with all completions by 'bard', 'gpt-3.5-turbo', or 'gpt-4' removed prior to binarization.
The annotations are still written by GPT4, so this dataset is neither OpenAI-free nor commercially-available.
If you're looking for an open-source DPO dataset, you may want to try nvidia/HelpSteer for the time being. | monology/ultrafeedback-liberated | [
"license:apache-2.0",
"region:us"
] | 2023-12-04T22:41:37+00:00 | {"license": "apache-2.0"} | 2023-12-04T23:28:02+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
Another clone of openbmb/UltraFeedback, with all completions by 'bard', 'gpt-3.5-turbo', or 'gpt-4' removed prior to binarization.
The annotations are still written by GPT4, so this dataset is neither OpenAI-free nor commercially-available.
If you're looking for an open-source DPO dataset, you may want to try nvidia/HelpSteer for the time being. | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
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14
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4a6a98dfadc7fb79a5f1ee4cc3db8e3e5a9fec54 | # Dataset Card for "fm-updates-falcon-instruct-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | coastalcph/fm-updates-falcon-instruct-7b | [
"region:us"
] | 2023-12-04T22:48:56+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "query", "struct": [{"name": "label", "dtype": "string"}, {"name": "objects", "list": [{"name": "aliases", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "qid", "dtype": "string"}]}, {"name": "qid", "dtype": "string"}, {"name": "rel_id", "dtype": "string"}, {"name": "relation", "dtype": "string"}]}, {"name": "prediction", "struct": [{"name": "predictions", "list": [{"name": "answer", "dtype": "string"}, {"name": "first_token_probability", "dtype": "float64"}, {"name": "per_token_probability", "sequence": "float64"}, {"name": "perplexity", "dtype": "float64"}]}, {"name": "query", "dtype": "string"}]}, {"name": "f1", "dtype": "float64"}, {"name": "relation", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "original_answer", "dtype": "string"}, {"name": "updates", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 694312.6861702128, "num_examples": 1749}], "download_size": 383499, "dataset_size": 694312.6861702128}} | 2023-12-04T22:49:01+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "fm-updates-falcon-instruct-7b"
More Information needed | [
"# Dataset Card for \"fm-updates-falcon-instruct-7b\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
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] | [
6,
23
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"passage: TAGS\n#region-us \n# Dataset Card for \"fm-updates-falcon-instruct-7b\"\n\nMore Information needed"
] |
f79a7f415e15f851bf0169da9322640d6ae5bc6e |
# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Q
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/kyujinpy/PlatYi-34B-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-Q](https://huggingface.co/kyujinpy/PlatYi-34B-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-Q",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T22:52:59.529862](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Q/blob/main/results_2023-12-04T22-52-59.529862.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": 0.7691265720086405,
"acc_stderr": 0.027746564509879067,
"acc_norm": 0.7760516739566624,
"acc_norm_stderr": 0.028247188531903868,
"mc1": 0.3818849449204406,
"mc1_stderr": 0.017008101939163495,
"mc2": 0.5302838395915566,
"mc2_stderr": 0.014898239428144871
},
"harness|arc:challenge|25": {
"acc": 0.6313993174061433,
"acc_stderr": 0.014097810678042194,
"acc_norm": 0.6689419795221843,
"acc_norm_stderr": 0.013752062419817825
},
"harness|hellaswag|10": {
"acc": 0.654052977494523,
"acc_stderr": 0.004747038768172525,
"acc_norm": 0.8514240191196972,
"acc_norm_stderr": 0.0035494312479073674
},
"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": 0.7333333333333333,
"acc_stderr": 0.038201699145179055,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.038201699145179055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8552631578947368,
"acc_stderr": 0.028631951845930384,
"acc_norm": 0.8552631578947368,
"acc_norm_stderr": 0.028631951845930384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
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}
```
### 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-Q | [
"region:us"
] | 2023-12-04T22:55:49+00:00 | {"pretty_name": "Evaluation run of kyujinpy/PlatYi-34B-Q", "dataset_summary": "Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Q](https://huggingface.co/kyujinpy/PlatYi-34B-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-Q\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T22:52:59.529862](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Q/blob/main/results_2023-12-04T22-52-59.529862.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\": 0.7691265720086405,\n \"acc_stderr\": 0.027746564509879067,\n \"acc_norm\": 0.7760516739566624,\n \"acc_norm_stderr\": 0.028247188531903868,\n \"mc1\": 0.3818849449204406,\n \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5302838395915566,\n \"mc2_stderr\": 0.014898239428144871\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042194,\n \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.013752062419817825\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.654052977494523,\n \"acc_stderr\": 0.004747038768172525,\n \"acc_norm\": 0.8514240191196972,\n \"acc_norm_stderr\": 0.0035494312479073674\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 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["**/details_harness|winogrande|5_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T22-52-59.529862.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_04T22_52_59.529862", "path": ["results_2023-12-04T22-52-59.529862.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T22-52-59.529862.parquet"]}]}]} | 2023-12-04T22:56:37+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-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-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 2023-12-04T22:52:59.529862(note that their might be results for other tasks in 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
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-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-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 2023-12-04T22:52:59.529862(note that their might be results for other tasks in 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"
] |
48259648c202d82469341ae6070f60b77cbfc6b3 |
## This is a TriviaQA wikipedia dataset that was reformated and "answer_start" added
This dataset has context max tokens length of 5000.
I used this dataset for my research, you can find code for reformatting TriviaQA here:
https://github.com/Kkordik/NovelQSI
| Kkordik/TriviaQA_SQuAD | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
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"en"
] | TAGS
#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us
|
## This is a TriviaQA wikipedia dataset that was reformated and "answer_start" added
This dataset has context max tokens length of 5000.
I used this dataset for my research, you can find code for reformatting TriviaQA here:
URL
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] |
aee17b9ecfcb4624d93b4b28e1396873196671d1 | # Dataset Card for "cityscape_3_classes_offset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Arham-Imran/cityscape_3_classes_offset | [
"region:us"
] | 2023-12-04T23:08:37+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 6824060744.525, "num_examples": 2975}, {"name": "val", "num_bytes": 1185871140.0, "num_examples": 500}], "download_size": 3207188951, "dataset_size": 8009931884.525}} | 2023-12-05T09:45:50+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "cityscape_3_classes_offset"
More Information needed | [
"# Dataset Card for \"cityscape_3_classes_offset\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"cityscape_3_classes_offset\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"cityscape_3_classes_offset\"\n\nMore Information needed"
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2ddf3079272f07005d856d3eae4b0aefd36d21fd |
# Dataset Card for Evaluation run of migtissera/Tess-M-v1.3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Tess-M-v1.3
- **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 [migtissera/Tess-M-v1.3](https://huggingface.co/migtissera/Tess-M-v1.3) 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_migtissera__Tess-M-v1.3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T23:32:51.712332](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-v1.3/blob/main/results_2023-12-04T23-32-51.712332.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": 0.747566002523043,
"acc_stderr": 0.028749261755203245,
"acc_norm": 0.7529037953743296,
"acc_norm_stderr": 0.029285728391357593,
"mc1": 0.397796817625459,
"mc1_stderr": 0.017133934248559638,
"mc2": 0.5603469779031626,
"mc2_stderr": 0.015661408014010857
},
"harness|arc:challenge|25": {
"acc": 0.5921501706484642,
"acc_stderr": 0.014361097288449705,
"acc_norm": 0.6254266211604096,
"acc_norm_stderr": 0.014144193471893456
},
"harness|hellaswag|10": {
"acc": 0.6494722166899024,
"acc_stderr": 0.004761601303258892,
"acc_norm": 0.8394742083250348,
"acc_norm_stderr": 0.0036634275361781586
},
"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": 0.7111111111111111,
"acc_stderr": 0.03915450630414251,
"acc_norm": 0.7111111111111111,
"acc_norm_stderr": 0.03915450630414251
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8618421052631579,
"acc_stderr": 0.028081042939576552,
"acc_norm": 0.8618421052631579,
"acc_norm_stderr": 0.028081042939576552
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8377358490566038,
"acc_stderr": 0.022691482872035353,
"acc_norm": 0.8377358490566038,
"acc_norm_stderr": 0.022691482872035353
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.026280550932848062,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.026280550932848062
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"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": 0.48,
"acc_stderr": 0.05021167315686779,
"acc_norm": 0.48,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7109826589595376,
"acc_stderr": 0.03456425745086999,
"acc_norm": 0.7109826589595376,
"acc_norm_stderr": 0.03456425745086999
},
"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": 0.82,
"acc_stderr": 0.03861229196653695,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653695
},
"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": 0.5789473684210527,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.046446020912223177
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}
```
### 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_migtissera__Tess-M-v1.3 | [
"region:us"
] | 2023-12-04T23:35:40+00:00 | {"pretty_name": "Evaluation run of migtissera/Tess-M-v1.3", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/Tess-M-v1.3](https://huggingface.co/migtissera/Tess-M-v1.3) 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_migtissera__Tess-M-v1.3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T23:32:51.712332](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-v1.3/blob/main/results_2023-12-04T23-32-51.712332.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\": 0.747566002523043,\n \"acc_stderr\": 0.028749261755203245,\n \"acc_norm\": 0.7529037953743296,\n \"acc_norm_stderr\": 0.029285728391357593,\n \"mc1\": 0.397796817625459,\n \"mc1_stderr\": 0.017133934248559638,\n \"mc2\": 0.5603469779031626,\n \"mc2_stderr\": 0.015661408014010857\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.014361097288449705,\n \"acc_norm\": 0.6254266211604096,\n \"acc_norm_stderr\": 0.014144193471893456\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6494722166899024,\n \"acc_stderr\": 0.004761601303258892,\n \"acc_norm\": 0.8394742083250348,\n \"acc_norm_stderr\": 0.0036634275361781586\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\": 0.7111111111111111,\n \"acc_stderr\": 0.03915450630414251,\n \"acc_norm\": 0.7111111111111111,\n \"acc_norm_stderr\": 0.03915450630414251\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8618421052631579,\n \"acc_stderr\": 0.028081042939576552,\n \"acc_norm\": 0.8618421052631579,\n \"acc_norm_stderr\": 0.028081042939576552\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.8377358490566038,\n \"acc_stderr\": 0.022691482872035353,\n \"acc_norm\": 0.8377358490566038,\n \"acc_norm_stderr\": 0.022691482872035353\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.026280550932848062,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.026280550932848062\n },\n \"harness|hendrycksTest-college_chemistry|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_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\": 0.48,\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\": 0.7109826589595376,\n \"acc_stderr\": 0.03456425745086999,\n \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.03456425745086999\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\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n 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#region-us
|
# Dataset Card for Evaluation run of migtissera/Tess-M-v1.3
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model migtissera/Tess-M-v1.3 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 2023-12-04T23:32:51.712332(note that their might be results for other tasks in 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
| [
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] |
6f512972fe3a5890afe987cd9cb3f5b51d77872d |
# Instruction-Following Evaluation Dataset
## 📜 Overview
This dataset, specifically designed for the **evaluation of large language models in instruction-following tasks**, is directly inspired by the methodologies and experiments described in the paper titled _"Instruction-Following Evaluation for Large Language Models"_. The dataset's creation and availability on HuggingFace are aimed at enhancing research and application in the field of natural language understanding, particularly in the context of instruction interpretation and execution by AI models.
## 🌐 Source
The dataset draws its structure and content from the insights provided in:
- **Original Research Paper**: [_"Instruction-Following Evaluation for Large Language Models"_](https://arxiv.org/abs/2311.07911)
- **Original Data Repository**: [Google Research on GitHub](https://github.com/google-research/google-research/tree/master/instruction_following_eval)
## 📊 Dataset Structure
Comprising primarily of **'prompts'**, this dataset is tailored to challenge and assess language models on various facets of understanding and executing instructions. Each prompt represents a unique scenario or task, simulating real-world applications where accurate interpretation of instructions is crucial.
## 💡 Usage
Targeted for use within the **HuggingFace ecosystem**, this dataset serves as a pivotal tool for researchers and developers focusing on the advancement of language models. It stands as a benchmark for:
- 📈 Evaluating model performance in instruction-following tasks.
- 🔍 Identifying model capabilities and areas of improvement.
- 🤖 Enhancing AI's understanding of complex, human-like commands.
## 🙏 Acknowledgements
This dataset is a tribute to the foundational work presented in the original paper and is intended for academic and research purposes. It reflects a commitment to furthering the understanding of AI's interaction with human language, particularly in processing and responding to diverse and complex instructions.
| harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models | [
"arxiv:2311.07911",
"region:us"
] | 2023-12-04T23:42:12+00:00 | {"dataset_info": {"features": [{"name": "key", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "instruction_id_list", "sequence": "string"}, {"name": "kwargs", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 181824, "num_examples": 541}], "download_size": 80840, "dataset_size": 181824}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-16T01:06:32+00:00 | [
"2311.07911"
] | [] | TAGS
#arxiv-2311.07911 #region-us
|
# Instruction-Following Evaluation Dataset
## Overview
This dataset, specifically designed for the evaluation of large language models in instruction-following tasks, is directly inspired by the methodologies and experiments described in the paper titled _"Instruction-Following Evaluation for Large Language Models"_. The dataset's creation and availability on HuggingFace are aimed at enhancing research and application in the field of natural language understanding, particularly in the context of instruction interpretation and execution by AI models.
## Source
The dataset draws its structure and content from the insights provided in:
- Original Research Paper: _"Instruction-Following Evaluation for Large Language Models"_
- Original Data Repository: Google Research on GitHub
## Dataset Structure
Comprising primarily of 'prompts', this dataset is tailored to challenge and assess language models on various facets of understanding and executing instructions. Each prompt represents a unique scenario or task, simulating real-world applications where accurate interpretation of instructions is crucial.
## Usage
Targeted for use within the HuggingFace ecosystem, this dataset serves as a pivotal tool for researchers and developers focusing on the advancement of language models. It stands as a benchmark for:
- Evaluating model performance in instruction-following tasks.
- Identifying model capabilities and areas of improvement.
- Enhancing AI's understanding of complex, human-like commands.
## Acknowledgements
This dataset is a tribute to the foundational work presented in the original paper and is intended for academic and research purposes. It reflects a commitment to furthering the understanding of AI's interaction with human language, particularly in processing and responding to diverse and complex instructions.
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] |
2e3aff5b8d58eade73a6b3760088399ebecfc034 |
# Dataset Card for Evaluation run of APMIC/caigun-lora-model-33B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/APMIC/caigun-lora-model-33B
- **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 [APMIC/caigun-lora-model-33B](https://huggingface.co/APMIC/caigun-lora-model-33B) 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_APMIC__caigun-lora-model-33B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T00:06:51.823733](https://huggingface.co/datasets/open-llm-leaderboard/details_APMIC__caigun-lora-model-33B/blob/main/results_2023-12-05T00-06-51.823733.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": 0.23196194129343728,
"acc_stderr": 0.029934654752561563,
"acc_norm": 0.2314240573187148,
"acc_norm_stderr": 0.03071122006512167,
"mc1": 1.0,
"mc1_stderr": 0.0,
"mc2": NaN,
"mc2_stderr": NaN
},
"harness|arc:challenge|25": {
"acc": 0.22696245733788395,
"acc_stderr": 0.012240491536132861,
"acc_norm": 0.22696245733788395,
"acc_norm_stderr": 0.012240491536132861
},
"harness|hellaswag|10": {
"acc": 0.2504481179047998,
"acc_stderr": 0.004323856300539177,
"acc_norm": 0.2504481179047998,
"acc_norm_stderr": 0.004323856300539177
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
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}
```
### 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_APMIC__caigun-lora-model-33B | [
"region:us"
] | 2023-12-05T00:09:10+00:00 | {"pretty_name": "Evaluation run of APMIC/caigun-lora-model-33B", "dataset_summary": "Dataset automatically created during the evaluation run of model [APMIC/caigun-lora-model-33B](https://huggingface.co/APMIC/caigun-lora-model-33B) 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_APMIC__caigun-lora-model-33B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T00:06:51.823733](https://huggingface.co/datasets/open-llm-leaderboard/details_APMIC__caigun-lora-model-33B/blob/main/results_2023-12-05T00-06-51.823733.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\": 0.23196194129343728,\n \"acc_stderr\": 0.029934654752561563,\n \"acc_norm\": 0.2314240573187148,\n \"acc_norm_stderr\": 0.03071122006512167,\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.22696245733788395,\n \"acc_stderr\": 0.012240491536132861,\n \"acc_norm\": 0.22696245733788395,\n \"acc_norm_stderr\": 0.012240491536132861\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2504481179047998,\n \"acc_stderr\": 0.004323856300539177,\n \"acc_norm\": 0.2504481179047998,\n \"acc_norm_stderr\": 0.004323856300539177\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\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\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n 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["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": 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["**/details_harness|winogrande|5_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-05T00-06-51.823733.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_05T00_06_51.823733", "path": ["results_2023-12-05T00-06-51.823733.parquet"]}, {"split": "latest", "path": ["results_2023-12-05T00-06-51.823733.parquet"]}]}]} | 2023-12-05T00:09:53+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of APMIC/caigun-lora-model-33B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model APMIC/caigun-lora-model-33B 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 2023-12-05T00:06:51.823733(note that their might be results for other tasks in 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
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] |
46ccda446ca0f4cf303653d9022928487e047657 | # Dataset Card for "INFO-desc-llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fightfei/INFO-desc-llama2 | [
"region:us"
] | 2023-12-05T00:09:48+00:00 | {"dataset_info": {"features": [{"name": "Subject Code", "dtype": "string"}, {"name": "Subject number", "dtype": "int64"}, {"name": "Unnamed: 2", "dtype": "string"}, {"name": "Hours", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2394.0, "num_examples": 36}, {"name": "test", "num_bytes": 266.0, "num_examples": 4}], "download_size": 6214, "dataset_size": 2660.0}} | 2023-12-05T00:11:53+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "INFO-desc-llama2"
More Information needed | [
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44282dea81be1fefa3bfd791e5ee686060b6aa28 | # Dataset Card for "fintuned-llm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mhzarem76/fintuned-llm | [
"region:us"
] | 2023-12-05T00:46:14+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18997844, "num_examples": 51942}], "download_size": 11986973, "dataset_size": 18997844}} | 2023-12-05T00:46:16+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "fintuned-llm"
More Information needed | [
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d87e35b04df2db9ee020930f90cab1d18822d2b9 |
# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-LoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/kyujinpy/PlatYi-34B-LoRA
- **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-LoRA](https://huggingface.co/kyujinpy/PlatYi-34B-LoRA) 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-LoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T00:53:53.251371](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-LoRA/blob/main/results_2023-12-05T00-53-53.251371.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": 0.774827191806162,
"acc_stderr": 0.027570240573755966,
"acc_norm": 0.7838406442171528,
"acc_norm_stderr": 0.028074245588194685,
"mc1": 0.3733170134638923,
"mc1_stderr": 0.01693237055757063,
"mc2": 0.5332425206331443,
"mc2_stderr": 0.014839740435159312
},
"harness|arc:challenge|25": {
"acc": 0.643344709897611,
"acc_stderr": 0.013998056902620189,
"acc_norm": 0.6715017064846417,
"acc_norm_stderr": 0.013724978465537297
},
"harness|hellaswag|10": {
"acc": 0.6567416849233221,
"acc_stderr": 0.00473826494473715,
"acc_norm": 0.8537143995220076,
"acc_norm_stderr": 0.003526700741879443
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"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": 0.8947368421052632,
"acc_stderr": 0.024974533450920697,
"acc_norm": 0.8947368421052632,
"acc_norm_stderr": 0.024974533450920697
},
"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": 0.8,
"acc_stderr": 0.02461829819586651,
"acc_norm": 0.8,
"acc_norm_stderr": 0.02461829819586651
},
"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": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"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": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7398843930635838,
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}
```
### 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-LoRA | [
"region:us"
] | 2023-12-05T00:56:39+00:00 | {"pretty_name": "Evaluation run of kyujinpy/PlatYi-34B-LoRA", "dataset_summary": "Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-LoRA](https://huggingface.co/kyujinpy/PlatYi-34B-LoRA) 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-LoRA\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T00:53:53.251371](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-LoRA/blob/main/results_2023-12-05T00-53-53.251371.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\": 0.774827191806162,\n \"acc_stderr\": 0.027570240573755966,\n \"acc_norm\": 0.7838406442171528,\n \"acc_norm_stderr\": 0.028074245588194685,\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.01693237055757063,\n \"mc2\": 0.5332425206331443,\n \"mc2_stderr\": 0.014839740435159312\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620189,\n \"acc_norm\": 0.6715017064846417,\n \"acc_norm_stderr\": 0.013724978465537297\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6567416849233221,\n \"acc_stderr\": 0.00473826494473715,\n \"acc_norm\": 0.8537143995220076,\n \"acc_norm_stderr\": 0.003526700741879443\n },\n \"harness|hendrycksTest-abstract_algebra|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-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\": 0.8947368421052632,\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\": 0.81,\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\": 0.8,\n \"acc_stderr\": 0.02461829819586651,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02461829819586651\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\": 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#region-us
|
# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-LoRA
## 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-LoRA 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 2023-12-05T00:53:53.251371(note that their might be results for other tasks in 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
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] |
0cc7adc5d5eb8e2651a2d69767cbd59ac57c4b56 | # Dataset Card for "dafny-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | metareflection/dafny-train | [
"region:us"
] | 2023-12-05T00:59:36+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 122357884, "num_examples": 5619}], "download_size": 16684709, "dataset_size": 122357884}} | 2023-12-05T00:59:43+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "dafny-train"
More Information needed | [
"# Dataset Card for \"dafny-train\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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b770218214a63a7ffb28bf47e400df57650df2b0 | # Dataset Card for "fintuned-llm2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mhzarem76/fintuned-llm2 | [
"region:us"
] | 2023-12-05T01:02:54+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18997844, "num_examples": 51942}], "download_size": 11986973, "dataset_size": 18997844}} | 2023-12-05T01:02:56+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "fintuned-llm2"
More Information needed | [
"# Dataset Card for \"fintuned-llm2\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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9bf70f95d828c70a2ee1856352c7edbb1ee19b64 | # Dataset Card for "kor_ai2_arc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
``` | KETI-AIR/kor_ai2_arc | [
"license:cc-by-sa-4.0",
"region:us"
