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46f50e45d202380f738464c5f0c61fa5533a0a93
|
# Dataset Card for "fashion_image_caption-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adirik/fashion_image_caption-100
|
[
"region:us"
] |
2023-08-29T09:41:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22842342.0, "num_examples": 100}], "download_size": 22823708, "dataset_size": 22842342.0}}
|
2023-08-29T09:41:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fashion_image_caption-100"
More Information needed
|
[
"# Dataset Card for \"fashion_image_caption-100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fashion_image_caption-100\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fashion_image_caption-100\"\n\nMore Information needed"
] |
372ddb81b50f675c052e145938e3deb60b8d2724
|
# Dataset Card for "cot_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LahiruLowe/cot_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML
|
[
"region:us"
] |
2023-08-29T09:55:26+00:00
|
{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}, {"name": "system_message", "dtype": "string"}, {"name": "explained_targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59919, "num_examples": 54}], "download_size": 33669, "dataset_size": 59919}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T16:59:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cot_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML"
More Information needed
|
[
"# Dataset Card for \"cot_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cot_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
[
6,
45
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cot_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
e80dae493f822add5888f44f427e39cd6a561808
|
# Dataset Card for "eu_test5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KatMarie/eu_test5
|
[
"region:us"
] |
2023-08-29T09:56:14+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 307789, "num_examples": 5172}], "download_size": 208326, "dataset_size": 307789}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T10:19:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eu_test5"
More Information needed
|
[
"# Dataset Card for \"eu_test5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eu_test5\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eu_test5\"\n\nMore Information needed"
] |
fa2630ab9da7ab52f2ad9adb40072514ec386060
|
# Dataset Card for "loss_landscape_test_set_context_len_128"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/mlm_test_set_context_len_128
|
[
"region:us"
] |
2023-08-29T09:58:48+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 499200, "num_examples": 640}], "download_size": 183124, "dataset_size": 499200}}
|
2023-08-29T09:58:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "loss_landscape_test_set_context_len_128"
More Information needed
|
[
"# Dataset Card for \"loss_landscape_test_set_context_len_128\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"loss_landscape_test_set_context_len_128\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"loss_landscape_test_set_context_len_128\"\n\nMore Information needed"
] |
8c0eb1902ab3b6a3acca01ccb3c8d92149693bbb
|
# Dataset Card for "result_with_w2v2_baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/result_with_w2v2_baseline
|
[
"region:us"
] |
2023-08-29T10:07:39+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "id", "dtype": "string"}, {"name": "w2v2_baseline_transcription", "dtype": "string"}, {"name": "w2v2_baseline_norm", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 174371487.625, "num_examples": 1299}], "download_size": 164231228, "dataset_size": 174371487.625}}
|
2023-08-29T10:08:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "result_with_w2v2_baseline"
More Information needed
|
[
"# Dataset Card for \"result_with_w2v2_baseline\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"result_with_w2v2_baseline\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"result_with_w2v2_baseline\"\n\nMore Information needed"
] |
001c499cc178b3f653344790c582dcbc810480eb
|
# Dataset Card for "loss_landscape_test_set_context_len_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/mlm_test_set_context_len_64
|