] | 2023-12-05T01:35:37+00:00 | {"license": "cc-by-sa-4.0", "configs": [{"config_name": "ARC-Challenge", "data_files": [{"split": "train", "path": "ARC-Challenge/train-*"}, {"split": "validation", "path": "ARC-Challenge/validation-*"}, {"split": "test", "path": "ARC-Challenge/test-*"}]}, {"config_name": "ARC-Easy", "data_files": [{"split": "train", "path": "ARC-Easy/train-*"}, {"split": "validation", "path": "ARC-Easy/validation-*"}, {"split": "test", "path": "ARC-Easy/test-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": [{"config_name": "ARC-Challenge", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "struct": [{"name": "text", "sequence": "string"}, {"name": "label", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 396164, "num_examples": 1119}, {"name": "validation", "num_bytes": 108314, "num_examples": 299}, {"name": "test", "num_bytes": 425252, "num_examples": 1172}], "download_size": 516331, "dataset_size": 929730}, {"config_name": "ARC-Easy", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "struct": [{"name": "text", "sequence": "string"}, {"name": "label", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 694289, "num_examples": 2251}, {"name": "validation", "num_bytes": 175983, "num_examples": 570}, {"name": "test", "num_bytes": 735067, "num_examples": 2376}], "download_size": 861121, "dataset_size": 1605339}, {"config_name": "default", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "struct": [{"name": "text", "sequence": "string"}, {"name": "label", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 694289, "num_examples": 2251}, {"name": "validation", "num_bytes": 175983, "num_examples": 570}, {"name": "test", "num_bytes": 735067, "num_examples": 2376}], "download_size": 861121, "dataset_size": 1605339}]} | 2023-12-05T02:37:22+00:00 | [] | [] | TAGS
#license-cc-by-sa-4.0 #region-us
| # Dataset Card for "kor_ai2_arc"
More Information needed
# Source Data Citation Information
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e94e035c3c3efe7564f3386bc77a9d2d36f53644 |
# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-33B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/Python-Code-33B
- **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/Python-Code-33B](https://huggingface.co/ajibawa-2023/Python-Code-33B) 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__Python-Code-33B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T01:45:33.454054](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Python-Code-33B/blob/main/results_2023-12-05T01-45-33.454054.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": 0.5411850719173311,
"acc_stderr": 0.03390619726316288,
"acc_norm": 0.5470787854450224,
"acc_norm_stderr": 0.034649290725190234,
"mc1": 0.2876376988984088,
"mc1_stderr": 0.01584631510139481,
"mc2": 0.443943398739872,
"mc2_stderr": 0.01568143022823914
},
"harness|arc:challenge|25": {
"acc": 0.5418088737201365,
"acc_stderr": 0.014560220308714698,
"acc_norm": 0.5631399317406144,
"acc_norm_stderr": 0.014494421584256527
},
"harness|hellaswag|10": {
"acc": 0.622087233618801,
"acc_stderr": 0.004838747305783349,
"acc_norm": 0.8100975901214897,
"acc_norm_stderr": 0.003914221738689083
},
"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": 0.45185185185185184,
"acc_stderr": 0.04299268905480863,
"acc_norm": 0.45185185185185184,
"acc_norm_stderr": 0.04299268905480863
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5460526315789473,
"acc_stderr": 0.04051646342874143,
"acc_norm": 0.5460526315789473,
"acc_norm_stderr": 0.04051646342874143
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5283018867924528,
"acc_stderr": 0.0307235352490061,
"acc_norm": 0.5283018867924528,
"acc_norm_stderr": 0.0307235352490061
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.04076663253918567,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.04076663253918567
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
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}
```
### 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__Python-Code-33B | [
"region:us"
] | 2023-12-05T01:47:51+00:00 | {"pretty_name": "Evaluation run of ajibawa-2023/Python-Code-33B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ajibawa-2023/Python-Code-33B](https://huggingface.co/ajibawa-2023/Python-Code-33B) 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__Python-Code-33B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T01:45:33.454054](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Python-Code-33B/blob/main/results_2023-12-05T01-45-33.454054.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\": 0.5411850719173311,\n \"acc_stderr\": 0.03390619726316288,\n \"acc_norm\": 0.5470787854450224,\n \"acc_norm_stderr\": 0.034649290725190234,\n \"mc1\": 0.2876376988984088,\n \"mc1_stderr\": 0.01584631510139481,\n \"mc2\": 0.443943398739872,\n \"mc2_stderr\": 0.01568143022823914\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5418088737201365,\n \"acc_stderr\": 0.014560220308714698,\n \"acc_norm\": 0.5631399317406144,\n \"acc_norm_stderr\": 0.014494421584256527\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.622087233618801,\n \"acc_stderr\": 0.004838747305783349,\n \"acc_norm\": 0.8100975901214897,\n \"acc_norm_stderr\": 0.003914221738689083\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\": 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#region-us
|
# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-33B
## 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/Python-Code-33B 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 2023-12-05T01:45:33.454054(note that their might be results for other tasks in 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
| [
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130efdbaf6c6f8a8e519ee210405ee764692ddb8 |
The dataset comprises aerial imagery of Dubai acquired by MBRSC satellites and annotated with pixel-level semantic segmentation across 6 distinct classes. The dataset comprises a total of 72 images, which are organised into 6 larger tiles. The categories are as follows:
Credit: Humans in the Loop is releasing an openly accessible dataset that has been annotated for a collaborative project with the Mohammed Bin Rashid Space Centre in Dubai, United Arab Emirates.
<a href="http://projectcentersinchennai.co.in/Final-Year-Projects-for-CSE/Final-Year-Projects-for-CSE-Deep-learning-Domain" title="Deep Learning Projects for Final Year">Deep Learning Projects for Final Year</a> | gymprathap/Semantic-Segmentation-Aerial-Imagery-Dataset | [
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
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"climate",
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"en"
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#task_categories-feature-extraction #size_categories-n<1K #language-English #license-cc-by-4.0 #climate #region-us
|
The dataset comprises aerial imagery of Dubai acquired by MBRSC satellites and annotated with pixel-level semantic segmentation across 6 distinct classes. The dataset comprises a total of 72 images, which are organised into 6 larger tiles. The categories are as follows:
Credit: Humans in the Loop is releasing an openly accessible dataset that has been annotated for a collaborative project with the Mohammed Bin Rashid Space Centre in Dubai, United Arab Emirates.
<a href="URL title="Deep Learning Projects for Final Year">Deep Learning Projects for Final Year</a> | [] | [
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8bc89c0e523b645e6933cb0b107d9276c6ca2cb2 | # Dataset Card for "kor_commonsense_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
archivePrefix = "arXiv",
eprint = "1811.00937",
primaryClass = "cs",
}
``` | KETI-AIR/kor_commonsense_qa | [
"license:mit",
"region:us"
] | 2023-12-05T02:17:36+00:00 | {"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_concept", "dtype": "string"}, {"name": "choices", "struct": [{"name": "text", "sequence": "string"}, {"name": "label", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2642161, "num_examples": 9741}, {"name": "validation", "num_bytes": 327694, "num_examples": 1221}, {"name": "test", "num_bytes": 309213, "num_examples": 1140}], "download_size": 1782280, "dataset_size": 3279068}} | 2023-12-05T02:36:54+00:00 | [] | [] | TAGS
#license-mit #region-us
| # Dataset Card for "kor_commonsense_qa"
More Information needed
# Source Data Citation Information
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a21f12834657cc0a2badd311003dab2597c0b124 | # Dataset Card for "librispeech960-wavlm-large-km1000_asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cmu-mlsp/librispeech960-wavlm-large-km1000_asr | [
"region:us"
] | 2023-12-05T02:19:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation_other", "path": "data/validation_other-*"}, {"split": "test_other", "path": "data/test_other-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio_codes", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1246247156, "num_examples": 281241}, {"name": "validation", "num_bytes": 7052458, "num_examples": 2703}, {"name": "test", "num_bytes": 7062964, "num_examples": 2620}, {"name": "validation_other", "num_bytes": 6706447, "num_examples": 2864}, {"name": "test_other", "num_bytes": 6987808, "num_examples": 2939}], "download_size": 254541270, "dataset_size": 1274056833}} | 2023-12-05T02:20:54+00:00 | [] | [] | TAGS
#region-us
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More Information needed | [
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202b971078b1b1be719bab211bfdc7b9848bed71 | This dataset is used to adapt [DeepLabCut](https://www.mackenziemathislab.org/deeplabcut) for Human motion tracking.
### Structure of the dataset
- `videos` contains 100+ videos of 4 candidates recorded during a game of darts.
- `labeled-data` contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under `labeled-data` there are 2 folders for every video.
- `video_name` has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.
- `video_name`_labeled has the overlay of the labels for the frames in `video_name`. | pratikshapai/human-motion-tracking-deeplabcut | [
"region:us"
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#region-us
| This dataset is used to adapt DeepLabCut for Human motion tracking.
### Structure of the dataset
- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.
- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.
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"### Structure of the dataset\n- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.\n- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.\n - 'video_name' has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.\n - 'video_name'_labeled has the overlay of the labels for the frames in 'video_name'."
] | [
"TAGS\n#region-us \n",
"### Structure of the dataset\n- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.\n- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.\n - 'video_name' has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.\n - 'video_name'_labeled has the overlay of the labels for the frames in 'video_name'."
] | [
6,
153
] | [
"passage: TAGS\n#region-us \n### Structure of the dataset\n- 'videos' contains 100+ videos of 4 candidates recorded during a game of darts.\n- 'labeled-data' contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under 'labeled-data' there are 2 folders for every video.\n - 'video_name' has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the corresponding h5 file.\n - 'video_name'_labeled has the overlay of the labels for the frames in 'video_name'."
] |
a3c887e7f0c63f82551874a7354582323b7ac519 |
# Dataset Card for Evaluation run of brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/brucethemoose/CapyTessBorosYi-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/CapyTessBorosYi-34B-200K-DARE-Ties](https://huggingface.co/brucethemoose/CapyTessBorosYi-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__CapyTessBorosYi-34B-200K-DARE-Ties",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T03:16:54.690977](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CapyTessBorosYi-34B-200K-DARE-Ties/blob/main/results_2023-12-05T03-16-54.690977.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": 0.7567711901753588,
"acc_stderr": 0.028382267920122734,
"acc_norm": 0.7615616815437645,
"acc_norm_stderr": 0.028914131489708655,
"mc1": 0.40514075887392903,
"mc1_stderr": 0.017185611727753368,
"mc2": 0.5583921075323958,
"mc2_stderr": 0.015750345067611658
},
"harness|arc:challenge|25": {
"acc": 0.6203071672354948,
"acc_stderr": 0.014182119866974872,
"acc_norm": 0.6493174061433447,
"acc_norm_stderr": 0.013944635930726097
},
"harness|hellaswag|10": {
"acc": 0.6693885680143398,
"acc_stderr": 0.004694718918225748,
"acc_norm": 0.8591913961362279,
"acc_norm_stderr": 0.0034711315448920457
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"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": 0.9078947368421053,
"acc_stderr": 0.02353268597044349,
"acc_norm": 0.9078947368421053,
"acc_norm_stderr": 0.02353268597044349
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8301886792452831,
"acc_stderr": 0.02310839379984132,
"acc_norm": 0.8301886792452831,
"acc_norm_stderr": 0.02310839379984132
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.026280550932848076,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.026280550932848076
},
"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": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"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": 0.7456647398843931,
"acc_stderr": 0.0332055644308557,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.0332055644308557
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.049512182523962604,
"acc_norm": 0.5490196078431373,
"acc_norm_stderr": 0.049512182523962604
},
"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": {
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"acc_stderr": 0.026947483121496224,
"acc_norm": 0.7829787234042553,
"acc_norm_stderr": 0.026947483121496224
},
"harness|hendrycksTest-econometrics|5": {
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"acc_stderr": 0.045981880578165414,
"acc_norm": 0.6052631578947368,
"acc_norm_stderr": 0.045981880578165414
},
"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": {
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"acc_norm": 0.6878306878306878,
"acc_norm_stderr": 0.023865206836972592
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.04458029125470973,
"acc_norm": 0.5396825396825397,
"acc_norm_stderr": 0.04458029125470973
},
"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": {
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"acc_norm": 0.896774193548387,
"acc_norm_stderr": 0.01730838128103453
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_stderr": 0.03355400904969566,
"acc_norm": 0.6502463054187192,
"acc_norm_stderr": 0.03355400904969566
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.8,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.8,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.8606060606060606,
"acc_norm_stderr": 0.027045948825865394
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.9343434343434344,
"acc_norm_stderr": 0.01764652667723332
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_stderr": 0.01146452335695318,
"acc_norm": 0.9740932642487047,
"acc_norm_stderr": 0.01146452335695318
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_stderr": 0.019982347208637303,
"acc_norm": 0.8076923076923077,
"acc_norm_stderr": 0.019982347208637303
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"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_stderr": 0.029914812342227627,
"acc_norm": 0.40370370370370373,
"acc_norm_stderr": 0.029914812342227627
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8571428571428571,
"acc_stderr": 0.02273020811930654,
"acc_norm": 0.8571428571428571,
"acc_norm_stderr": 0.02273020811930654
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.5033112582781457,
"acc_stderr": 0.04082393379449654,
"acc_norm": 0.5033112582781457,
"acc_norm_stderr": 0.04082393379449654
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9229357798165138,
"acc_stderr": 0.011434381698911096,
"acc_norm": 0.9229357798165138,
"acc_norm_stderr": 0.011434381698911096
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"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.03293377139415191,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.03293377139415191
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9215686274509803,
"acc_stderr": 0.018869514646658928,
"acc_norm": 0.9215686274509803,
"acc_norm_stderr": 0.018869514646658928
},
"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": {
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"acc_norm": 0.7982062780269058,
"acc_norm_stderr": 0.02693611191280227
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"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm": 0.8702290076335878,
"acc_norm_stderr": 0.029473649496907065
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8677685950413223,
"acc_stderr": 0.030922788320445784,
"acc_norm": 0.8677685950413223,
"acc_norm_stderr": 0.030922788320445784
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.03038159675665168,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.03038159675665168
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.8588957055214724,
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},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.6160714285714286,
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},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
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"acc_norm": 0.8446601941747572,
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},
"harness|hendrycksTest-marketing|5": {
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"acc_stderr": 0.016046261631673137,
"acc_norm": 0.9358974358974359,
"acc_norm_stderr": 0.016046261631673137
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.9106002554278416,
"acc_stderr": 0.010203017847688303,
"acc_norm": 0.9106002554278416,
"acc_norm_stderr": 0.010203017847688303
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8179190751445087,
"acc_stderr": 0.020776761102512992,
"acc_norm": 0.8179190751445087,
"acc_norm_stderr": 0.020776761102512992
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_stderr": 0.015183844307206165,
"acc_norm": 0.7094972067039106,
"acc_norm_stderr": 0.015183844307206165
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8366013071895425,
"acc_stderr": 0.021170623011213505,
"acc_norm": 0.8366013071895425,
"acc_norm_stderr": 0.021170623011213505
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.8006430868167203,
"acc_stderr": 0.022691033780549656,
"acc_norm": 0.8006430868167203,
"acc_norm_stderr": 0.022691033780549656
},
"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": 0.6453900709219859,
"acc_stderr": 0.02853865002887863,
"acc_norm": 0.6453900709219859,
"acc_norm_stderr": 0.02853865002887863
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5873533246414603,
"acc_stderr": 0.012573836633799022,
"acc_norm": 0.5873533246414603,
"acc_norm_stderr": 0.012573836633799022
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8088235294117647,
"acc_stderr": 0.02388688192244033,
"acc_norm": 0.8088235294117647,
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.015478369653108566
},
"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": 0.8530612244897959,
"acc_stderr": 0.022665400417217638,
"acc_norm": 0.8530612244897959,
"acc_norm_stderr": 0.022665400417217638
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.9104477611940298,
"acc_stderr": 0.020190670535027908,
"acc_norm": 0.9104477611940298,
"acc_norm_stderr": 0.020190670535027908
},
"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": 0.8596491228070176,
"acc_stderr": 0.026640582539133196,
"acc_norm": 0.8596491228070176,
"acc_norm_stderr": 0.026640582539133196
},
"harness|truthfulqa:mc|0": {
"mc1": 0.40514075887392903,
"mc1_stderr": 0.017185611727753368,
"mc2": 0.5583921075323958,
"mc2_stderr": 0.015750345067611658
},
"harness|winogrande|5": {
"acc": 0.8303078137332282,
"acc_stderr": 0.010549542647363698
},
"harness|gsm8k|5": {
"acc": 0.6194086429112965,
"acc_stderr": 0.013373971277729817
}
}
```
### 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__CapyTessBorosYi-34B-200K-DARE-Ties | [
"region:us"
] | 2023-12-05T03:19:43+00:00 | {"pretty_name": "Evaluation run of brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties](https://huggingface.co/brucethemoose/CapyTessBorosYi-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__CapyTessBorosYi-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 2023-12-05T03:16:54.690977](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CapyTessBorosYi-34B-200K-DARE-Ties/blob/main/results_2023-12-05T03-16-54.690977.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\": 0.7567711901753588,\n \"acc_stderr\": 0.028382267920122734,\n \"acc_norm\": 0.7615616815437645,\n \"acc_norm_stderr\": 0.028914131489708655,\n \"mc1\": 0.40514075887392903,\n \"mc1_stderr\": 0.017185611727753368,\n \"mc2\": 0.5583921075323958,\n \"mc2_stderr\": 0.015750345067611658\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6203071672354948,\n \"acc_stderr\": 0.014182119866974872,\n \"acc_norm\": 0.6493174061433447,\n \"acc_norm_stderr\": 0.013944635930726097\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6693885680143398,\n \"acc_stderr\": 0.004694718918225748,\n \"acc_norm\": 0.8591913961362279,\n \"acc_norm_stderr\": 0.0034711315448920457\n },\n \"harness|hendrycksTest-abstract_algebra|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-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\": 0.9078947368421053,\n \"acc_stderr\": 0.02353268597044349,\n \"acc_norm\": 0.9078947368421053,\n \"acc_norm_stderr\": 0.02353268597044349\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8301886792452831,\n \"acc_stderr\": 0.02310839379984132,\n \"acc_norm\": 0.8301886792452831,\n \"acc_norm_stderr\": 0.02310839379984132\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.026280550932848076,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.026280550932848076\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\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\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\": 0.7456647398843931,\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\": 0.5490196078431373,\n \"acc_stderr\": 0.049512182523962604,\n \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.049512182523962604\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\": 0.026947483121496224,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.026947483121496224\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.045981880578165414\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\": 0.6878306878306878,\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\": 0.5396825396825397,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n \"acc_norm_stderr\": 0.04458029125470973\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\": 0.896774193548387,\n \"acc_stderr\": 0.01730838128103453,\n \"acc_norm\": 0.896774193548387,\n \"acc_norm_stderr\": 0.01730838128103453\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6502463054187192,\n \"acc_stderr\": 0.03355400904969566,\n \"acc_norm\": 0.6502463054187192,\n \"acc_norm_stderr\": 0.03355400904969566\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.040201512610368445\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\": 0.01764652667723332,\n \"acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.01764652667723332\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\": 0.8076923076923077,\n \"acc_stderr\": 0.019982347208637303,\n \"acc_norm\": 0.8076923076923077,\n \"acc_norm_stderr\": 0.019982347208637303\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.40370370370370373,\n \"acc_stderr\": 0.029914812342227627,\n \"acc_norm\": 0.40370370370370373,\n \"acc_norm_stderr\": 0.029914812342227627\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8571428571428571,\n \"acc_stderr\": 0.02273020811930654,\n \"acc_norm\": 0.8571428571428571,\n \"acc_norm_stderr\": 0.02273020811930654\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9229357798165138,\n \"acc_stderr\": 0.011434381698911096,\n \"acc_norm\": 0.9229357798165138,\n \"acc_norm_stderr\": 0.011434381698911096\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.03293377139415191,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.03293377139415191\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\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\": 0.8702290076335878,\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\": 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445784,\n \"acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445784\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.03038159675665168,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.03038159675665168\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\": 0.6160714285714286,\n \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.6160714285714286,\n \"acc_norm_stderr\": 0.04616143075028546\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\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9106002554278416,\n \"acc_stderr\": 0.010203017847688303,\n \"acc_norm\": 0.9106002554278416,\n \"acc_norm_stderr\": 0.010203017847688303\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8179190751445087,\n \"acc_stderr\": 0.020776761102512992,\n \"acc_norm\": 0.8179190751445087,\n \"acc_norm_stderr\": 0.020776761102512992\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7094972067039106,\n \"acc_stderr\": 0.015183844307206165,\n \"acc_norm\": 0.7094972067039106,\n \"acc_norm_stderr\": 0.015183844307206165\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8366013071895425,\n \"acc_stderr\": 0.021170623011213505,\n \"acc_norm\": 0.8366013071895425,\n \"acc_norm_stderr\": 0.021170623011213505\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8006430868167203,\n \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8827160493827161,\n \"acc_stderr\": 0.017903112615281123,\n \"acc_norm\": 0.8827160493827161,\n \"acc_norm_stderr\": 0.017903112615281123\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.6453900709219859,\n \"acc_stderr\": 0.02853865002887863,\n \"acc_norm\": 0.6453900709219859,\n \"acc_norm_stderr\": 0.02853865002887863\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5873533246414603,\n \"acc_stderr\": 0.012573836633799022,\n \"acc_norm\": 0.5873533246414603,\n \"acc_norm_stderr\": 0.012573836633799022\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.02388688192244033,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.02388688192244033\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8218954248366013,\n \"acc_stderr\": 0.015478369653108566,\n \"acc_norm\": 0.8218954248366013,\n \"acc_norm_stderr\": 0.015478369653108566\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\": 0.8530612244897959,\n \"acc_stderr\": 0.022665400417217638,\n \"acc_norm\": 0.8530612244897959,\n \"acc_norm_stderr\": 0.022665400417217638\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n \"acc_stderr\": 0.020190670535027908,\n \"acc_norm\": 0.9104477611940298,\n \"acc_norm_stderr\": 0.020190670535027908\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\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40514075887392903,\n \"mc1_stderr\": 0.017185611727753368,\n \"mc2\": 0.5583921075323958,\n \"mc2_stderr\": 0.015750345067611658\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8303078137332282,\n \"acc_stderr\": 0.010549542647363698\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6194086429112965,\n \"acc_stderr\": 0.013373971277729817\n }\n}\n```", "repo_url": "https://huggingface.co/brucethemoose/CapyTessBorosYi-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_05T03_16_54.690977", "path": ["**/details_harness|arc:challenge|25_2023-12-05T03-16-54.690977.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-05T03-16-54.690977.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_05T03_16_54.690977", "path": ["**/details_harness|gsm8k|5_2023-12-05T03-16-54.690977.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-05T03-16-54.690977.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_05T03_16_54.690977", "path": ["**/details_harness|hellaswag|10_2023-12-05T03-16-54.690977.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-05T03-16-54.690977.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_05T03_16_54.690977", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-16-54.690977.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-16-54.690977.parquet", 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#region-us
|
# Dataset Card for Evaluation run of brucethemoose/CapyTessBorosYi-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/CapyTessBorosYi-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 2023-12-05T03:16:54.690977(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CapyTessBorosYi-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 2023-12-05T03:16:54.690977(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Dataset Curators",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model brucethemoose/CapyTessBorosYi-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 2023-12-05T03:16:54.690977(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of brucethemoose/CapyTessBorosYi-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/CapyTessBorosYi-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 2023-12-05T03:16:54.690977(note that their might be results for other tasks in 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"
] |
0582076c7901fc28b6e10f61ae47686e4426d13d |
# Pexels 400k
Dataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption.
Note: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed.
| jovianzm/Pexels-400k | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_categories:text-to-video",
"task_categories:image-to-video",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | 2023-12-05T03:35:57+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text", "text-to-image", "text-to-video", "image-to-video"], "pretty_name": "Pexels-400k"} | 2024-01-15T19:04:50+00:00 | [] | [
"en"
] | TAGS
#task_categories-image-to-text #task_categories-text-to-image #task_categories-text-to-video #task_categories-image-to-video #size_categories-100K<n<1M #language-English #license-mit #region-us
|
# Pexels 400k
Dataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption.
Note: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed.
| [
"# Pexels 400k\n\nDataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption.\n\nNote: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed."
] | [
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"# Pexels 400k\n\nDataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption.\n\nNote: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed."
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59
] | [
"passage: TAGS\n#task_categories-image-to-text #task_categories-text-to-image #task_categories-text-to-video #task_categories-image-to-video #size_categories-100K<n<1M #language-English #license-mit #region-us \n# Pexels 400k\n\nDataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption.\n\nNote: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed."
] |
ddd27b58da8d587fd3b8a3450a379f2fce03bc9d | This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0 | JayMaier/assistant_test | [
"region:us"
] | 2023-12-05T03:38:08+00:00 | {} | 2023-12-05T05:04:37+00:00 | [] | [] | TAGS
#region-us
| This dataset is a subset of the Open Assistant dataset, which you can find here: URL
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0 | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
cadc7e631598d6b0631754cb167cfbac6aee1416 |
# Dataset Card for Evaluation run of 01-ai/Yi-34B-200K
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/01-ai/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 [01-ai/Yi-34B-200K](https://huggingface.co/01-ai/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_01-ai__Yi-34B-200K",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T03:41:41.478096](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-34B-200K/blob/main/results_2023-12-05T03-41-41.478096.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": 0.7553618929104267,
"acc_stderr": 0.02837585903729335,
"acc_norm": 0.7603811984841083,
"acc_norm_stderr": 0.028905075105130153,
"mc1": 0.3818849449204406,
"mc1_stderr": 0.017008101939163495,
"mc2": 0.5364445120598228,
"mc2_stderr": 0.014804162952722544
},
"harness|arc:challenge|25": {
"acc": 0.6262798634812287,
"acc_stderr": 0.014137708601759091,
"acc_norm": 0.6535836177474402,
"acc_norm_stderr": 0.013905011180063227
},
"harness|hellaswag|10": {
"acc": 0.6557458673571002,
"acc_stderr": 0.004741534106470288,
"acc_norm": 0.8558056164110734,
"acc_norm_stderr": 0.0035056879433872927
},
"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": 0.7185185185185186,
"acc_stderr": 0.038850042458002526,
"acc_norm": 0.7185185185185186,
"acc_norm_stderr": 0.038850042458002526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8618421052631579,
"acc_stderr": 0.028081042939576552,
"acc_norm": 0.8618421052631579,
"acc_norm_stderr": 0.028081042939576552
},
"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": 0.8226415094339623,
"acc_stderr": 0.02350873921884694,
"acc_norm": 0.8226415094339623,
"acc_norm_stderr": 0.02350873921884694
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.875,
"acc_stderr": 0.02765610492929436,
"acc_norm": 0.875,
"acc_norm_stderr": 0.02765610492929436
},
"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": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7341040462427746,
"acc_stderr": 0.033687629322594316,
"acc_norm": 0.7341040462427746,
"acc_norm_stderr": 0.033687629322594316
},
"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": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7659574468085106,
"acc_stderr": 0.02767845257821239,
"acc_norm": 0.7659574468085106,
"acc_norm_stderr": 0.02767845257821239
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5964912280701754,
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"acc_norm": 0.5964912280701754,
"acc_norm_stderr": 0.04615186962583707
},
"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": {
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"acc_norm": 0.6322751322751323,
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"acc_norm": 0.5793650793650794,
"acc_norm_stderr": 0.04415438226743745
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.8935483870967742,
"acc_norm_stderr": 0.01754510295165663
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.6748768472906403,
"acc_norm_stderr": 0.032957975663112704
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"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8484848484848485,
"acc_stderr": 0.027998073798781675,
"acc_norm": 0.8484848484848485,
"acc_norm_stderr": 0.027998073798781675
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_stderr": 0.019417681889724536,
"acc_norm": 0.9191919191919192,
"acc_norm_stderr": 0.019417681889724536
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9792746113989638,
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"acc_norm": 0.9792746113989638,
"acc_norm_stderr": 0.010281417011909039
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm": 0.8051282051282052,
"acc_norm_stderr": 0.020083167595181393
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"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.029958249250082114
},
"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": 0.5099337748344371,
"acc_stderr": 0.04081677107248436,
"acc_norm": 0.5099337748344371,
"acc_norm_stderr": 0.04081677107248436
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9211009174311927,
"acc_stderr": 0.011558198113769574,
"acc_norm": 0.9211009174311927,
"acc_norm_stderr": 0.011558198113769574
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6481481481481481,
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"acc_norm": 0.6481481481481481,
"acc_norm_stderr": 0.03256850570293648
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_stderr": 0.019398452135813905,
"acc_norm": 0.9166666666666666,
"acc_norm_stderr": 0.019398452135813905
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm": 0.9113924050632911,
"acc_norm_stderr": 0.018498315206865384
},
"harness|hendrycksTest-human_aging|5": {
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"acc_norm": 0.8071748878923767,
"acc_norm_stderr": 0.026478240960489365
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm": 0.8625954198473282,
"acc_norm_stderr": 0.030194823996804468
},
"harness|hendrycksTest-international_law|5": {
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"acc_norm": 0.9090909090909091,
"acc_norm_stderr": 0.02624319405407388
},
"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": 0.8834355828220859,
"acc_stderr": 0.02521232721050711,
"acc_norm": 0.8834355828220859,
"acc_norm_stderr": 0.02521232721050711
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_stderr": 0.04718471485219587,
"acc_norm": 0.5535714285714286,
"acc_norm_stderr": 0.04718471485219587
},
"harness|hendrycksTest-management|5": {
"acc": 0.8640776699029126,
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"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.03393295729761011
},
"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": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.9042145593869731,
"acc_norm_stderr": 0.01052403107905584
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8092485549132948,
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"acc_norm": 0.8092485549132948,
"acc_norm_stderr": 0.021152676966575277
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6558659217877095,
"acc_stderr": 0.015889221313307094,
"acc_norm": 0.6558659217877095,
"acc_norm_stderr": 0.015889221313307094
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.869281045751634,
"acc_stderr": 0.01930187362421527,
"acc_norm": 0.869281045751634,
"acc_norm_stderr": 0.01930187362421527
},
"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": 0.8672839506172839,
"acc_stderr": 0.018877353839571853,
"acc_norm": 0.8672839506172839,
"acc_norm_stderr": 0.018877353839571853
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.6099290780141844,
"acc_stderr": 0.02909767559946393,
"acc_norm": 0.6099290780141844,
"acc_norm_stderr": 0.02909767559946393
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5990873533246415,
"acc_stderr": 0.012516960350640814,
"acc_norm": 0.5990873533246415,
"acc_norm_stderr": 0.012516960350640814
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8198529411764706,
"acc_stderr": 0.02334516361654484,
"acc_norm": 0.8198529411764706,
"acc_norm_stderr": 0.02334516361654484
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.8186274509803921,
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"acc_norm": 0.8186274509803921,
"acc_norm_stderr": 0.015588643495370463
},
"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": 0.8163265306122449,
"acc_stderr": 0.024789071332007646,
"acc_norm": 0.8163265306122449,
"acc_norm_stderr": 0.024789071332007646
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.9104477611940298,
"acc_stderr": 0.0201906705350279,
"acc_norm": 0.9104477611940298,
"acc_norm_stderr": 0.0201906705350279
},
"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": 0.5662650602409639,
"acc_stderr": 0.03858158940685516,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685516
},
"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": 0.3818849449204406,
"mc1_stderr": 0.017008101939163495,
"mc2": 0.5364445120598228,
"mc2_stderr": 0.014804162952722544
},
"harness|winogrande|5": {
"acc": 0.8255722178374112,
"acc_stderr": 0.010665187902498438
},
"harness|gsm8k|5": {
"acc": 0.6163760424564063,
"acc_stderr": 0.013394238584938161
}
}
```
### 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_01-ai__Yi-34B-200K | [
"region:us"
] | 2023-12-05T03:44:28+00:00 | {"pretty_name": "Evaluation run of 01-ai/Yi-34B-200K", "dataset_summary": "Dataset automatically created during the evaluation run of model [01-ai/Yi-34B-200K](https://huggingface.co/01-ai/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_01-ai__Yi-34B-200K\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T03:41:41.478096](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-34B-200K/blob/main/results_2023-12-05T03-41-41.478096.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\": 0.7553618929104267,\n \"acc_stderr\": 0.02837585903729335,\n \"acc_norm\": 0.7603811984841083,\n \"acc_norm_stderr\": 0.028905075105130153,\n \"mc1\": 0.3818849449204406,\n \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5364445120598228,\n \"mc2_stderr\": 0.014804162952722544\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759091,\n \"acc_norm\": 0.6535836177474402,\n \"acc_norm_stderr\": 0.013905011180063227\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6557458673571002,\n \"acc_stderr\": 0.004741534106470288,\n \"acc_norm\": 0.8558056164110734,\n \"acc_norm_stderr\": 0.0035056879433872927\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\": 0.7185185185185186,\n \"acc_stderr\": 0.038850042458002526,\n \"acc_norm\": 0.7185185185185186,\n \"acc_norm_stderr\": 0.038850042458002526\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8618421052631579,\n \"acc_stderr\": 0.028081042939576552,\n \"acc_norm\": 0.8618421052631579,\n \"acc_norm_stderr\": 0.028081042939576552\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\": 0.8226415094339623,\n \"acc_stderr\": 0.02350873921884694,\n \"acc_norm\": 0.8226415094339623,\n \"acc_norm_stderr\": 0.02350873921884694\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.02765610492929436\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\": 0.63,\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\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.033687629322594316,\n \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.033687629322594316\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\": 0.84,\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\": 0.7659574468085106,\n \"acc_stderr\": 0.02767845257821239,\n \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.02767845257821239\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n \"acc_norm_stderr\": 0.04615186962583707\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\": 0.6322751322751323,\n \"acc_stderr\": 0.02483383982556242,\n \"acc_norm\": 0.6322751322751323,\n \"acc_norm_stderr\": 0.02483383982556242\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8935483870967742,\n \"acc_stderr\": 0.01754510295165663,\n \"acc_norm\": 0.8935483870967742,\n \"acc_norm_stderr\": 0.01754510295165663\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\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781675,\n \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781675\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9191919191919192,\n \"acc_stderr\": 0.019417681889724536,\n \"acc_norm\": 0.9191919191919192,\n \"acc_norm_stderr\": 0.019417681889724536\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.010281417011909039,\n \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.010281417011909039\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8051282051282052,\n \"acc_stderr\": 0.020083167595181393,\n \"acc_norm\": 0.8051282051282052,\n \"acc_norm_stderr\": 0.020083167595181393\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.029958249250082114,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.029958249250082114\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\": 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248436,\n \"acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248436\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769574,\n \"acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769574\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293648,\n \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293648\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\": 0.9113924050632911,\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\": 0.8071748878923767,\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\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804468,\n \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804468\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.9090909090909091,\n \"acc_stderr\": 0.02624319405407388,\n \"acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.02624319405407388\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.02521232721050711,\n \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.02521232721050711\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\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761011,\n \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761011\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\": 0.03379976689896309,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9042145593869731,\n \"acc_stderr\": 0.01052403107905584,\n \"acc_norm\": 0.9042145593869731,\n \"acc_norm_stderr\": 0.01052403107905584\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\": 0.6558659217877095,\n \"acc_stderr\": 0.015889221313307094,\n \"acc_norm\": 0.6558659217877095,\n \"acc_norm_stderr\": 0.015889221313307094\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.869281045751634,\n \"acc_stderr\": 0.01930187362421527,\n \"acc_norm\": 0.869281045751634,\n \"acc_norm_stderr\": 0.01930187362421527\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.842443729903537,\n \"acc_stderr\": 0.020692237273583984,\n \"acc_norm\": 0.842443729903537,\n \"acc_norm_stderr\": 0.020692237273583984\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571853,\n \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571853\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.6099290780141844,\n \"acc_stderr\": 0.02909767559946393,\n \"acc_norm\": 0.6099290780141844,\n \"acc_norm_stderr\": 0.02909767559946393\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5990873533246415,\n \"acc_stderr\": 0.012516960350640814,\n \"acc_norm\": 0.5990873533246415,\n \"acc_norm_stderr\": 0.012516960350640814\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8198529411764706,\n \"acc_stderr\": 0.02334516361654484,\n \"acc_norm\": 0.8198529411764706,\n \"acc_norm_stderr\": 0.02334516361654484\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.015588643495370463,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.015588643495370463\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8163265306122449,\n \"acc_stderr\": 0.024789071332007646,\n \"acc_norm\": 0.8163265306122449,\n \"acc_norm_stderr\": 0.024789071332007646\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n \"acc_stderr\": 0.0201906705350279,\n \"acc_norm\": 0.9104477611940298,\n \"acc_norm_stderr\": 0.0201906705350279\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\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685516\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\": 0.3818849449204406,\n \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5364445120598228,\n \"mc2_stderr\": 0.014804162952722544\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8255722178374112,\n \"acc_stderr\": 0.010665187902498438\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6163760424564063,\n \"acc_stderr\": 0.013394238584938161\n }\n}\n```", "repo_url": "https://huggingface.co/01-ai/Yi-34B-200K", "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_05T03_41_41.478096", "path": ["**/details_harness|arc:challenge|25_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|gsm8k|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hellaswag|10_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-41-41.478096.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-41-41.478096.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["**/details_harness|winogrande|5_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-05T03-41-41.478096.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_05T03_41_41.478096", "path": ["results_2023-12-05T03-41-41.478096.parquet"]}, {"split": "latest", "path": ["results_2023-12-05T03-41-41.478096.parquet"]}]}]} | 2023-12-05T03:45:12+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of 01-ai/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 01-ai/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 2023-12-05T03:41:41.478096(note that their might be results for other tasks in 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
| [
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"## Latest results\n\nThese are the latest results from run 2023-12-05T03:41:41.478096(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model 01-ai/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 2023-12-05T03:41:41.478096(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of 01-ai/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 01-ai/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 2023-12-05T03:41:41.478096(note that their might be results for other tasks in 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"
] |
75097f42b5e159c2aadf2f35202fb4ec0da9edef |
# Dataset Card for Evaluation run of migtissera/Tess-M-Creative-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Tess-M-Creative-v1.0
- **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 [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) 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_migtissera__Tess-M-Creative-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T03:45:38.672992](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0/blob/main/results_2023-12-05T03-45-38.672992.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": 0.7506953369656723,
"acc_stderr": 0.028559826064592703,
"acc_norm": 0.755544561120704,
"acc_norm_stderr": 0.029096967565438774,
"mc1": 0.41982864137086906,
"mc1_stderr": 0.01727703030177577,
"mc2": 0.5768450076180885,
"mc2_stderr": 0.014925146586405758
},
"harness|arc:challenge|25": {
"acc": 0.6331058020477816,
"acc_stderr": 0.014084133118104296,
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}
```
### 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_migtissera__Tess-M-Creative-v1.0 | [
"region:us"
] | 2023-12-05T03:48:25+00:00 | {"pretty_name": "Evaluation run of migtissera/Tess-M-Creative-v1.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) 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_migtissera__Tess-M-Creative-v1.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T03:45:38.672992](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0/blob/main/results_2023-12-05T03-45-38.672992.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\": 0.7506953369656723,\n \"acc_stderr\": 0.028559826064592703,\n \"acc_norm\": 0.755544561120704,\n \"acc_norm_stderr\": 0.029096967565438774,\n \"mc1\": 0.41982864137086906,\n \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.5768450076180885,\n \"mc2_stderr\": 0.014925146586405758\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6331058020477816,\n \"acc_stderr\": 0.014084133118104296,\n \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.01376098820088053\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6496713802031467,\n \"acc_stderr\": 0.004760978203023324,\n \"acc_norm\": 0.8514240191196972,\n \"acc_norm_stderr\": 0.003549431247907371\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\": 0.875,\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\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\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\": 0.8888888888888888,\n \"acc_stderr\": 0.026280550932848062,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.026280550932848062\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 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#region-us
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# Dataset Card for Evaluation run of migtissera/Tess-M-Creative-v1.0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model migtissera/Tess-M-Creative-v1.0 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 2023-12-05T03:45:38.672992(note that their might be results for other tasks in 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
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83c368c41c88352cf932876699fb1fbbd17356ed | # Dataset Card for "librispeech960-encodec1024_asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cmu-mlsp/librispeech960-encodec1024_asr | [
"region:us"
] | 2023-12-05T03:58:58+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation_other", "path": "data/validation_other-*"}, {"split": "test_other", "path": "data/test_other-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio_codes", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1859401929, "num_examples": 281241}, {"name": "validation", "num_bytes": 10515210, "num_examples": 2703}, {"name": "test", "num_bytes": 10516648, "num_examples": 2620}, {"name": "validation_other", "num_bytes": 9974741, "num_examples": 2864}, {"name": "test_other", "num_bytes": 10389123, "num_examples": 2939}], "download_size": 0, "dataset_size": 1900797651}} | 2023-12-05T17:19:28+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "librispeech960-encodec1024_asr"
More Information needed | [
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8b3ab603f3556dc30844e7c36bef322ac916c862 | # Dataset Card for "Customizable-Code-Assistant-Data"
## Dataset Summary
This dataset contains is a dummy Version of the Customizable Code Assistant Dataset.