[
"region:us"
] |
2023-08-29T10:09:28+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 506880, "num_examples": 1280}], "download_size": 0, "dataset_size": 506880}}
|
2023-08-29T10:28:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "loss_landscape_test_set_context_len_64"
More Information needed
|
[
"# Dataset Card for \"loss_landscape_test_set_context_len_64\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"loss_landscape_test_set_context_len_64\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"loss_landscape_test_set_context_len_64\"\n\nMore Information needed"
] |
0dcf6e335822c924c251ec2960353e5bb9c6304b
|
# Dataset Card for Evaluation run of ai-business/Luban-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ai-business/Luban-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ai-business/Luban-13B](https://huggingface.co/ai-business/Luban-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_ai-business__Luban-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T11:15:33.793306](https://huggingface.co/datasets/open-llm-leaderboard/details_ai-business__Luban-13B/blob/main/results_2023-09-17T11-15-33.793306.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": {
"em": 0.007340604026845637,
"em_stderr": 0.0008741896875346207,
"f1": 0.10464869966443034,
"f1_stderr": 0.0019947106278579182,
"acc": 0.431315608856773,
"acc_stderr": 0.010029949190396351
},
"harness|drop|3": {
"em": 0.007340604026845637,
"em_stderr": 0.0008741896875346207,
"f1": 0.10464869966443034,
"f1_stderr": 0.0019947106278579182
},
"harness|gsm8k|5": {
"acc": 0.09704321455648218,
"acc_stderr": 0.008153768274554716
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237985
}
}
```
### 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_ai-business__Luban-13B
|
[
"region:us"
] |
2023-08-29T10:09:28+00:00
|
{"pretty_name": "Evaluation run of ai-business/Luban-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ai-business/Luban-13B](https://huggingface.co/ai-business/Luban-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 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 agregated 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_ai-business__Luban-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T11:15:33.793306](https://huggingface.co/datasets/open-llm-leaderboard/details_ai-business__Luban-13B/blob/main/results_2023-09-17T11-15-33.793306.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 \"em\": 0.007340604026845637,\n \"em_stderr\": 0.0008741896875346207,\n \"f1\": 0.10464869966443034,\n \"f1_stderr\": 0.0019947106278579182,\n \"acc\": 0.431315608856773,\n \"acc_stderr\": 0.010029949190396351\n },\n \"harness|drop|3\": {\n \"em\": 0.007340604026845637,\n \"em_stderr\": 0.0008741896875346207,\n \"f1\": 0.10464869966443034,\n \"f1_stderr\": 0.0019947106278579182\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09704321455648218,\n \"acc_stderr\": 0.008153768274554716\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n }\n}\n```", "repo_url": "https://huggingface.co/ai-business/Luban-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_17T11_15_33.793306", "path": ["**/details_harness|drop|3_2023-09-17T11-15-33.793306.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T11-15-33.793306.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T11_15_33.793306", "path": ["**/details_harness|gsm8k|5_2023-09-17T11-15-33.793306.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T11-15-33.793306.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T11_15_33.793306", "path": ["**/details_harness|winogrande|5_2023-09-17T11-15-33.793306.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T11-15-33.793306.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T11_08_27.769283", "path": ["results_2023-08-29T11:08:27.769283.parquet"]}, {"split": "2023_09_17T11_15_33.793306", "path": ["results_2023-09-17T11-15-33.793306.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T11-15-33.793306.parquet"]}]}]}
|
2023-09-17T10:15:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of ai-business/Luban-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ai-business/Luban-13B on the Open LLM Leaderboard.