## Supported Tasks and Leaderboards
Customizable Code Assistant is a dataset for code completion. The task is to predict the next token in a code snippet. The dataset is designed to be customizable, so that it can be used for different programming languages and different code completion tasks.
[More Information Needed] | ammarnasr/Customizable-Code-Assistant-Data | [
"region:us"
] | 2023-12-05T04:17:57+00:00 | {"dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "repo_url", "dtype": "string"}, {"name": "repo_description", "dtype": "string"}, {"name": "repo_stars", "dtype": "int64"}, {"name": "repo_forks", "dtype": "int64"}, {"name": "repo_last_updated", "dtype": "string"}, {"name": "repo_created_at", "dtype": "string"}, {"name": "repo_size", "dtype": "int64"}, {"name": "repo_license", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "avg_line_length", "dtype": "float64"}, {"name": "max_line_length", "dtype": "int64"}, {"name": "alphnanum_fraction", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2004792, "num_examples": 604}], "download_size": 174531, "dataset_size": 2004792}} | 2023-12-05T04:21:57+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "Customizable-Code-Assistant-Data"
## Dataset Summary
This dataset contains is a dummy Version of the Customizable Code Assistant Dataset.
## Supported Tasks and Leaderboards
Customizable Code Assistant is a dataset for code completion. The task is to predict the next token in a code snippet. The dataset is designed to be customizable, so that it can be used for different programming languages and different code completion tasks.
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] |
75794087be02248e5d7e9c67b8f7a37d08e7e826 |
# Dataset Card for Evaluation run of JosephusCheung/Yee-34B-200K-Chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/JosephusCheung/Yee-34B-200K-Chat
- **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 [JosephusCheung/Yee-34B-200K-Chat](https://huggingface.co/JosephusCheung/Yee-34B-200K-Chat) 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_JosephusCheung__Yee-34B-200K-Chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T04:15:54.776905](https://huggingface.co/datasets/open-llm-leaderboard/details_JosephusCheung__Yee-34B-200K-Chat/blob/main/results_2023-12-05T04-15-54.776905.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": 0.7397087702526806,
"acc_stderr": 0.028697152379174293,
"acc_norm": 0.749145830773331,
"acc_norm_stderr": 0.029232668522838182,
"mc1": 0.379436964504284,
"mc1_stderr": 0.01698703926614299,
"mc2": 0.538842608150276,
"mc2_stderr": 0.015448158590971197
},
"harness|arc:challenge|25": {
"acc": 0.6254266211604096,
"acc_stderr": 0.014144193471893446,
"acc_norm": 0.6561433447098977,
"acc_norm_stderr": 0.013880644570156218
},
"harness|hellaswag|10": {
"acc": 0.6506671977693687,
"acc_stderr": 0.0047578490234119605,
"acc_norm": 0.8432583150766779,
"acc_norm_stderr": 0.003628140427399768
},
"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": 0.7333333333333333,
"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": 0.8301886792452831,
"acc_stderr": 0.023108393799841326,
"acc_norm": 0.8301886792452831,
"acc_norm_stderr": 0.023108393799841326
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.875,
"acc_stderr": 0.02765610492929436,
"acc_norm": 0.875,
"acc_norm_stderr": 0.02765610492929436
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939098
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.03533133389323657,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.03533133389323657
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.04940635630605659,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.04940635630605659
},
"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": 0.7617021276595745,
"acc_stderr": 0.027851252973889774,
"acc_norm": 0.7617021276595745,
"acc_norm_stderr": 0.027851252973889774
},
"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": 0.7517241379310344,
"acc_stderr": 0.03600105692727771,
"acc_norm": 0.7517241379310344,
"acc_norm_stderr": 0.03600105692727771
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.6375661375661376,
"acc_stderr": 0.024757473902752045,
"acc_norm": 0.6375661375661376,
"acc_norm_stderr": 0.024757473902752045
},
"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": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8612903225806452,
"acc_stderr": 0.019662961321414027,
"acc_norm": 0.8612903225806452,
"acc_norm_stderr": 0.019662961321414027
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6206896551724138,
"acc_stderr": 0.034139638059062345,
"acc_norm": 0.6206896551724138,
"acc_norm_stderr": 0.034139638059062345
},
"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": 0.8787878787878788,
"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": 0.9689119170984456,
"acc_stderr": 0.012525310625527046,
"acc_norm": 0.9689119170984456,
"acc_norm_stderr": 0.012525310625527046
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7794871794871795,
"acc_stderr": 0.0210206726808279,
"acc_norm": 0.7794871794871795,
"acc_norm_stderr": 0.0210206726808279
},
"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": 0.819327731092437,
"acc_stderr": 0.02499196496660077,
"acc_norm": 0.819327731092437,
"acc_norm_stderr": 0.02499196496660077
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.48344370860927155,
"acc_stderr": 0.0408024418562897,
"acc_norm": 0.48344370860927155,
"acc_norm_stderr": 0.0408024418562897
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9137614678899083,
"acc_stderr": 0.012035597300116245,
"acc_norm": 0.9137614678899083,
"acc_norm_stderr": 0.012035597300116245
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.625,
"acc_stderr": 0.033016908987210894,
"acc_norm": 0.625,
"acc_norm_stderr": 0.033016908987210894
},
"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": 0.8116591928251121,
"acc_stderr": 0.026241132996407256,
"acc_norm": 0.8116591928251121,
"acc_norm_stderr": 0.026241132996407256
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.9007633587786259,
"acc_stderr": 0.026222235171477374,
"acc_norm": 0.9007633587786259,
"acc_norm_stderr": 0.026222235171477374
},
"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": 0.8888888888888888,
"acc_stderr": 0.03038159675665167,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.03038159675665167
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8650306748466258,
"acc_stderr": 0.02684576505455386,
"acc_norm": 0.8650306748466258,
"acc_norm_stderr": 0.02684576505455386
},
"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": 0.8640776699029126,
"acc_stderr": 0.033932957297610096,
"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.033932957297610096
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9145299145299145,
"acc_stderr": 0.01831589168562586,
"acc_norm": 0.9145299145299145,
"acc_norm_stderr": 0.01831589168562586
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352203,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352203
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8978288633461047,
"acc_stderr": 0.010830724713134182,
"acc_norm": 0.8978288633461047,
"acc_norm_stderr": 0.010830724713134182
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8092485549132948,
"acc_stderr": 0.02115267696657528,
"acc_norm": 0.8092485549132948,
"acc_norm_stderr": 0.02115267696657528
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.7195530726256983,
"acc_stderr": 0.015024083883322895,
"acc_norm": 0.7195530726256983,
"acc_norm_stderr": 0.015024083883322895
},
"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": 0.8006430868167203,
"acc_stderr": 0.022691033780549656,
"acc_norm": 0.8006430868167203,
"acc_norm_stderr": 0.022691033780549656
},
"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": 0.6170212765957447,
"acc_stderr": 0.02899908090480618,
"acc_norm": 0.6170212765957447,
"acc_norm_stderr": 0.02899908090480618
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5560625814863103,
"acc_stderr": 0.012689708167787679,
"acc_norm": 0.5560625814863103,
"acc_norm_stderr": 0.012689708167787679
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8014705882352942,
"acc_stderr": 0.02423101337054109,
"acc_norm": 0.8014705882352942,
"acc_norm_stderr": 0.02423101337054109
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.8218954248366013,
"acc_stderr": 0.015478369653108568,
"acc_norm": 0.8218954248366013,
"acc_norm_stderr": 0.015478369653108568
},
"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": 0.8367346938775511,
"acc_stderr": 0.023661699177098615,
"acc_norm": 0.8367346938775511,
"acc_norm_stderr": 0.023661699177098615
},
"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": 0.88,
"acc_stderr": 0.032659863237109066,
"acc_norm": 0.88,
"acc_norm_stderr": 0.032659863237109066
},
"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": 0.8654970760233918,
"acc_stderr": 0.026168221344662297,
"acc_norm": 0.8654970760233918,
"acc_norm_stderr": 0.026168221344662297
},
"harness|truthfulqa:mc|0": {
"mc1": 0.379436964504284,
"mc1_stderr": 0.01698703926614299,
"mc2": 0.538842608150276,
"mc2_stderr": 0.015448158590971197
},
"harness|winogrande|5": {
"acc": 0.797947908445146,
"acc_stderr": 0.01128501375404745
},
"harness|gsm8k|5": {
"acc": 0.3479909021986353,
"acc_stderr": 0.013120581030382132
}
}
```
### 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_JosephusCheung__Yee-34B-200K-Chat | [
"region:us"
] | 2023-12-05T04:18:43+00:00 | {"pretty_name": "Evaluation run of JosephusCheung/Yee-34B-200K-Chat", "dataset_summary": "Dataset automatically created during the evaluation run of model [JosephusCheung/Yee-34B-200K-Chat](https://huggingface.co/JosephusCheung/Yee-34B-200K-Chat) 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_JosephusCheung__Yee-34B-200K-Chat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T04:15:54.776905](https://huggingface.co/datasets/open-llm-leaderboard/details_JosephusCheung__Yee-34B-200K-Chat/blob/main/results_2023-12-05T04-15-54.776905.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\": 0.7397087702526806,\n \"acc_stderr\": 0.028697152379174293,\n \"acc_norm\": 0.749145830773331,\n \"acc_norm_stderr\": 0.029232668522838182,\n \"mc1\": 0.379436964504284,\n \"mc1_stderr\": 0.01698703926614299,\n \"mc2\": 0.538842608150276,\n \"mc2_stderr\": 0.015448158590971197\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.014144193471893446,\n \"acc_norm\": 0.6561433447098977,\n \"acc_norm_stderr\": 0.013880644570156218\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6506671977693687,\n \"acc_stderr\": 0.0047578490234119605,\n \"acc_norm\": 0.8432583150766779,\n \"acc_norm_stderr\": 0.003628140427399768\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\": 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#region-us
|
# Dataset Card for Evaluation run of JosephusCheung/Yee-34B-200K-Chat
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model JosephusCheung/Yee-34B-200K-Chat 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 2023-12-05T04:15:54.776905(note that their might be results for other tasks in 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
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of JosephusCheung/Yee-34B-200K-Chat## 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 JosephusCheung/Yee-34B-200K-Chat 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 2023-12-05T04:15:54.776905(note that their might be results for other tasks in 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"
] |
9bcd0c4f14e99c7c672e143dd88ab9bb32c3627f | # Dataset Card for "kor_dbpedia_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
Lehmann, Jens, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann et al. "DBpedia–a large-scale, multilingual knowledge base extracted from Wikipedia." Semantic web 6, no. 2 (2015): 167-195.
``` | KETI-AIR/kor_dbpedia_14 | [
"license:cc-by-sa-3.0",
"region:us"
] | 2023-12-05T04:28:01+00:00 | {"license": "cc-by-sa-3.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "label", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 207331112, "num_examples": 560000}, {"name": "test", "num_bytes": 25970187, "num_examples": 70000}], "download_size": 136871622, "dataset_size": 233301299}} | 2023-12-05T04:29:58+00:00 | [] | [] | TAGS
#license-cc-by-sa-3.0 #region-us
| # Dataset Card for "kor_dbpedia_14"
More Information needed
# Source Data Citation Information
| [
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] |
4c986f8452a87f929b30179f9c525ca256d31662 | # Dataset Card for "MedQA_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hippocrates/MedQA_train | [
"region:us"
] | 2023-12-05T04:34:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28990738, "num_examples": 10178}, {"name": "valid", "num_bytes": 3622152, "num_examples": 1272}, {"name": "test", "num_bytes": 3678270, "num_examples": 1273}], "download_size": 14570611, "dataset_size": 36291160}} | 2023-12-05T20:29:07+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "MedQA_train"
More Information needed | [
"# Dataset Card for \"MedQA_train\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"MedQA_train\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"MedQA_train\"\n\nMore Information needed"
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22b0ec2662c73e7c34b3a8488367d2d87078320a | # Dataset Card for "MedMCQA_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hippocrates/MedMCQA_train | [
"region:us"
] | 2023-12-05T04:53:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 139904836, "num_examples": 182822}, {"name": "valid", "num_bytes": 3340728, "num_examples": 4183}, {"name": "test", "num_bytes": 3340728, "num_examples": 4183}], "download_size": 52413017, "dataset_size": 146586292}} | 2023-12-05T04:56:57+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "MedMCQA_train"
More Information needed | [
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] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"MedMCQA_train\"\n\nMore Information needed"
] | [
6,
16
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"MedMCQA_train\"\n\nMore Information needed"
] |
47939f8766f0db335072d012e9bddec02e26de42 |
# Dataset Card for Evaluation run of Enoch/llama-65b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Enoch/llama-65b-hf
- **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 [Enoch/llama-65b-hf](https://huggingface.co/Enoch/llama-65b-hf) 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_Enoch__llama-65b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T05:06:29.042599](https://huggingface.co/datasets/open-llm-leaderboard/details_Enoch__llama-65b-hf/blob/main/results_2023-12-05T05-06-29.042599.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": 0.6380517777268255,
"acc_stderr": 0.032178718879849834,
"acc_norm": 0.6421210460838432,
"acc_norm_stderr": 0.0328302725617492,
"mc1": 0.28518971848225216,
"mc1_stderr": 0.015805827874454892,
"mc2": 0.43425303494253065,
"mc2_stderr": 0.013768101142659904
},
"harness|arc:challenge|25": {
"acc": 0.5921501706484642,
"acc_stderr": 0.014361097288449708,
"acc_norm": 0.6331058020477816,
"acc_norm_stderr": 0.014084133118104298
},
"harness|hellaswag|10": {
"acc": 0.6650069707229636,
"acc_stderr": 0.004710234188047369,
"acc_norm": 0.8608842859988051,
"acc_norm_stderr": 0.003453599726736566
},
"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": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"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": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"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": 0.7013888888888888,
"acc_stderr": 0.03827052357950756,
"acc_norm": 0.7013888888888888,
"acc_norm_stderr": 0.03827052357950756
},
"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": {
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"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"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": 0.5491329479768786,
"acc_stderr": 0.03794012674697032,
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"acc_norm_stderr": 0.03794012674697032
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm_stderr": 0.04810840148082636
},
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},
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"acc_norm_stderr": 0.04137931034482757
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"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.0442626668137991
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"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.7483870967741936,
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.48268156424581005,
"acc_norm_stderr": 0.016712467441702517
},
"harness|hendrycksTest-nutrition|5": {
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"acc_stderr": 0.02664327847450875,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.02664327847450875
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"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.7266881028938906,
"acc_norm_stderr": 0.025311765975426125
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600713002
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"harness|hendrycksTest-professional_accounting|5": {
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"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
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"harness|hendrycksTest-professional_law|5": {
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},
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"acc_norm_stderr": 0.04172343038705383
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.028795185574291282,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.028795185574291282
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8009950248756219,
"acc_stderr": 0.028231365092758406,
"acc_norm": 0.8009950248756219,
"acc_norm_stderr": 0.028231365092758406
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"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": 0.28518971848225216,
"mc1_stderr": 0.015805827874454892,
"mc2": 0.43425303494253065,
"mc2_stderr": 0.013768101142659904
},
"harness|winogrande|5": {
"acc": 0.824782951854775,
"acc_stderr": 0.010684179227706175
},
"harness|gsm8k|5": {
"acc": 0.44806671721000757,
"acc_stderr": 0.013697992668274522
}
}
```
### 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_Enoch__llama-65b-hf | [
"region:us"
] | 2023-12-05T05:09:08+00:00 | {"pretty_name": "Evaluation run of Enoch/llama-65b-hf", "dataset_summary": "Dataset automatically created during the evaluation run of model [Enoch/llama-65b-hf](https://huggingface.co/Enoch/llama-65b-hf) 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_Enoch__llama-65b-hf\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T05:06:29.042599](https://huggingface.co/datasets/open-llm-leaderboard/details_Enoch__llama-65b-hf/blob/main/results_2023-12-05T05-06-29.042599.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\": 0.6380517777268255,\n \"acc_stderr\": 0.032178718879849834,\n \"acc_norm\": 0.6421210460838432,\n \"acc_norm_stderr\": 0.0328302725617492,\n \"mc1\": 0.28518971848225216,\n \"mc1_stderr\": 0.015805827874454892,\n \"mc2\": 0.43425303494253065,\n \"mc2_stderr\": 0.013768101142659904\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.014361097288449708,\n \"acc_norm\": 0.6331058020477816,\n \"acc_norm_stderr\": 0.014084133118104298\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6650069707229636,\n \"acc_stderr\": 0.004710234188047369,\n \"acc_norm\": 0.8608842859988051,\n \"acc_norm_stderr\": 0.003453599726736566\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\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\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\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\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\": 0.7013888888888888,\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\": 0.49,\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\": 0.46,\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\": 0.35,\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\": 0.5491329479768786,\n \"acc_stderr\": 0.03794012674697032,\n \"acc_norm\": 0.5491329479768786,\n \"acc_norm_stderr\": 0.03794012674697032\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\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\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n \"acc_stderr\": 0.045595221419582166,\n \"acc_norm\": 0.37719298245614036,\n \"acc_norm_stderr\": 0.045595221419582166\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\": 0.025279850397404904,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\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\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\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\": 0.41379310344827586,\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\": 0.68,\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\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483016,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483016\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\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121448,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121448\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633506,\n \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633506\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\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\": 0.37748344370860926,\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\": 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099864,\n \"acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099864\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.03324708911809117,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.03324708911809117\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8396624472573839,\n \"acc_stderr\": 0.02388438092596567,\n \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.02388438092596567\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n \"acc_norm_stderr\": 0.031708824268455\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\": 0.8181818181818182,\n \"acc_stderr\": 0.035208939510976534,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.035208939510976534\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n \"acc_stderr\": 0.04745033255489124,\n \"acc_norm\": 0.49107142857142855,\n \"acc_norm_stderr\": 0.04745033255489124\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\": 0.8717948717948718,\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\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n \"acc_stderr\": 0.01385372417092253,\n \"acc_norm\": 0.8160919540229885,\n \"acc_norm_stderr\": 0.01385372417092253\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258176,\n \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258176\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.48268156424581005,\n \"acc_stderr\": 0.016712467441702517,\n \"acc_norm\": 0.48268156424581005,\n \"acc_norm_stderr\": 0.016712467441702517\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.02664327847450875,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.02664327847450875\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n \"acc_stderr\": 0.025311765975426125,\n \"acc_norm\": 0.7266881028938906,\n \"acc_norm_stderr\": 0.025311765975426125\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713002,\n \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713002\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\": 0.4941329856584094,\n \"acc_stderr\": 0.012769356925216526,\n \"acc_norm\": 0.4941329856584094,\n \"acc_norm_stderr\": 0.012769356925216526\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6650326797385621,\n \"acc_stderr\": 0.01909422816700033,\n \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.01909422816700033\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.04172343038705383,\n \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.04172343038705383\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291282,\n \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291282\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8009950248756219,\n \"acc_stderr\": 0.028231365092758406,\n \"acc_norm\": 0.8009950248756219,\n \"acc_norm_stderr\": 0.028231365092758406\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\": 0.5421686746987951,\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\": 0.28518971848225216,\n \"mc1_stderr\": 0.015805827874454892,\n \"mc2\": 0.43425303494253065,\n \"mc2_stderr\": 0.013768101142659904\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706175\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44806671721000757,\n \"acc_stderr\": 0.013697992668274522\n }\n}\n```", "repo_url": "https://huggingface.co/Enoch/llama-65b-hf", "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_05T05_06_29.042599", "path": ["**/details_harness|arc:challenge|25_2023-12-05T05-06-29.042599.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-12-05T05-06-29.042599.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_05T05_06_29.042599", "path": ["**/details_harness|gsm8k|5_2023-12-05T05-06-29.042599.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-05T05-06-29.042599.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_05T05_06_29.042599", "path": ["**/details_harness|hellaswag|10_2023-12-05T05-06-29.042599.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-12-05T05-06-29.042599.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_05T05_06_29.042599", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T05-06-29.042599.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T05-06-29.042599.parquet", 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#region-us
|
# Dataset Card for Evaluation run of Enoch/llama-65b-hf
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Enoch/llama-65b-hf 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 2023-12-05T05:06:29.042599(note that their might be results for other tasks in 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
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Enoch/llama-65b-hf 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:",
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"### Social Impact of Dataset",
"### Discussion of Biases",
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"### Dataset Curators",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Enoch/llama-65b-hf 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 2023-12-05T05:06:29.042599(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Enoch/llama-65b-hf## 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 Enoch/llama-65b-hf 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 2023-12-05T05:06:29.042599(note that their might be results for other tasks in 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"
] |
85a6dbdd9b27fa2fe54d4a6c45e5cbbda28faa41 | # Dataset Card for "kor_glue"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
``` | KETI-AIR/kor_glue | [
"license:cc-by-4.0",
"region:us"
] | 2023-12-05T05:42:54+00:00 | {"license": "cc-by-4.0", "dataset_info": [{"config_name": "cola", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "label", "dtype": "int32"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 569511, "num_examples": 8551}, {"name": "validation", "num_bytes": 72661, "num_examples": 1043}, {"name": "test", "num_bytes": 72979, "num_examples": 1063}], "download_size": 381894, "dataset_size": 715151}, {"config_name": "mrpc", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int32"}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 1078522, "num_examples": 3668}, {"name": "validation", "num_bytes": 120306, "num_examples": 408}, {"name": "test", "num_bytes": 504069, "num_examples": 1725}], "download_size": 1176356, "dataset_size": 1702897}, {"config_name": "qnli", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "label", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28343211, "num_examples": 104743}, {"name": "validation", "num_bytes": 1507016, "num_examples": 5463}, {"name": "test", "num_bytes": 1510880, "num_examples": 5463}], "download_size": 21097078, "dataset_size": 31361107}, {"config_name": "qqp", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int32"}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 64564524, "num_examples": 363846}], "download_size": 40798086, "dataset_size": 64564524}, {"config_name": "wnli", "features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int32"}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 132171, "num_examples": 635}, {"name": "validation", "num_bytes": 15331, "num_examples": 71}, {"name": "test", "num_bytes": 47430, "num_examples": 146}], "download_size": 80151, "dataset_size": 194932}], "configs": [{"config_name": "cola", "data_files": [{"split": "train", "path": "cola/train-*"}, {"split": "validation", "path": "cola/validation-*"}, {"split": "test", "path": "cola/test-*"}]}, {"config_name": "mrpc", "data_files": [{"split": "train", "path": "mrpc/train-*"}, {"split": "validation", "path": "mrpc/validation-*"}, {"split": "test", "path": "mrpc/test-*"}]}, {"config_name": "qnli", "data_files": [{"split": "train", "path": "qnli/train-*"}, {"split": "validation", "path": "qnli/validation-*"}, {"split": "test", "path": "qnli/test-*"}]}, {"config_name": "qqp", "data_files": [{"split": "train", "path": "qqp/train-*"}]}, {"config_name": "wnli", "data_files": [{"split": "train", "path": "wnli/train-*"}, {"split": "validation", "path": "wnli/validation-*"}, {"split": "test", "path": "wnli/test-*"}]}]} | 2023-12-05T06:00:09+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| # Dataset Card for "kor_glue"
More Information needed
# Source Data Citation Information
| [
"# Dataset Card for \"kor_glue\"\n\nMore Information needed",
"# Source Data Citation Information"
] | [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# Dataset Card for \"kor_glue\"\n\nMore Information needed",
"# Source Data Citation Information"
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"passage: TAGS\n#license-cc-by-4.0 #region-us \n# Dataset Card for \"kor_glue\"\n\nMore Information needed# Source Data Citation Information"
] |
5e2444c1db6c05d4e5ea3a1cf29e5744f0abaa66 | # Dataset Card for "Nexusflow/Function_Call_Definitions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
| Nexusflow/Function_Call_Definitions | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-12-05T06:04:42+00:00 | {"license": "cc-by-nc-sa-4.0", "dataset_info": [{"config_name": "CVECPE", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8237, "num_examples": 2}], "download_size": 13384, "dataset_size": 8237}, {"config_name": "CVECPE_Multi (Nested)", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17425, "num_examples": 20}], "download_size": 15503, "dataset_size": 17425}, {"config_name": "Climate", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2905, "num_examples": 8}], "download_size": 4163, "dataset_size": 2905}, {"config_name": "OTX", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7040, "num_examples": 9}], "download_size": 8407, "dataset_size": 7040}, {"config_name": "Places", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2460, "num_examples": 7}], "download_size": 5759, "dataset_size": 2460}, {"config_name": "VT_Multi (Nested)", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18137, "num_examples": 29}], "download_size": 13810, "dataset_size": 18137}, {"config_name": "VT_Multi (Parallel)", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18137, "num_examples": 29}], "download_size": 13810, "dataset_size": 18137}, {"config_name": "VirusTotal", "features": [{"name": "function_calls", "dtype": "string"}, {"name": "descriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11501, "num_examples": 12}], "download_size": 11668, "dataset_size": 11501}], "configs": [{"config_name": "CVECPE", "data_files": [{"split": "train", "path": "CVECPE/train-*"}]}, {"config_name": "CVECPE_Multi (Nested)", "data_files": [{"split": "train", "path": "CVECPE_Multi (Nested)/train-*"}]}, {"config_name": "Climate", "data_files": [{"split": "train", "path": "Climate/train-*"}]}, {"config_name": "OTX", "data_files": [{"split": "train", "path": "OTX/train-*"}]}, {"config_name": "Places", "data_files": [{"split": "train", "path": "Places/train-*"}]}, {"config_name": "VT_Multi (Nested)", "data_files": [{"split": "train", "path": "VT_Multi (Nested)/train-*"}]}, {"config_name": "VT_Multi (Parallel)", "data_files": [{"split": "train", "path": "VT_Multi (Parallel)/train-*"}]}, {"config_name": "VirusTotal", "data_files": [{"split": "train", "path": "VirusTotal/train-*"}]}]} | 2023-12-05T07:08:39+00:00 | [] | [] | TAGS
#license-cc-by-nc-sa-4.0 #region-us
| # Dataset Card for "Nexusflow/Function_Call_Definitions"
More Information needed
| [
"# Dataset Card for \"Nexusflow/Function_Call_Definitions\"\n\nMore Information needed"
] | [
"TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n",
"# Dataset Card for \"Nexusflow/Function_Call_Definitions\"\n\nMore Information needed"
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"passage: TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n# Dataset Card for \"Nexusflow/Function_Call_Definitions\"\n\nMore Information needed"
] |
cdf75c523903a6429506c1adbabb28175979f112 |
# Dataset Card for Evaluation run of deepseek-ai/deepseek-llm-67b-chat
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) 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_deepseek-ai__deepseek-llm-67b-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-20T05:32:04.370506](https://huggingface.co/datasets/open-llm-leaderboard/details_deepseek-ai__deepseek-llm-67b-chat/blob/main/results_2024-01-20T05-32-04.370506.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
{
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"mc2_stderr": 0.014945999339089985
},
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},
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"acc_norm_stderr": 0.04461272175910508
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7755102040816326,
"acc_stderr": 0.0267114305555384,
"acc_norm": 0.7755102040816326,
"acc_norm_stderr": 0.0267114305555384
},
"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": 0.5662650602409639,
"acc_stderr": 0.03858158940685516,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685516
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061456,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061456
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3953488372093023,
"mc1_stderr": 0.017115815632418194,
"mc2": 0.5583209009287327,
"mc2_stderr": 0.014945999339089985
},
"harness|winogrande|5": {
"acc": 0.8421468034727704,
"acc_stderr": 0.010247165248719764
},
"harness|gsm8k|5": {
"acc": 0.623199393479909,
"acc_stderr": 0.013347858757829154
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
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### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_deepseek-ai__deepseek-llm-67b-chat | [
"region:us"
] | 2023-12-05T06:09:20+00:00 | {"pretty_name": "Evaluation run of deepseek-ai/deepseek-llm-67b-chat", "dataset_summary": "Dataset automatically created during the evaluation run of model [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) 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_deepseek-ai__deepseek-llm-67b-chat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-20T05:32:04.370506](https://huggingface.co/datasets/open-llm-leaderboard/details_deepseek-ai__deepseek-llm-67b-chat/blob/main/results_2024-01-20T05-32-04.370506.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\": 0.7202833490892042,\n \"acc_stderr\": 0.029579907486427835,\n \"acc_norm\": 0.7235978318716265,\n \"acc_norm_stderr\": 0.030155588132811505,\n \"mc1\": 0.3953488372093023,\n \"mc1_stderr\": 0.017115815632418194,\n \"mc2\": 0.5583209009287327,\n \"mc2_stderr\": 0.014945999339089985\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6450511945392492,\n \"acc_stderr\": 0.013983036904094083,\n \"acc_norm\": 0.6774744027303754,\n \"acc_norm_stderr\": 0.013659980894277371\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6800438159729137,\n \"acc_stderr\": 0.004655059308602615,\n \"acc_norm\": 0.8679545907189803,\n \"acc_norm_stderr\": 0.0033784824887488673\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237103,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237103\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\": 0.8092105263157895,\n \"acc_stderr\": 0.031975658210324984,\n \"acc_norm\": 0.8092105263157895,\n \"acc_norm_stderr\": 0.031975658210324984\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02461829819586651,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02461829819586651\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.031164899666948617,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.031164899666948617\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.034765996075164785,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.034765996075164785\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\n },\n \"harness|hendrycksTest-computer_security|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-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\": 0.5526315789473685,\n \"acc_stderr\": 0.046774730044912,\n \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.046774730044912\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03960933549451207,\n \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03960933549451207\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.5291005291005291,\n \"acc_stderr\": 0.025707658614154947,\n \"acc_norm\": 0.5291005291005291,\n \"acc_norm_stderr\": 0.025707658614154947\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5158730158730159,\n \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.8225806451612904,\n \"acc_stderr\": 0.021732540689329286,\n \"acc_norm\": 0.8225806451612904,\n \"acc_norm_stderr\": 0.021732540689329286\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6059113300492611,\n \"acc_stderr\": 0.03438157967036543,\n \"acc_norm\": 0.6059113300492611,\n \"acc_norm_stderr\": 0.03438157967036543\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066584,\n \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066584\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9090909090909091,\n \"acc_stderr\": 0.020482086775424218,\n \"acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.020482086775424218\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295141,\n \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295141\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7282051282051282,\n \"acc_stderr\": 0.022556551010132354,\n \"acc_norm\": 0.7282051282051282,\n \"acc_norm_stderr\": 0.022556551010132354\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8151260504201681,\n \"acc_stderr\": 0.025215992877954202,\n \"acc_norm\": 0.8151260504201681,\n \"acc_norm_stderr\": 0.025215992877954202\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.45695364238410596,\n \"acc_stderr\": 0.04067325174247443,\n \"acc_norm\": 0.45695364238410596,\n \"acc_norm_stderr\": 0.04067325174247443\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9009174311926605,\n \"acc_stderr\": 0.012809780081878929,\n \"acc_norm\": 0.9009174311926605,\n \"acc_norm_stderr\": 0.012809780081878929\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6527777777777778,\n \"acc_stderr\": 0.032468872436376486,\n \"acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.032468872436376486\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9071729957805907,\n \"acc_stderr\": 0.018889750550956715,\n \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.018889750550956715\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.820627802690583,\n \"acc_stderr\": 0.0257498195691928,\n \"acc_norm\": 0.820627802690583,\n \"acc_norm_stderr\": 0.0257498195691928\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\": 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.03247224389917949,\n \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.03247224389917949\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8282208588957055,\n \"acc_stderr\": 0.029634717272371037,\n \"acc_norm\": 0.8282208588957055,\n \"acc_norm_stderr\": 0.029634717272371037\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\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331366,\n \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331366\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9230769230769231,\n \"acc_stderr\": 0.017456987872436186,\n \"acc_norm\": 0.9230769230769231,\n \"acc_norm_stderr\": 0.017456987872436186\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\": 0.9080459770114943,\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\": 0.7861271676300579,\n \"acc_stderr\": 0.022075709251757177,\n \"acc_norm\": 0.7861271676300579,\n \"acc_norm_stderr\": 0.022075709251757177\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.48044692737430167,\n \"acc_stderr\": 0.016709709877661995,\n \"acc_norm\": 0.48044692737430167,\n \"acc_norm_stderr\": 0.016709709877661995\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.0231527224394023,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.0231527224394023\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8038585209003215,\n \"acc_stderr\": 0.02255244778047803,\n \"acc_norm\": 0.8038585209003215,\n \"acc_norm_stderr\": 0.02255244778047803\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8611111111111112,\n \"acc_stderr\": 0.019242526226544536,\n \"acc_norm\": 0.8611111111111112,\n \"acc_norm_stderr\": 0.019242526226544536\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.02949482760014437,\n \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.02949482760014437\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5834419817470665,\n \"acc_stderr\": 0.01259115324505739,\n \"acc_norm\": 0.5834419817470665,\n \"acc_norm_stderr\": 0.01259115324505739\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02518778666022726,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02518778666022726\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8104575163398693,\n \"acc_stderr\": 0.015856152189980245,\n \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.015856152189980245\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.04461272175910508,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.04461272175910508\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7755102040816326,\n \"acc_stderr\": 0.0267114305555384,\n \"acc_norm\": 0.7755102040816326,\n \"acc_norm_stderr\": 0.0267114305555384\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.93,\n \"acc_stderr\": 0.025643239997624294,\n \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.025643239997624294\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 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["results_2023-12-05T06-06-20.627396.parquet"]}, {"split": "2024_01_20T05_32_04.370506", "path": ["results_2024-01-20T05-32-04.370506.parquet"]}, {"split": "latest", "path": ["results_2024-01-20T05-32-04.370506.parquet"]}]}]} | 2024-01-20T05:34:30+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of deepseek-ai/deepseek-llm-67b-chat
Dataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-67b-chat 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 2024-01-20T05:32:04.370506(note that their might be results for other tasks in 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 deepseek-ai/deepseek-llm-67b-chat\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-67b-chat 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 2024-01-20T05:32:04.370506(note that their might be results for other tasks in 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 deepseek-ai/deepseek-llm-67b-chat\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-67b-chat 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 2024-01-20T05:32:04.370506(note that their might be results for other tasks in 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"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of deepseek-ai/deepseek-llm-67b-chat\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-67b-chat 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 2024-01-20T05:32:04.370506(note that their might be results for other tasks in 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]"
] |
cf666de8b31a569e9dea63c716b0c145d763df99 | # Dataset Card for "kor_hellaswag"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}
``` | KETI-AIR/kor_hellaswag | [
"license:mit",
"region:us"
] | 2023-12-05T06:22:14+00:00 | {"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "ind", "dtype": "int32"}, {"name": "activity_label", "dtype": "string"}, {"name": "ctx_a", "dtype": "string"}, {"name": "ctx_b", "dtype": "string"}, {"name": "ctx", "dtype": "string"}, {"name": "endings", "sequence": "string"}, {"name": "source_id", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "split_type", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "joined", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 105739666, "num_examples": 39905}, {"name": "validation", "num_bytes": 27367976, "num_examples": 10042}, {"name": "test", "num_bytes": 26340397, "num_examples": 10003}], "download_size": 69994643, "dataset_size": 159448039}} | 2023-12-05T06:23:43+00:00 | [] | [] | TAGS
#license-mit #region-us
| # Dataset Card for "kor_hellaswag"
More Information needed
# Source Data Citation Information
| [
"# Dataset Card for \"kor_hellaswag\"\n\nMore Information needed",
"# Source Data Citation Information"
] | [
"TAGS\n#license-mit #region-us \n",
"# Dataset Card for \"kor_hellaswag\"\n\nMore Information needed",
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"passage: TAGS\n#license-mit #region-us \n# Dataset Card for \"kor_hellaswag\"\n\nMore Information needed# Source Data Citation Information"
] |
a8d62094a65ca55e32ed3c9e8bbe16db67ec46f7 | # Dataset Card for "librispeech960-wavlm-large-km1000_asr_tokenized_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cmu-mlsp/librispeech960-wavlm-large-km1000_asr_tokenized_final | [
"region:us"
] | 2023-12-05T06:28:08+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "validation_tts", "path": "data/validation_tts-*"}, {"split": "test", "path": "data/test-*"}, {"split": "test_tts", "path": "data/test_tts-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4809631893, "num_examples": 281241}, {"name": "validation", "num_bytes": 54319982, "num_examples": 5406}, {"name": "validation_tts", "num_bytes": 27159991, "num_examples": 2703}, {"name": "test", "num_bytes": 27180211, "num_examples": 2620}, {"name": "test_tts", "num_bytes": 27180211, "num_examples": 2620}], "download_size": 506035712, "dataset_size": 4945472288}} | 2023-12-05T14:33:35+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "librispeech960-wavlm-large-km1000_asr_tokenized_final"
More Information needed | [
"# Dataset Card for \"librispeech960-wavlm-large-km1000_asr_tokenized_final\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"librispeech960-wavlm-large-km1000_asr_tokenized_final\"\n\nMore Information needed"
] | [
6,
33
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"librispeech960-wavlm-large-km1000_asr_tokenized_final\"\n\nMore Information needed"
] |
2cb9dc80ba0f4394ff1e9e7d72c82068cdbf4f88 | # Dataset Card for "vietnamese-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tiennv/vietnamese-corpus | [
"region:us"
] | 2023-12-05T06:34:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8142342251, "num_examples": 19233991}], "download_size": 4233458271, "dataset_size": 8142342251}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T06:59:38+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "vietnamese-corpus"
More Information needed | [
"# Dataset Card for \"vietnamese-corpus\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"vietnamese-corpus\"\n\nMore Information needed"
] | [
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16
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"passage: TAGS\n#region-us \n# Dataset Card for \"vietnamese-corpus\"\n\nMore Information needed"
] |
ec6ae40e2a472b0585044c035f3846561afbce22 | # Dataset Card for "kor_nq_open"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@article{doi:10.1162/tacl\_a\_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
title = {Natural Questions: A Benchmark for Question Answering Research},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {453-466},
year = {2019},
doi = {10.1162/tacl\_a\_00276},
URL = {
https://doi.org/10.1162/tacl_a_00276
},
eprint = {
https://doi.org/10.1162/tacl_a_00276
},
abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. }
}
@inproceedings{lee-etal-2019-latent,
title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering",
author = "Lee, Kenton and
Chang, Ming-Wei and
Toutanova, Kristina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1612",
doi = "10.18653/v1/P19-1612",
pages = "6086--6096",
abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.",
}
``` | KETI-AIR/kor_nq_open | [
"license:cc-by-sa-3.0",
"region:us"
] | 2023-12-05T06:35:09+00:00 | {"license": "cc-by-sa-3.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "answer", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 8520218, "num_examples": 87925}, {"name": "validation", "num_bytes": 394518, "num_examples": 3610}], "download_size": 5925491, "dataset_size": 8914736}} | 2023-12-05T06:39:46+00:00 | [] | [] | TAGS
#license-cc-by-sa-3.0 #region-us
| # Dataset Card for "kor_nq_open"
More Information needed
# Source Data Citation Information
| [
"# Dataset Card for \"kor_nq_open\"\n\nMore Information needed",
"# Source Data Citation Information"
] | [
"TAGS\n#license-cc-by-sa-3.0 #region-us \n",
"# Dataset Card for \"kor_nq_open\"\n\nMore Information needed",
"# Source Data Citation Information"
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] | [
"passage: TAGS\n#license-cc-by-sa-3.0 #region-us \n# Dataset Card for \"kor_nq_open\"\n\nMore Information needed# Source Data Citation Information"
] |
628df753856d6e409a470753253a43d45852ea33 | # Dataset Card for "fin_instruct_dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gagan3012/fin_instruct_dpo | [
"region:us"
] | 2023-12-05T06:51:29+00:00 | {"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 135251054.04366517, "num_examples": 42601}, {"name": "test", "num_bytes": 1368352.9563348205, "num_examples": 431}], "download_size": 76818308, "dataset_size": 136619407.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-12-05T09:27:13+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "fin_instruct_dpo"
More Information needed | [
"# Dataset Card for \"fin_instruct_dpo\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"fin_instruct_dpo\"\n\nMore Information needed"
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044ef56a1f779f1709dc742f4fbce6de8e943921 | ## Data
This datset is uploaded as a .tar.gz file that was orginally used to finetune a stable diffusion model.
It consists of 11 Renaissance era portraits of human figures whom are often rendered in dynamic poses, showing expression amd possibly using gesture.
Renaissance portraits are characterized by realism, with the subject being the focus of the work and the background being plain.
Additionally, the file includes a .csv file with two columns, one that serves as a placeholder for an image path and the other for textual description used in training the model.
Image Format: .jpg <br>
Image Size: 256 x 256px | morj/renaissance_portraits | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"art",
"renaissance",
"finetune",
"doi:10.57967/hf/1427",
"region:us"
] | 2023-12-05T07:07:19+00:00 | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "pretty_name": "renaissance_portraits", "tags": ["art", "renaissance", "finetune"]} | 2024-02-05T01:13:43+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-to-image #size_categories-n<1K #language-English #license-cc-by-nc-sa-4.0 #art #renaissance #finetune #doi-10.57967/hf/1427 #region-us
| ## Data
This datset is uploaded as a .URL file that was orginally used to finetune a stable diffusion model.
It consists of 11 Renaissance era portraits of human figures whom are often rendered in dynamic poses, showing expression amd possibly using gesture.
Renaissance portraits are characterized by realism, with the subject being the focus of the work and the background being plain.
Additionally, the file includes a .csv file with two columns, one that serves as a placeholder for an image path and the other for textual description used in training the model.
Image Format: .jpg <br>
Image Size: 256 x 256px | [
"## Data\nThis datset is uploaded as a .URL file that was orginally used to finetune a stable diffusion model.\nIt consists of 11 Renaissance era portraits of human figures whom are often rendered in dynamic poses, showing expression amd possibly using gesture. \nRenaissance portraits are characterized by realism, with the subject being the focus of the work and the background being plain.\nAdditionally, the file includes a .csv file with two columns, one that serves as a placeholder for an image path and the other for textual description used in training the model.\n\nImage Format: .jpg <br> \nImage Size: 256 x 256px"
] | [
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"## Data\nThis datset is uploaded as a .URL file that was orginally used to finetune a stable diffusion model.\nIt consists of 11 Renaissance era portraits of human figures whom are often rendered in dynamic poses, showing expression amd possibly using gesture. \nRenaissance portraits are characterized by realism, with the subject being the focus of the work and the background being plain.\nAdditionally, the file includes a .csv file with two columns, one that serves as a placeholder for an image path and the other for textual description used in training the model.\n\nImage Format: .jpg <br> \nImage Size: 256 x 256px"
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] |
3c9f67b2197f79651efe63210dace2d48cc8e026 | # Dataset Card for "kor_piqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@inproceedings{Bisk2020,
author = {Yonatan Bisk and Rowan Zellers and
Ronan Le Bras and Jianfeng Gao
and Yejin Choi},
title = {PIQA: Reasoning about Physical Commonsense in
Natural Language},
booktitle = {Thirty-Fourth AAAI Conference on
Artificial Intelligence},
year = {2020},
}
``` | KETI-AIR/kor_piqa | [
"license:afl-3.0",
"region:us"
] | 2023-12-05T07:23:15+00:00 | {"license": "afl-3.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "goal", "dtype": "string"}, {"name": "sol1", "dtype": "string"}, {"name": "sol2", "dtype": "string"}, {"name": "label", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 5065072, "num_examples": 16113}, {"name": "validation", "num_bytes": 563144, "num_examples": 1838}, {"name": "test", "num_bytes": 935859, "num_examples": 3084}], "download_size": 3857117, "dataset_size": 6564075}} | 2023-12-05T07:27:32+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| # Dataset Card for "kor_piqa"
More Information needed
# Source Data Citation Information
| [
"# Dataset Card for \"kor_piqa\"\n\nMore Information needed",
"# Source Data Citation Information"
] | [
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d41039c93cafef6ac93fbda58770455e1bc17697 | https://huggingface.co/datasets/maywell/ko_Ultrafeedback_binarized 에서 번역 작업 오류로 잘못 번역된 레코드만 삭제함.<br><br>
예) "두 사람 간의 대화가 주어집니다.'Person1:'과 'Person2:'는 각자의 대화를 구분하는 데 사용됩니다. 대화에 2개 이상의 고유 감정이 존재하는지 분류해야 합니다. 대화에 2개 이상의 고유 감정이 존재하면 출력은 '1'로 분류되고, 그렇지 않으면 '0'으로 분류해야 합니다.예시 입력:Person1: 안녕하세요, 마이크. 뭐 물어볼 게 있어요? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슈"
| hankang2023/Ultrafeedback_binarized.ko.maywell-mini | [
"region:us"
] | 2023-12-05T07:44:43+00:00 | {} | 2023-12-06T00:22:47+00:00 | [] | [] | TAGS
#region-us
| URL 에서 번역 작업 오류로 잘못 번역된 레코드만 삭제함.<br><br>
예) "두 사람 간의 대화가 주어집니다.'Person1:'과 'Person2:'는 각자의 대화를 구분하는 데 사용됩니다. 대화에 2개 이상의 고유 감정이 존재하는지 분류해야 합니다. 대화에 2개 이상의 고유 감정이 존재하면 출력은 '1'로 분류되고, 그렇지 않으면 '0'으로 분류해야 합니다.예시 입력:Person1: 안녕하세요, 마이크. 뭐 물어볼 게 있어요? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슨 일이야? 그럼 무슈"
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
18eba943372da841656b8044eeccf94c7f64be31 |
# Sycophancy Rotten Tomatoes Dataset
The generated dataset includes a text (chat between a human and an assistant), the sycophancy of the exchange, and additional information.
### Dataset Structure
The dataset is structured as follows:
- `text`: The generated prompt text of the chat between the human and the assistant.
- `assistant_opinion`: The assistant's opinion, converted to a label (i.e. its final answer.
- `human_opinion`: The human's opinion, converted to a label.
- `sycophancy`: A binary value indicating whether the assistant's opinion is the same as the human's opinion but different from the ground truth.
- `comment`: The initial comment from Rotten Tomatoes.
- `ground_truth`: The actual label of the initial comment.
- `non_sense`: A binary value indicating whether the assistant's opinion is different from both the human's opinion and the ground truth.
> The `non_sense` column reports instances where the assistant provides an answer that differs from the ground truth, even though the human has given their opinion that matches the correct label. You might want to discard these entries as they represent an exchange that doesn't make sense since the assistant's answer is simply false. | romaingrx/sycophancy_rotten_tomatoes | [
"task_categories:zero-shot-classification",
"task_categories:text-classification",
"language:en",
"license:openrail",
"region:us"
] | 2023-12-05T07:48:06+00:00 | {"language": ["en"], "license": "openrail", "task_categories": ["zero-shot-classification", "text-classification"]} | 2023-12-05T17:55:10+00:00 | [] | [
"en"
] | TAGS
#task_categories-zero-shot-classification #task_categories-text-classification #language-English #license-openrail #region-us
|
# Sycophancy Rotten Tomatoes Dataset
The generated dataset includes a text (chat between a human and an assistant), the sycophancy of the exchange, and additional information.
### Dataset Structure
The dataset is structured as follows:
- 'text': The generated prompt text of the chat between the human and the assistant.
- 'assistant_opinion': The assistant's opinion, converted to a label (i.e. its final answer.
- 'human_opinion': The human's opinion, converted to a label.
- 'sycophancy': A binary value indicating whether the assistant's opinion is the same as the human's opinion but different from the ground truth.
- 'comment': The initial comment from Rotten Tomatoes.
- 'ground_truth': The actual label of the initial comment.
- 'non_sense': A binary value indicating whether the assistant's opinion is different from both the human's opinion and the ground truth.
> The 'non_sense' column reports instances where the assistant provides an answer that differs from the ground truth, even though the human has given their opinion that matches the correct label. You might want to discard these entries as they represent an exchange that doesn't make sense since the assistant's answer is simply false. | [
"# Sycophancy Rotten Tomatoes Dataset\n\nThe generated dataset includes a text (chat between a human and an assistant), the sycophancy of the exchange, and additional information.",
"### Dataset Structure\n\nThe dataset is structured as follows:\n\n- 'text': The generated prompt text of the chat between the human and the assistant.\n- 'assistant_opinion': The assistant's opinion, converted to a label (i.e. its final answer.\n- 'human_opinion': The human's opinion, converted to a label.\n- 'sycophancy': A binary value indicating whether the assistant's opinion is the same as the human's opinion but different from the ground truth.\n- 'comment': The initial comment from Rotten Tomatoes.\n- 'ground_truth': The actual label of the initial comment.\n- 'non_sense': A binary value indicating whether the assistant's opinion is different from both the human's opinion and the ground truth.\n \n\n> The 'non_sense' column reports instances where the assistant provides an answer that differs from the ground truth, even though the human has given their opinion that matches the correct label. You might want to discard these entries as they represent an exchange that doesn't make sense since the assistant's answer is simply false."
] | [
"TAGS\n#task_categories-zero-shot-classification #task_categories-text-classification #language-English #license-openrail #region-us \n",
"# Sycophancy Rotten Tomatoes Dataset\n\nThe generated dataset includes a text (chat between a human and an assistant), the sycophancy of the exchange, and additional information.",
"### Dataset Structure\n\nThe dataset is structured as follows:\n\n- 'text': The generated prompt text of the chat between the human and the assistant.\n- 'assistant_opinion': The assistant's opinion, converted to a label (i.e. its final answer.\n- 'human_opinion': The human's opinion, converted to a label.\n- 'sycophancy': A binary value indicating whether the assistant's opinion is the same as the human's opinion but different from the ground truth.\n- 'comment': The initial comment from Rotten Tomatoes.\n- 'ground_truth': The actual label of the initial comment.\n- 'non_sense': A binary value indicating whether the assistant's opinion is different from both the human's opinion and the ground truth.\n \n\n> The 'non_sense' column reports instances where the assistant provides an answer that differs from the ground truth, even though the human has given their opinion that matches the correct label. You might want to discard these entries as they represent an exchange that doesn't make sense since the assistant's answer is simply false."
] | [
40,
42,
257
] | [
"passage: TAGS\n#task_categories-zero-shot-classification #task_categories-text-classification #language-English #license-openrail #region-us \n# Sycophancy Rotten Tomatoes Dataset\n\nThe generated dataset includes a text (chat between a human and an assistant), the sycophancy of the exchange, and additional information.### Dataset Structure\n\nThe dataset is structured as follows:\n\n- 'text': The generated prompt text of the chat between the human and the assistant.\n- 'assistant_opinion': The assistant's opinion, converted to a label (i.e. its final answer.\n- 'human_opinion': The human's opinion, converted to a label.\n- 'sycophancy': A binary value indicating whether the assistant's opinion is the same as the human's opinion but different from the ground truth.\n- 'comment': The initial comment from Rotten Tomatoes.\n- 'ground_truth': The actual label of the initial comment.\n- 'non_sense': A binary value indicating whether the assistant's opinion is different from both the human's opinion and the ground truth.\n \n\n> The 'non_sense' column reports instances where the assistant provides an answer that differs from the ground truth, even though the human has given their opinion that matches the correct label. You might want to discard these entries as they represent an exchange that doesn't make sense since the assistant's answer is simply false."
] |
dc2592fa912c7144b1b2f1aea6b9ac1a869f5900 | # Dataset Card for "thai_sample_200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | wannaphong/thai_sample_200k | [
"region:us"
] | 2023-12-05T07:51:20+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1156975584, "num_examples": 200000}], "download_size": 453690863, "dataset_size": 1156975584}} | 2023-12-05T07:53:18+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "thai_sample_200k"
More Information needed | [
"# Dataset Card for \"thai_sample_200k\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"thai_sample_200k\"\n\nMore Information needed"
] | [
6,
17
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"thai_sample_200k\"\n\nMore Information needed"
] |
be223ef972e71eec45cf4c21bffe5685e67c6da7 |
## 食品安全领域指令微调数据
* 包含两个任务：多文档QA、论文QA
* 文档数据来自中国食品安全国标、教材、综述论文 | yuyijiong/FoodSafe-Doc-QA-Chinese | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-12-05T08:02:16+00:00 | {"language": ["zh"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"]} | 2023-12-05T09:05:29+00:00 | [] | [
"zh"
] | TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-cc-by-nc-4.0 #region-us
|
## 食品安全领域指令微调数据
* 包含两个任务：多文档QA、论文QA
* 文档数据来自中国食品安全国标、教材、综述论文 | [
"## 食品安全领域指令微调数据\n* 包含两个任务：多文档QA、论文QA\n* 文档数据来自中国食品安全国标、教材、综述论文"
] | [
"TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-cc-by-nc-4.0 #region-us \n",
"## 食品安全领域指令微调数据\n* 包含两个任务：多文档QA、论文QA\n* 文档数据来自中国食品安全国标、教材、综述论文"
] | [
45,
37
] | [
"passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-cc-by-nc-4.0 #region-us \n## 食品安全领域指令微调数据\n* 包含两个任务：多文档QA、论文QA\n* 文档数据来自中国食品安全国标、教材、综述论文"
] |
cfab73689709802342534d140fd816a6bcc4cbcb | This dataset is for the "DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs" paper.
@article{liang2023drugchat,
title={DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs},
author={Liang, Youwei and Zhang, Ruiyi and Zhang, li and Xie, Pengtao},
journal={TechRxiv},
year={2023}
}
There are no changes in data; only added datasets class to download the set from HF and generate split from data in .json files. | avaliev/drugchat | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:bsd-3-clause",
"biology",
"chemistry",
"medical",
"region:us"
] | 2023-12-05T08:04:57+00:00 | {"language": ["en"], "license": "bsd-3-clause", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "DrugChat Dataset", "tags": ["biology", "chemistry", "medical"]} | 2023-12-06T11:00:15+00:00 | [] | [
"en"
] | TAGS
#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-bsd-3-clause #biology #chemistry #medical #region-us
| This dataset is for the "DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs" paper.
@article{liang2023drugchat,
title={DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs},
author={Liang, Youwei and Zhang, Ruiyi and Zhang, li and Xie, Pengtao},
journal={TechRxiv},
year={2023}
}
There are no changes in data; only added datasets class to download the set from HF and generate split from data in .json files. | [] | [
"TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-bsd-3-clause #biology #chemistry #medical #region-us \n"
] | [
54
] | [
"passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-bsd-3-clause #biology #chemistry #medical #region-us \n"
] |
53b33d247947b14eaa62dbcd661a2b20c7ff1604 | # Dataset Card for "thai_sample_500k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | wannaphong/thai_sample_500k | [
"region:us"
] | 2023-12-05T08:06:26+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2878877988, "num_examples": 500000}], "download_size": 1128997330, "dataset_size": 2878877988}} | 2023-12-05T08:09:33+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "thai_sample_500k"
More Information needed | [
"# Dataset Card for \"thai_sample_500k\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"thai_sample_500k\"\n\nMore Information needed"
] | [
6,
17
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"thai_sample_500k\"\n\nMore Information needed"
] |
e9d5152c4446d984e71ab960f7844cbd6575da6b | # Dataset Card for "kor_quail"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@inproceedings{DBLP:conf/aaai/RogersKDR20,
author = {Anna Rogers and
Olga Kovaleva and
Matthew Downey and
Anna Rumshisky},
title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
Real Tasks},
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
pages = {8722--8731},
publisher = {{AAAI} Press},
year = {2020},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6398},
timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
biburl = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | KETI-AIR/kor_quail | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-12-05T08:11:03+00:00 | {"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "challenge", "path": "data/challenge-*"}]}], "dataset_info": {"features": [{"name": "data_index_by_user", "dtype": "int32"}, {"name": "id", "dtype": "string"}, {"name": "context_id", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "author", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "correct_answer_id", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 27612173, "num_examples": 10246}, {"name": "validation", "num_bytes": 5860893, "num_examples": 2164}, {"name": "challenge", "num_bytes": 1451663, "num_examples": 556}], "download_size": 2671154, "dataset_size": 34924729}} | 2023-12-05T08:19:44+00:00 | [] | [] | TAGS
#license-cc-by-nc-sa-4.0 #region-us
| # Dataset Card for "kor_quail"
More Information needed
# Source Data Citation Information
| [
"# Dataset Card for \"kor_quail\"\n\nMore Information needed",
"# Source Data Citation Information"
] | [
"TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n",
"# Dataset Card for \"kor_quail\"\n\nMore Information needed",
"# Source Data Citation Information"
] | [
19,
14,
6
] | [
"passage: TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n# Dataset Card for \"kor_quail\"\n\nMore Information needed# Source Data Citation Information"
] |
f26b00ef528914415cbc2013180ce52ec0831fc2 |
Converting newsqa dataset to identical format as lmqg/qag_squad for asahi417/lm-question-generation
[GitHub Repo](https://github.com/gabrieltorresgamez/newsqa) | StellarMilk/newsqa | [
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | 2023-12-05T09:01:30+00:00 | {"language": ["en"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "newsqa_train.parquet"}, {"split": "validation", "path": "newsqa_validation.parquet"}, {"split": "test", "path": "newsqa_test.parquet"}]}]} | 2023-12-05T09:43:41+00:00 | [] | [
"en"
] | TAGS
#size_categories-10K<n<100K #language-English #region-us
|
Converting newsqa dataset to identical format as lmqg/qag_squad for asahi417/lm-question-generation
GitHub Repo | [] | [
"TAGS\n#size_categories-10K<n<100K #language-English #region-us \n"
] | [
22
] | [
"passage: TAGS\n#size_categories-10K<n<100K #language-English #region-us \n"
] |
ca1ec6f34863b1afcfe3bd68fd4b41875f3b59c0 |
# Dataset Card for Evaluation run of DiscoResearch/DiscoLM-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/DiscoResearch/DiscoLM-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 [DiscoResearch/DiscoLM-70b](https://huggingface.co/DiscoResearch/DiscoLM-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_DiscoResearch__DiscoLM-70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T09:06:38.645783](https://huggingface.co/datasets/open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b/blob/main/results_2023-12-05T09-06-38.645783.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": 0.6867356803337994,
"acc_stderr": 0.030796614406114163,
"acc_norm": 0.6887936208915021,
"acc_norm_stderr": 0.03141252742960891,
"mc1": 0.41615667074663404,
"mc1_stderr": 0.017255657502903043,
"mc2": 0.576426250198526,
"mc2_stderr": 0.015041628962992867
},
"harness|arc:challenge|25": {
"acc": 0.6527303754266212,
"acc_stderr": 0.013913034529620453,
"acc_norm": 0.6877133105802048,
"acc_norm_stderr": 0.013542598541688065
},
"harness|hellaswag|10": {
"acc": 0.6768571997610038,
"acc_stderr": 0.004667209383690245,
"acc_norm": 0.8609838677554272,
"acc_norm_stderr": 0.0034525630964691296
},
"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": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7894736842105263,
"acc_stderr": 0.03317672787533157,
"acc_norm": 0.7894736842105263,
"acc_norm_stderr": 0.03317672787533157
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7396226415094339,
"acc_stderr": 0.027008766090708045,
"acc_norm": 0.7396226415094339,
"acc_norm_stderr": 0.027008766090708045
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7916666666666666,
"acc_stderr": 0.033961162058453336,
"acc_norm": 0.7916666666666666,
"acc_norm_stderr": 0.033961162058453336
},
"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": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"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": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6510638297872341,
"acc_stderr": 0.03115852213135779,
"acc_norm": 0.6510638297872341,
"acc_norm_stderr": 0.03115852213135779
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4298245614035088,
"acc_stderr": 0.046570472605949625,
"acc_norm": 0.4298245614035088,
"acc_norm_stderr": 0.046570472605949625
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.039966295748767186,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.039966295748767186
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.43915343915343913,
"acc_stderr": 0.025559920550531006,
"acc_norm": 0.43915343915343913,
"acc_norm_stderr": 0.025559920550531006
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8096774193548387,
"acc_stderr": 0.022331707611823074,
"acc_norm": 0.8096774193548387,
"acc_norm_stderr": 0.022331707611823074
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5270935960591133,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.5270935960591133,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.04461960433384739,
"acc_norm": 0.73,
"acc_norm_stderr": 0.04461960433384739
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8303030303030303,
"acc_stderr": 0.02931118867498312,
"acc_norm": 0.8303030303030303,
"acc_norm_stderr": 0.02931118867498312
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8434343434343434,
"acc_stderr": 0.025890520358141454,
"acc_norm": 0.8434343434343434,
"acc_norm_stderr": 0.025890520358141454
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9326424870466321,
"acc_stderr": 0.0180883938390789,
"acc_norm": 0.9326424870466321,
"acc_norm_stderr": 0.0180883938390789
},
"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": 0.29259259259259257,
"acc_stderr": 0.027738969632176088,
"acc_norm": 0.29259259259259257,
"acc_norm_stderr": 0.027738969632176088
},
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}
```
### 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_DiscoResearch__DiscoLM-70b | [
"region:us"
] | 2023-12-05T09:09:38+00:00 | {"pretty_name": "Evaluation run of DiscoResearch/DiscoLM-70b", "dataset_summary": "Dataset automatically created during the evaluation run of model [DiscoResearch/DiscoLM-70b](https://huggingface.co/DiscoResearch/DiscoLM-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_DiscoResearch__DiscoLM-70b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-05T09:06:38.645783](https://huggingface.co/datasets/open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b/blob/main/results_2023-12-05T09-06-38.645783.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\": 0.6867356803337994,\n \"acc_stderr\": 0.030796614406114163,\n \"acc_norm\": 0.6887936208915021,\n \"acc_norm_stderr\": 0.03141252742960891,\n \"mc1\": 0.41615667074663404,\n \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.576426250198526,\n \"mc2_stderr\": 0.015041628962992867\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620453,\n \"acc_norm\": 0.6877133105802048,\n \"acc_norm_stderr\": 0.013542598541688065\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6768571997610038,\n \"acc_stderr\": 0.004667209383690245,\n \"acc_norm\": 0.8609838677554272,\n \"acc_norm_stderr\": 0.0034525630964691296\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\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03317672787533157,\n \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03317672787533157\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708045,\n \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708045\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.7916666666666666,\n \"acc_norm_stderr\": 0.033961162058453336\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\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\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\": 0.6589595375722543,\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\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n 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\"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.02931118867498312\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8434343434343434,\n \"acc_stderr\": 0.025890520358141454,\n \"acc_norm\": 0.8434343434343434,\n \"acc_norm_stderr\": 0.025890520358141454\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.0180883938390789,\n \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.0180883938390789\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\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176088,\n \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176088\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7689075630252101,\n \"acc_stderr\": 0.027381406927868886,\n \"acc_norm\": 0.7689075630252101,\n \"acc_norm_stderr\": 0.027381406927868886\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8752293577981651,\n \"acc_stderr\": 0.01416829835915634,\n \"acc_norm\": 0.8752293577981651,\n \"acc_norm_stderr\": 0.01416829835915634\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5694444444444444,\n \"acc_stderr\": 0.03376922151252335,\n \"acc_norm\": 0.5694444444444444,\n \"acc_norm_stderr\": 0.03376922151252335\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8872549019607843,\n \"acc_stderr\": 0.02219857103945679,\n \"acc_norm\": 0.8872549019607843,\n \"acc_norm_stderr\": 0.02219857103945679\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7937219730941704,\n \"acc_stderr\": 0.02715715047956382,\n \"acc_norm\": 0.7937219730941704,\n \"acc_norm_stderr\": 0.02715715047956382\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.0349814938546247,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.0349814938546247\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8677685950413223,\n \"acc_stderr\": 0.03092278832044579,\n \"acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.03092278832044579\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n \"acc_stderr\": 0.03434300243631002,\n \"acc_norm\": 0.8518518518518519,\n \"acc_norm_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of DiscoResearch/DiscoLM-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 DiscoResearch/DiscoLM-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 2023-12-05T09:06:38.645783(note that their might be results for other tasks in 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
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of DiscoResearch/DiscoLM-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 DiscoResearch/DiscoLM-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 2023-12-05T09:06:38.645783(note that their might be results for other tasks in 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"
] |
9e8bcf16206e744ea76b1c5271a908fb4bd3a45a | # Dataset Card for "vietnamese-news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tiennv/vietnamese-news | [
"region:us"
] | 2023-12-05T09:14:46+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28837412151, "num_examples": 12573213}], "download_size": 15141327938, "dataset_size": 28837412151}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T09:45:05+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "vietnamese-news"
More Information needed | [
"# Dataset Card for \"vietnamese-news\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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"passage: TAGS\n#region-us \n# Dataset Card for \"vietnamese-news\"\n\nMore Information needed"
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23e0633de11f1de0585274c874953f10c85bfcd2 | # Dataset Card for "english-wiki-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tiennv/english-wiki-corpus | [
"region:us"
] | 2023-12-05T09:46:42+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8275936982, "num_examples": 10686170}], "download_size": 1407476006, "dataset_size": 8275936982}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T09:49:56+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "english-wiki-corpus"
More Information needed | [
"# Dataset Card for \"english-wiki-corpus\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"english-wiki-corpus\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"english-wiki-corpus\"\n\nMore Information needed"
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6ba82933eaf39b0defbcefe2cf4b9e01f5ad60eb | # Dataset Card for "english-mc4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tiennv/english-mc4 | [
"region:us"
] | 2023-12-05T09:55:22+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24653765251, "num_examples": 14294240}], "download_size": 15068999152, "dataset_size": 24653765251}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T10:26:36+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "english-mc4"
More Information needed | [
"# Dataset Card for \"english-mc4\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"english-mc4\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"english-mc4\"\n\nMore Information needed"
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56080b4b6f77c39b0a57147b1025feaabd1d843e | # Dataset Card for RuSRL
## Dataset Summary
This dataset contains annotations of semantic frames and intra-frame syntax for 1500 Russian sentences.
### Dataset Description
Each sentence is annotated with predicate-argument structures. Syntactic information is also provided for each frame.
```
{
"sent_id": 1404,
"tokens": ["в", "такой", "ситуации", "основные", "метеоэлементы",
"-", "температура", ",", "влажность", ",", "давление", "-",
"претерпевают", "малые", "суточные", "изменения", "."],
"synt_head": [12, 2, 0, 4, 12, -1, 4, -1, 6, -1, 8, -1, -1, 15, 15, 12, -1],
"sem_head": [-1, -1, -1, -1, 12, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 12, -1],
"sem_role": ["_", "_", "_", "_", "субъект", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "предикат", "_"]
}
```
- **Language:** Russian
- **Size:** 1500 sentences
## Citation
```
@inproceedings{shelmanov2014methods,
title={Methods for semantic role labeling of Russian texts},
author={Shelmanov, AO and Smirnov, IV},
booktitle={Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference Dialogue},
volume={13},
number={20},
pages={607--620},
year={2014}
}
``` | IsaNLP/RuSRL | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:ru",
"license:cc-by-nc-4.0",
"semantic-role-labeling",
"syntax-parsing",
"tokenization",
"region:us"
] | 2023-12-05T09:58:39+00:00 | {"annotations_creators": ["expert-generated"], "language": ["ru"], "license": "cc-by-nc-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "pretty_name": "RuSRL", "subtasks": ["semantic-role-labeling", "parsing"], "tags": ["semantic-role-labeling", "syntax-parsing", "tokenization"]} | 2023-12-05T11:01:32+00:00 | [] | [
"ru"
] | TAGS
#task_categories-token-classification #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Russian #license-cc-by-nc-4.0 #semantic-role-labeling #syntax-parsing #tokenization #region-us
| # Dataset Card for RuSRL
## Dataset Summary
This dataset contains annotations of semantic frames and intra-frame syntax for 1500 Russian sentences.
### Dataset Description
Each sentence is annotated with predicate-argument structures. Syntactic information is also provided for each frame.
- Language: Russian
- Size: 1500 sentences
| [
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] |
422ec5314cd50f70ec3f9a0b654fd8ef532584b2 | # Dataset Card for "kdd210_hourly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
**Download the Dataset**:
```python
from datasets import load_dataset
dataset = load_dataset("LeoTungAnh/kdd210_hourly")
```
**Dataset Card for Air Quality in KDD cup 2018**
Originally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.
**Preprocessing information**:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
- Preprocessing steps:
1. Standardizing data.
2. Replacing NaN values with zeros.
**Dataset information**:
- Missing values are converted to zeros.
- Number of time series: 210
- Number of training samples: 10802
- Number of validation samples: 10850 (number_of_training_samples + 48)
- Number of testing samples: 10898 (number_of_validation_samples + 48)
**Dataset format**:
```python
Dataset({
features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
num_rows: 210
})
```
**Data format for a sample**:
- 'start': datetime.datetime
- 'target': list of a time series data
- 'feat_static_cat': time series index
- 'feat_dynamic_real': None
- 'item_id': name of time series
**Data example**:
```python
{'start': datetime.datetime(2017, 1, 1, 14, 0, 0),
'feat_static_cat': [0],
'feat_dynamic_real': None,
'item_id': 'T1',
'target': [ 1.46812152, 1.31685537, 1.26169969, ..., 0.47487208, 0.80586637, 0.33006964]
}
```
**Usage**:
- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
- Other algorithms can extract data directly by making use of 'target' feature. | LeoTungAnh/kdd210_hourly | [
"region:us"
] | 2023-12-05T10:19:13+00:00 | {"dataset_info": {"features": [{"name": "start", "dtype": "timestamp[s]"}, {"name": "feat_static_cat", "sequence": "uint64"}, {"name": "feat_dynamic_real", "sequence": {"sequence": "float32"}}, {"name": "item_id", "dtype": "string"}, {"name": "target", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 18154839, "num_examples": 210}, {"name": "validation", "num_bytes": 18235479, "num_examples": 210}, {"name": "test", "num_bytes": 18316119, "num_examples": 210}], "download_size": 47737715, "dataset_size": 54706437}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-12-06T00:51:46+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "kdd210_hourly"
More Information needed
Download the Dataset:
Dataset Card for Air Quality in KDD cup 2018
Originally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.
Preprocessing information:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
- Preprocessing steps:
1. Standardizing data.
2. Replacing NaN values with zeros.
Dataset information:
- Missing values are converted to zeros.
- Number of time series: 210
- Number of training samples: 10802
- Number of validation samples: 10850 (number_of_training_samples + 48)
- Number of testing samples: 10898 (number_of_validation_samples + 48)
Dataset format:
Data format for a sample:
- 'start': datetime.datetime
- 'target': list of a time series data
- 'feat_static_cat': time series index
- 'feat_dynamic_real': None
- 'item_id': name of time series
Data example:
Usage:
- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
- Other algorithms can extract data directly by making use of 'target' feature. | [
"# Dataset Card for \"kdd210_hourly\"\n\nMore Information needed\n\nDownload the Dataset:\n\n\nDataset Card for Air Quality in KDD cup 2018\n\nOriginally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.\n\nPreprocessing information:\n- Grouped by hour (frequency: \"1H\").\n- Applied Standardization as preprocessing technique (\"Std\").\n- Preprocessing steps:\n 1. Standardizing data.\n 2. Replacing NaN values with zeros.\n \nDataset information:\n- Missing values are converted to zeros.\n- Number of time series: 210\n- Number of training samples: 10802\n- Number of validation samples: 10850 (number_of_training_samples + 48)\n- Number of testing samples: 10898 (number_of_validation_samples + 48)\n\nDataset format:\n\nData format for a sample:\n\n- 'start': datetime.datetime\n\n- 'target': list of a time series data\n\n- 'feat_static_cat': time series index\n\n- 'feat_dynamic_real': None\n\n- 'item_id': name of time series\n\n\nData example:\n\n\nUsage:\n- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.\n- Other algorithms can extract data directly by making use of 'target' feature."
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"kdd210_hourly\"\n\nMore Information needed\n\nDownload the Dataset:\n\n\nDataset Card for Air Quality in KDD cup 2018\n\nOriginally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.\n\nPreprocessing information:\n- Grouped by hour (frequency: \"1H\").\n- Applied Standardization as preprocessing technique (\"Std\").\n- Preprocessing steps:\n 1. Standardizing data.\n 2. Replacing NaN values with zeros.\n \nDataset information:\n- Missing values are converted to zeros.\n- Number of time series: 210\n- Number of training samples: 10802\n- Number of validation samples: 10850 (number_of_training_samples + 48)\n- Number of testing samples: 10898 (number_of_validation_samples + 48)\n\nDataset format:\n\nData format for a sample:\n\n- 'start': datetime.datetime\n\n- 'target': list of a time series data\n\n- 'feat_static_cat': time series index\n\n- 'feat_dynamic_real': None\n\n- 'item_id': name of time series\n\n\nData example:\n\n\nUsage:\n- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.\n- Other algorithms can extract data directly by making use of 'target' feature."
] | [
6,
353
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"kdd210_hourly\"\n\nMore Information needed\n\nDownload the Dataset:\n\n\nDataset Card for Air Quality in KDD cup 2018\n\nOriginally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.\n\nPreprocessing information:\n- Grouped by hour (frequency: \"1H\").\n- Applied Standardization as preprocessing technique (\"Std\").\n- Preprocessing steps:\n 1. Standardizing data.\n 2. Replacing NaN values with zeros.\n \nDataset information:\n- Missing values are converted to zeros.\n- Number of time series: 210\n- Number of training samples: 10802\n- Number of validation samples: 10850 (number_of_training_samples + 48)\n- Number of testing samples: 10898 (number_of_validation_samples + 48)\n\nDataset format:\n\nData format for a sample:\n\n- 'start': datetime.datetime\n\n- 'target': list of a time series data\n\n- 'feat_static_cat': time series index\n\n- 'feat_dynamic_real': None\n\n- 'item_id': name of time series\n\n\nData example:\n\n\nUsage:\n- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.\n- Other algorithms can extract data directly by making use of 'target' feature."
] |
cc85c24c863afb05b5a52f68950f9ba6b69975cf |
OpenViVQA: Open-domain Vietnamese Visual Question Answering
=====
The OpenViVQA dataset contains <b>11,000+</b> images with <b>37,000+</b> question-answer pairs which introduces the Text-based Open-ended Visual Question Answering in Vietnamese. This dataset is publicly available to the research community in the VLSP 2023 - ViVRC shared task challenge. You can access the dataset as well as submit your results to evaluate on the private test set on the [Codalab](https://codalab.lisn.upsaclay.fr/competitions/15212#participate) evaluation system.
Link to the OpenViVQA dataset:
- [Train images](train-images.zip) + [train annotations](vlsp2023_train_data.json).
- [Dev images](dev-images.zip) + [dev annotations](vlsp2023_dev_data.json).
- [Test images](test-images.zip) + [test annotations (without answers)](vlsp2023_test_data.json).
If you mention or use any information from our dataset, please cite our paper:
```
@article{NGUYEN2023101868,
title = {OpenViVQA: Task, dataset, and multimodal fusion models for visual question answering in Vietnamese},
journal = {Information Fusion},
volume = {100},
pages = {101868},
year = {2023},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2023.101868},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523001847},
author = {Nghia Hieu Nguyen and Duong T.D. Vo and Kiet {Van Nguyen} and Ngan Luu-Thuy Nguyen},
keywords = {Visual question answering, Vision-language understanding, Low-resource languages, Information fusion, Multimodal representation},
abstract = {In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question–answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and questions, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset11https://github.com/hieunghia-pat/OpenViVQA-dataset. is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese.}
}
```
### Contact
This repository was constructed under the instruction of the [NLP@UIT Research Group](https://nlp.uit.edu.vn/). For more information, contact the following author:
1. Nghia Hieu Nguyen. Email: [email protected] | uitnlp/OpenViVQA-dataset | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:vi",
"license:mit",
"region:us"
] | 2023-12-05T10:52:34+00:00 | {"language": ["vi"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["visual-question-answering"]} | 2023-12-13T14:37:50+00:00 | [] | [
"vi"
] | TAGS
#task_categories-visual-question-answering #size_categories-10K<n<100K #language-Vietnamese #license-mit #region-us
|
OpenViVQA: Open-domain Vietnamese Visual Question Answering
=====
!examples
The OpenViVQA dataset contains <b>11,000+</b> images with <b>37,000+</b> question-answer pairs which introduces the Text-based Open-ended Visual Question Answering in Vietnamese. This dataset is publicly available to the research community in the VLSP 2023 - ViVRC shared task challenge. You can access the dataset as well as submit your results to evaluate on the private test set on the Codalab evaluation system.
Link to the OpenViVQA dataset:
- Train images + train annotations.
- Dev images + dev annotations.
- Test images + test annotations (without answers).
If you mention or use any information from our dataset, please cite our paper:
### Contact
This repository was constructed under the instruction of the NLP@UIT Research Group. For more information, contact the following author:
1. Nghia Hieu Nguyen. Email: nghiangh@URL | [
"### Contact\n\nThis repository was constructed under the instruction of the NLP@UIT Research Group. For more information, contact the following author:\n1. Nghia Hieu Nguyen. Email: nghiangh@URL"
] | [
"TAGS\n#task_categories-visual-question-answering #size_categories-10K<n<100K #language-Vietnamese #license-mit #region-us \n",
"### Contact\n\nThis repository was constructed under the instruction of the NLP@UIT Research Group. For more information, contact the following author:\n1. Nghia Hieu Nguyen. Email: nghiangh@URL"
] | [
45,
47
] | [
"passage: TAGS\n#task_categories-visual-question-answering #size_categories-10K<n<100K #language-Vietnamese #license-mit #region-us \n### Contact\n\nThis repository was constructed under the instruction of the NLP@UIT Research Group. For more information, contact the following author:\n1. Nghia Hieu Nguyen. Email: nghiangh@URL"
] |
770076f077c4c5e298498fa32f804857f46d5134 |
# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences),
and is the **recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback**.
Read more about Argilla's approach towards UltraFeedback binarization at [`argilla/ultrafeedback-binarized-preferences/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences/blob/main/README.md).
## Differences with `argilla/ultrafeedback-binarized-preferences`
Thanks to the recent issue identified by [AllenAI](https://huggingface.co/allenai) related to the TruthfulQA contamination within the
original UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking
in the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) from HuggingFace H4), we also decided
to follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which
implied using the average of the preference ratings rather than the critique overall score, as
[`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) did.
Besides that, we also saw that not only the rows with the `source=truthful_qa` were contamined (for obvious reasons), but also some
coming from ShareGPT, so we also removed those doing a left join with both subsets from the [`truthful_qa`](https://huggingface.co/datasets/truthful_qa) dataset.
Additionally, we also modified the formatting to be aligned with both [`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized),
and [`allenai/ultrafeedback_binarized_cleaned`](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) in order to ease
the integration within the [`huggingface/alignment-handbook`](https://github.com/huggingface/alignment-handbook) so that the formatting is standardized.
## Reproduce
<a target="_blank" href="https://colab.research.google.com/drive/1XR9P1St4yTNY0tjti_tIjm-yzP5Bfqc0?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to
remove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at [`notebook.ipynb`](./notebook.ipynb) within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as
ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
## Citation
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback
Additionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:
```bibtex
@misc{notus2023,
author = {Alvaro Bartolome and Gabriel Martin and Daniel Vila},
title = {Notus},
year = {2023},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/argilla-io/notus}}
}
```
> Alphabetically ordered by last name due to equal contribution. | argilla/ultrafeedback-binarized-preferences-cleaned | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"dpo",
"preference",
"ultrafeedback",
"region:us"
] | 2023-12-05T11:07:34+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "UltraFeedback Binarized Preferences Cleaned", "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "chosen-rating", "dtype": "float64"}, {"name": "chosen-model", "dtype": "string"}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected-rating", "dtype": "float64"}, {"name": "rejected-model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 284937773, "num_examples": 60917}], "download_size": 143257393, "dataset_size": 284937773}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["dpo", "preference", "ultrafeedback"]} | 2023-12-11T14:22:19+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #dpo #preference #ultrafeedback #region-us
|
# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of 'argilla/ultrafeedback-binarized-preferences',
and is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.
Read more about Argilla's approach towards UltraFeedback binarization at 'argilla/ultrafeedback-binarized-preferences/URL'.
## Differences with 'argilla/ultrafeedback-binarized-preferences'
Thanks to the recent issue identified by AllenAI related to the TruthfulQA contamination within the
original UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking
in the Open LLM Leaderboard from HuggingFace H4), we also decided
to follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which
implied using the average of the preference ratings rather than the critique overall score, as
'HuggingFaceH4/ultrafeedback_binarized' did.
Besides that, we also saw that not only the rows with the 'source=truthful_qa' were contamined (for obvious reasons), but also some
coming from ShareGPT, so we also removed those doing a left join with both subsets from the 'truthful_qa' dataset.
Additionally, we also modified the formatting to be aligned with both 'HuggingFaceH4/ultrafeedback_binarized',
and 'allenai/ultrafeedback_binarized_cleaned' in order to ease
the integration within the 'huggingface/alignment-handbook' so that the formatting is standardized.
## Reproduce
<a target="_blank" href="URL
<img src="URL alt="Open In Colab"/>
</a>
To reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to
remove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at 'URL' within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as
ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: URL
Additionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:
> Alphabetically ordered by last name due to equal contribution. | [
"# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)\n\nThis dataset represents a new iteration on top of 'argilla/ultrafeedback-binarized-preferences',\nand is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.\n\nRead more about Argilla's approach towards UltraFeedback binarization at 'argilla/ultrafeedback-binarized-preferences/URL'.",
"## Differences with 'argilla/ultrafeedback-binarized-preferences'\n\nThanks to the recent issue identified by AllenAI related to the TruthfulQA contamination within the\noriginal UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking\nin the Open LLM Leaderboard from HuggingFace H4), we also decided\nto follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which\nimplied using the average of the preference ratings rather than the critique overall score, as\n'HuggingFaceH4/ultrafeedback_binarized' did.\n\nBesides that, we also saw that not only the rows with the 'source=truthful_qa' were contamined (for obvious reasons), but also some\ncoming from ShareGPT, so we also removed those doing a left join with both subsets from the 'truthful_qa' dataset.\n\nAdditionally, we also modified the formatting to be aligned with both 'HuggingFaceH4/ultrafeedback_binarized',\nand 'allenai/ultrafeedback_binarized_cleaned' in order to ease\nthe integration within the 'huggingface/alignment-handbook' so that the formatting is standardized.",
"## Reproduce\n\n<a target=\"_blank\" href=\"URL\n <img src=\"URL alt=\"Open In Colab\"/>\n</a>\n\nTo reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to\nremove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at 'URL' within this repository.\n\nFrom Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as\nourselves, as well as the whole community, benefit a lot from open source and we also want to give back.\n\nIf you find this dataset is useful in your work, please cite the original UltraFeedback dataset: URL\n\nAdditionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:\n\n\n\n> Alphabetically ordered by last name due to equal contribution."
] | [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #dpo #preference #ultrafeedback #region-us \n",
"# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)\n\nThis dataset represents a new iteration on top of 'argilla/ultrafeedback-binarized-preferences',\nand is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.\n\nRead more about Argilla's approach towards UltraFeedback binarization at 'argilla/ultrafeedback-binarized-preferences/URL'.",
"## Differences with 'argilla/ultrafeedback-binarized-preferences'\n\nThanks to the recent issue identified by AllenAI related to the TruthfulQA contamination within the\noriginal UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking\nin the Open LLM Leaderboard from HuggingFace H4), we also decided\nto follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which\nimplied using the average of the preference ratings rather than the critique overall score, as\n'HuggingFaceH4/ultrafeedback_binarized' did.\n\nBesides that, we also saw that not only the rows with the 'source=truthful_qa' were contamined (for obvious reasons), but also some\ncoming from ShareGPT, so we also removed those doing a left join with both subsets from the 'truthful_qa' dataset.\n\nAdditionally, we also modified the formatting to be aligned with both 'HuggingFaceH4/ultrafeedback_binarized',\nand 'allenai/ultrafeedback_binarized_cleaned' in order to ease\nthe integration within the 'huggingface/alignment-handbook' so that the formatting is standardized.",
"## Reproduce\n\n<a target=\"_blank\" href=\"URL\n <img src=\"URL alt=\"Open In Colab\"/>\n</a>\n\nTo reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to\nremove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at 'URL' within this repository.\n\nFrom Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as\nourselves, as well as the whole community, benefit a lot from open source and we also want to give back.\n\nIf you find this dataset is useful in your work, please cite the original UltraFeedback dataset: URL\n\nAdditionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:\n\n\n\n> Alphabetically ordered by last name due to equal contribution."
] | [
49,
119,
303,
233
] | [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #dpo #preference #ultrafeedback #region-us \n# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)\n\nThis dataset represents a new iteration on top of 'argilla/ultrafeedback-binarized-preferences',\nand is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.\n\nRead more about Argilla's approach towards UltraFeedback binarization at 'argilla/ultrafeedback-binarized-preferences/URL'.## Differences with 'argilla/ultrafeedback-binarized-preferences'\n\nThanks to the recent issue identified by AllenAI related to the TruthfulQA contamination within the\noriginal UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking\nin the Open LLM Leaderboard from HuggingFace H4), we also decided\nto follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which\nimplied using the average of the preference ratings rather than the critique overall score, as\n'HuggingFaceH4/ultrafeedback_binarized' did.\n\nBesides that, we also saw that not only the rows with the 'source=truthful_qa' were contamined (for obvious reasons), but also some\ncoming from ShareGPT, so we also removed those doing a left join with both subsets from the 'truthful_qa' dataset.\n\nAdditionally, we also modified the formatting to be aligned with both 'HuggingFaceH4/ultrafeedback_binarized',\nand 'allenai/ultrafeedback_binarized_cleaned' in order to ease\nthe integration within the 'huggingface/alignment-handbook' so that the formatting is standardized."
] |
2284756c74f7aba6e4a74f75d4b6d52b72d231a5 | Dataset to train model | abhijeet-ta/ads_title_generation | [
"region:us"
] | 2023-12-05T11:42:22+00:00 | {} | 2023-12-05T13:33:13+00:00 | [] | [] | TAGS
#region-us
| Dataset to train model | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
77ee41e19954ff8203f68919c11096b08593a028 | # Dataset Card for "1000_trees_extended_onlytrees"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pin-lpt/1000_trees_extended_onlytrees | [
"region:us"
] | 2023-12-05T11:49:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23779232.0, "num_examples": 10}], "download_size": 23781715, "dataset_size": 23779232.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T15:28:55+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "1000_trees_extended_onlytrees"
More Information needed | [
"# Dataset Card for \"1000_trees_extended_onlytrees\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"1000_trees_extended_onlytrees\"\n\nMore Information needed"
] | [
6,
23
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"1000_trees_extended_onlytrees\"\n\nMore Information needed"
] |
173a4c9576528f085e2ef63263f6e010b81ff2d2 | # Dataset Card for "tmp_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | BobaZooba/tmp_dataset | [
"region:us"
] | 2023-12-05T12:33:52+00:00 | {"dataset_info": {"features": [{"name": "hello", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19, "num_examples": 2}], "download_size": 780, "dataset_size": 19}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T12:33:55+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "tmp_dataset"
More Information needed | [
"# Dataset Card for \"tmp_dataset\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"tmp_dataset\"\n\nMore Information needed"
] | [
6,
15
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"tmp_dataset\"\n\nMore Information needed"
] |
655ef66ef2be07f89aec61407f24c772802eb87d |
# EuroSAT RGB
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
EUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
- **Paper:** https://arxiv.org/abs/1709.00029
- **Homepage:** https://github.com/phelber/EuroSAT
## Description
<!-- Provide a longer summary of what this dataset is. -->
The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the [ESA Sentinel-2 satellite](https://sentinel.esa.int/web/sentinel/missions/sentinel-2). It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.
The dataset is available in two versions: **RGB only** (this repo) and all 13 [Multispectral (MS) Sentinel-2 bands](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.
- **Total Number of Images**: 27000
- **Bands**: 3 (RGB)
- **Image Resolution**: 64x64m
- **Land Cover Classes**: 10
- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/EuroSAT_RGB")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
EuroSAT_RGB = load_dataset("blanchon/EuroSAT_RGB")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{helber2017eurosat,
title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author={Helber, et al.},
journal={ArXiv preprint arXiv:1709.00029},
year={2017}
}
```
| blanchon/EuroSAT_RGB | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"land-cover-classification",
"sentinel-2",
"arxiv:1709.00029",
"region:us"
] | 2023-12-05T12:56:11+00:00 | {"language": "en", "license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "paperswithcode_id": "eurosat", "pretty_name": "EuroSAT RGB", "tags": ["remote-sensing", "earth-observation", "geospatial", "satellite-imagery", "land-cover-classification", "sentinel-2"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Annual Crop", "1": "Forest", "2": "Herbaceous Vegetation", "3": "Highway", "4": "Industrial Buildings", "5": "Pasture", "6": "Permanent Crop", "7": "Residential Buildings", "8": "River", "9": "SeaLake"}}}}, {"name": "filename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 104485303.0, "num_examples": 16200}, {"name": "test", "num_bytes": 34726245.0, "num_examples": 5400}, {"name": "validation", "num_bytes": 34781690.0, "num_examples": 5400}], "download_size": 174279561, "dataset_size": 173993238.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2023-12-05T13:02:42+00:00 | [
"1709.00029"
] | [
"en"
] | TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #land-cover-classification #sentinel-2 #arxiv-1709.00029 #region-us
|
# EuroSAT RGB
!EuroSAT RGB
EUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
- Paper: URL
- Homepage: URL
## Description
The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.
The dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.
- Total Number of Images: 27000
- Bands: 3 (RGB)
- Image Resolution: 64x64m
- Land Cover Classes: 10
- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
## Usage
To use this dataset, simply use 'datasets.load_dataset("blanchon/EuroSAT_RGB")'.
If you use the EuroSAT dataset in your research, please consider citing the following publication:
| [
"# EuroSAT RGB\n\n\n!EuroSAT RGB\n\n\nEUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.\n- Paper: URL\n- Homepage: URL",
"## Description\n\n\n\nThe EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.\n\nThe dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.\n\n- Total Number of Images: 27000\n- Bands: 3 (RGB)\n- Image Resolution: 64x64m\n- Land Cover Classes: 10\n- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake",
"## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/EuroSAT_RGB\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] | [
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #land-cover-classification #sentinel-2 #arxiv-1709.00029 #region-us \n",
"# EuroSAT RGB\n\n\n!EuroSAT RGB\n\n\nEUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.\n- Paper: URL\n- Homepage: URL",
"## Description\n\n\n\nThe EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.\n\nThe dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.\n\n- Total Number of Images: 27000\n- Bands: 3 (RGB)\n- Image Resolution: 64x64m\n- Land Cover Classes: 10\n- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake",
"## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/EuroSAT_RGB\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] | [
83,
63,
207,
53
] | [
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #land-cover-classification #sentinel-2 #arxiv-1709.00029 #region-us \n# EuroSAT RGB\n\n\n!EuroSAT RGB\n\n\nEUROSAT RGB is the RGB version of the EUROSAT dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.\n- Paper: URL\n- Homepage: URL## Description\n\n\n\nThe EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.\n\nThe dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.\n\n- Total Number of Images: 27000\n- Bands: 3 (RGB)\n- Image Resolution: 64x64m\n- Land Cover Classes: 10\n- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/EuroSAT_RGB\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] |
0bc543ea4de948e0522b46edbc57946a8fad8633 |
The Urban Sounds dataset consists of audio samples collected in Amsterdam in the period 2018 - 2020.
The datasamples were collected for a project to create a sensor to classify audio events, with the goal of tackling noise pollution in the city.
This 'urban sounds small' dataset is a small part of the dataset, used for testing and prototyping purposes.
More on the sensor can be found here: https://github.com/sensemakersamsterdam/OpenEars
| UrbanSounds/urban_sounds_small | [
"task_categories:audio-classification",
"size_categories:n<1K",
"language:nl",
"language:en",
"license:apache-2.0",
"audio event",
"noise pollution",
"urban",
"region:us"
] | 2023-12-05T13:06:58+00:00 | {"language": ["nl", "en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["audio-classification"], "tags": ["audio event", "noise pollution", "urban"]} | 2023-12-07T10:49:13+00:00 | [] | [
"nl",
"en"
] | TAGS
#task_categories-audio-classification #size_categories-n<1K #language-Dutch #language-English #license-apache-2.0 #audio event #noise pollution #urban #region-us
|
The Urban Sounds dataset consists of audio samples collected in Amsterdam in the period 2018 - 2020.
The datasamples were collected for a project to create a sensor to classify audio events, with the goal of tackling noise pollution in the city.
This 'urban sounds small' dataset is a small part of the dataset, used for testing and prototyping purposes.
More on the sensor can be found here: URL
| [] | [
"TAGS\n#task_categories-audio-classification #size_categories-n<1K #language-Dutch #language-English #license-apache-2.0 #audio event #noise pollution #urban #region-us \n"
] | [
56
] | [
"passage: TAGS\n#task_categories-audio-classification #size_categories-n<1K #language-Dutch #language-English #license-apache-2.0 #audio event #noise pollution #urban #region-us \n"
] |
fceda27a3c49aec8c9a9ecc674097a21d6b9b793 |
# EuroSAT MSI
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
EUROSAT is a classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
- **Paper:** https://arxiv.org/abs/1709.00029
- **Homepage:** https://github.com/phelber/EuroSAT
## Description
<!-- Provide a longer summary of what this dataset is. -->
The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the [ESA Sentinel-2 satellite](https://sentinel.esa.int/web/sentinel/missions/sentinel-2). It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.
The dataset is available in two versions: RGB only and **all 13** (this repo) [Multispectral (MS) Sentinel-2 bands](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial). EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.
- **Total Number of Images**: 27000
- **Bands**: 13 (MSI)
- **Image Resolution**: 64x64m
- **Land Cover Classes**: 10
- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/EuroSAT_MSI")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
EuroSAT_MSI = load_dataset("blanchon/EuroSAT_MSI")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{helber2017eurosat,
title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author={Helber, et al.},
journal={ArXiv preprint arXiv:1709.00029},
year={2017}
}
```
| blanchon/EuroSAT_MSI | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"land-cover-classification",
"multispectral",
"sentinel-2",
"arxiv:1709.00029",
"region:us"
] | 2023-12-05T13:15:45+00:00 | {"language": "en", "license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "paperswithcode_id": "eurosat", "pretty_name": "EuroSAT MSI", "tags": ["remote-sensing", "earth-observation", "geospatial", "satellite-imagery", "land-cover-classification", "multispectral", "sentinel-2"], "dataset_info": {"features": [{"name": "image", "dtype": {"array3_d": {"dtype": "uint16", "shape": [64, 64, 13]}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Annual Crop", "1": "Forest", "2": "Herbaceous Vegetation", "3": "Highway", "4": "Industrial Buildings", "5": "Pasture", "6": "Permanent Crop", "7": "Residential Buildings", "8": "River", "9": "SeaLake"}}}}, {"name": "filename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1995359806, "num_examples": 16200}, {"name": "test", "num_bytes": 665119564, "num_examples": 5400}, {"name": "validation", "num_bytes": 665120060, "num_examples": 5400}], "download_size": 2379014584, "dataset_size": 3325599430}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2023-12-05T13:33:44+00:00 | [
"1709.00029"
] | [
"en"
] | TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #land-cover-classification #multispectral #sentinel-2 #arxiv-1709.00029 #region-us
|
# EuroSAT MSI
!EuroSAT MSI
EUROSAT is a classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
- Paper: URL
- Homepage: URL
## Description
The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.
The dataset is available in two versions: RGB only and all 13 (this repo) Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.
- Total Number of Images: 27000
- Bands: 13 (MSI)
- Image Resolution: 64x64m
- Land Cover Classes: 10
- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
## Usage
To use this dataset, simply use 'datasets.load_dataset("blanchon/EuroSAT_MSI")'.
If you use the EuroSAT dataset in your research, please consider citing the following publication:
| [
"# EuroSAT MSI\n\n\n!EuroSAT MSI\n\n\nEUROSAT is a classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.\n- Paper: URL\n- Homepage: URL",
"## Description\n\n\n\nThe EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.\n\nThe dataset is available in two versions: RGB only and all 13 (this repo) Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.\n\n- Total Number of Images: 27000\n- Bands: 13 (MSI)\n- Image Resolution: 64x64m\n- Land Cover Classes: 10\n- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake",
"## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/EuroSAT_MSI\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] | [
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #land-cover-classification #multispectral #sentinel-2 #arxiv-1709.00029 #region-us \n",
"# EuroSAT MSI\n\n\n!EuroSAT MSI\n\n\nEUROSAT is a classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.\n- Paper: URL\n- Homepage: URL",
"## Description\n\n\n\nThe EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.\n\nThe dataset is available in two versions: RGB only and all 13 (this repo) Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.\n\n- Total Number of Images: 27000\n- Bands: 13 (MSI)\n- Image Resolution: 64x64m\n- Land Cover Classes: 10\n- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake",
"## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/EuroSAT_MSI\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] | [
87,
58,
207,
53
] | [
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #land-cover-classification #multispectral #sentinel-2 #arxiv-1709.00029 #region-us \n# EuroSAT MSI\n\n\n!EuroSAT MSI\n\n\nEUROSAT is a classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.\n- Paper: URL\n- Homepage: URL## Description\n\n\n\nThe EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.\n\nThe dataset is available in two versions: RGB only and all 13 (this repo) Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.\n\n- Total Number of Images: 27000\n- Bands: 13 (MSI)\n- Image Resolution: 64x64m\n- Land Cover Classes: 10\n- Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/EuroSAT_MSI\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] |
a00e8832402308b5399141cce9b030c39083b16a |
# ADVANCE
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
Audiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.
- **Paper:** https://arxiv.org/abs/2005.08449
- **Homepage:** https://akchen.github.io/ADVANCE-DATASET/
## Description
<!-- Provide a longer summary of what this dataset is. -->
The **Audiovisual Aerial Scene Recognition Dataset** is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from [FreeSound](https://freesound.org/browse/geotags/?c_lat=24&c_lon=20&z=2) and [Google Earth](https://earth.google.com/web/). These images are then labeled into 13 scene categories using OpenStreetMap
The dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.
- **Total Number of Images**: 5075
- **Bands**: 3 (RGB)
- **Image Resolution**: 10mm
- **Image size**: 512x512
- **Land Cover Classes**: 13
- **Classes**: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station
- **Source**: Sentinel-2
- **Dataset features**: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings
- **Dataset format**:, images are three-channel jpgs, audio files are in wav format
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/ADVANCE")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
ADVANCE = load_dataset("blanchon/ADVANCE")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{hu2020crosstask,
title = {Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition},
author = {Di Hu and Xuhong Li and Lichao Mou and P. Jin and Dong Chen and L. Jing and Xiaoxiang Zhu and D. Dou},
journal = {European Conference on Computer Vision},
year = {2020},
doi = {10.1007/978-3-030-58586-0_5},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/7fabb1ef96d2840834cfaf384408309bafc588d5}
}
```
| blanchon/ADVANCE | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"audiovisual-aerial-scene-recognition",
"sentinel-2",
"arxiv:2005.08449",
"region:us"
] | 2023-12-05T13:38:06+00:00 | {"language": "en", "license": "unknown", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "paperswithcode_id": "advance", "pretty_name": "ADVANCE", "tags": ["remote-sensing", "earth-observation", "geospatial", "satellite-imagery", "audiovisual-aerial-scene-recognition", "sentinel-2"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airport", "1": "beach", "2": "bridge", "3": "farmland", "4": "forest", "5": "grassland", "6": "harbour", "7": "lake", "8": "orchard", "9": "residential", "10": "sparse shrub land", "11": "sports land", "12": "train station"}}}}], "splits": [{"name": "train", "num_bytes": 6698580359.05, "num_examples": 5075}], "download_size": 6688165513, "dataset_size": 6698580359.05}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-05T14:14:32+00:00 | [
"2005.08449"
] | [
"en"
] | TAGS
#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #audiovisual-aerial-scene-recognition #sentinel-2 #arxiv-2005.08449 #region-us
|
# ADVANCE
!ADVANCE
Audiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.
- Paper: URL
- Homepage: URL
## Description
The Audiovisual Aerial Scene Recognition Dataset is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap
The dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.
- Total Number of Images: 5075
- Bands: 3 (RGB)
- Image Resolution: 10mm
- Image size: 512x512
- Land Cover Classes: 13
- Classes: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station
- Source: Sentinel-2
- Dataset features: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings
- Dataset format:, images are three-channel jpgs, audio files are in wav format
## Usage
To use this dataset, simply use 'datasets.load_dataset("blanchon/ADVANCE")'.
If you use the EuroSAT dataset in your research, please consider citing the following publication:
| [
"# ADVANCE\n\n\n!ADVANCE\n\n\nAudiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.\n- Paper: URL\n- Homepage: URL",
"## Description\n\n\n\nThe Audiovisual Aerial Scene Recognition Dataset is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap\n\nThe dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.\n\n- Total Number of Images: 5075\n- Bands: 3 (RGB)\n- Image Resolution: 10mm\n- Image size: 512x512\n- Land Cover Classes: 13\n- Classes: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station\n- Source: Sentinel-2\n- Dataset features: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings\n- Dataset format:, images are three-channel jpgs, audio files are in wav format",
"## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/ADVANCE\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] | [
"TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #audiovisual-aerial-scene-recognition #sentinel-2 #arxiv-2005.08449 #region-us \n",
"# ADVANCE\n\n\n!ADVANCE\n\n\nAudiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.\n- Paper: URL\n- Homepage: URL",
"## Description\n\n\n\nThe Audiovisual Aerial Scene Recognition Dataset is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap\n\nThe dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.\n\n- Total Number of Images: 5075\n- Bands: 3 (RGB)\n- Image Resolution: 10mm\n- Image size: 512x512\n- Land Cover Classes: 13\n- Classes: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station\n- Source: Sentinel-2\n- Dataset features: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings\n- Dataset format:, images are three-channel jpgs, audio files are in wav format",
"## Usage\n\nTo use this dataset, simply use 'datasets.load_dataset(\"blanchon/ADVANCE\")'.\n\n\n\nIf you use the EuroSAT dataset in your research, please consider citing the following publication:"
] | [
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"passage: TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-unknown #remote-sensing #earth-observation #geospatial #satellite-imagery #audiovisual-aerial-scene-recognition #sentinel-2 #arxiv-2005.08449 #region-us \n# ADVANCE\n\n\n!ADVANCE\n\n\nAudiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.\n- Paper: URL\n- Homepage: URL## Description\n\n\n\nThe Audiovisual Aerial Scene Recognition Dataset is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap\n\nThe dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.\n\n- Total Number of Images: 5075\n- Bands: 3 (RGB)\n- Image Resolution: 10mm\n- Image size: 512x512\n- Land Cover Classes: 13\n- Classes: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station\n- Source: Sentinel-2\n- Dataset features: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings\n- Dataset format:, images are three-channel jpgs, audio files are in wav format"
] |
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