The dataset is composed of 3 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 agregated 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 2023-09-17T11:15:33.793306(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 ai-business/Luban-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ai-business/Luban-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-09-17T11:15:33.793306(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 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 ai-business/Luban-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 ai-business/Luban-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ai-business/Luban-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-09-17T11:15:33.793306(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
dfb55cc079b24483f5d3f31ea9cfd773eb303d10
|
# Dataset Card for "SolidLogosID_converted_processed_V2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
temasarkisov/SolidLogosID_converted_processed_V2
|
[
"region:us"
] |
2023-08-29T10:13:21+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1031221.0, "num_examples": 48}], "download_size": 1031152, "dataset_size": 1031221.0}}
|
2023-08-29T10:13:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SolidLogosID_converted_processed_V2"
More Information needed
|
[
"# Dataset Card for \"SolidLogosID_converted_processed_V2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SolidLogosID_converted_processed_V2\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SolidLogosID_converted_processed_V2\"\n\nMore Information needed"
] |
38fa0ee5ebba609fd0eaf4917d0922736f50f0cb
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_eye_movements_gosdt_l512_d3
|
[
"region:us"
] |
2023-08-29T10:14:07+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2708670915, "dataset_size": 11949520000}}
|
2023-08-29T10:17:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3\"\n\nMore Information needed"
] |
d294d82c391099f0a16d3fb1790e0ea613f541fc
|
# Bangumi Image Base of Ben-to
This is the image base of bangumi Ben-to, we detected 17 characters, 1566 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 208 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 125 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 72 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 411 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 15 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 18 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 42 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 40 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 18 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 139 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 29 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 26 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 46 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 18 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 183 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 158 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/bento
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-08-29T10:15:25+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T03:05:01+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Ben-to
============================
This is the image base of bangumi Ben-to, we detected 17 characters, 1566 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
046d3ba880bd4db59670205b32ad28488c4b6c04
|
# Dataset Card for "mix-gpt4-6k-camel-rlhf-fixed-standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/mix-gpt4-6k-camel-rlhf-fixed-standardized
|
[
"region:us"
] |
2023-08-29T10:16:39+00:00
|
{"dataset_info": {"features": [{"name": "message", "dtype": "string"}, {"name": "message_type", "dtype": "string"}, {"name": "message_id", "dtype": "int64"}, {"name": "conversation_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 51028880, "num_examples": 47010}, {"name": "test", "num_bytes": 3058844, "num_examples": 2716}], "download_size": 25724863, "dataset_size": 54087724}}
|
2023-08-30T19:31:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mix-gpt4-6k-camel-rlhf-fixed-standardized"
More Information needed
|
[
"# Dataset Card for \"mix-gpt4-6k-camel-rlhf-fixed-standardized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mix-gpt4-6k-camel-rlhf-fixed-standardized\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mix-gpt4-6k-camel-rlhf-fixed-standardized\"\n\nMore Information needed"
] |
f49653c24beac7db9ed28849dcefa89acb6477f4
|
# Dataset Card for "loss_landscape_test_set_context_len_256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/mlm_test_set_context_len_256
|
[
"region:us"
] |
2023-08-29T10:20:04+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 495360, "num_examples": 320}], "download_size": 183583, "dataset_size": 495360}}
|
2023-08-29T10:20:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "loss_landscape_test_set_context_len_256"
More Information needed
|
[
"# Dataset Card for \"loss_landscape_test_set_context_len_256\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"loss_landscape_test_set_context_len_256\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"loss_landscape_test_set_context_len_256\"\n\nMore Information needed"
] |
584bcfb6d17b6b7d680834877b1f7e0ee5842ae5
|
# Dataset Card for Evaluation run of yeontaek/Platypus2-13B-LoRa-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/Platypus2-13B-LoRa-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 [yeontaek/Platypus2-13B-LoRa-v2](https://huggingface.co/yeontaek/Platypus2-13B-LoRa-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_yeontaek__Platypus2-13B-LoRa-v2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T11:20:59.240376](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2-13B-LoRa-v2/blob/main/results_2023-08-29T11%3A20%3A59.240376.json):
```python
{
"all": {
"acc": 0.571991245483798,
"acc_stderr": 0.034294067141786025,
"acc_norm": 0.5761375119651778,
"acc_norm_stderr": 0.03427336583128381,
"mc1": 0.28151774785801714,
"mc1_stderr": 0.01574402724825605,
"mc2": 0.4191985438925104,
"mc2_stderr": 0.014270484892545822
},
"harness|arc:challenge|25": {
"acc": 0.5563139931740614,
"acc_stderr": 0.014518421825670444,
"acc_norm": 0.5947098976109215,
"acc_norm_stderr": 0.014346869060229328
},
"harness|hellaswag|10": {
"acc": 0.6179047998406691,
"acc_stderr": 0.004849065962692132,
"acc_norm": 0.8241386178052181,
"acc_norm_stderr": 0.003799241408502969
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4962962962962963,
"acc_stderr": 0.04319223625811331,
"acc_norm": 0.4962962962962963,
"acc_norm_stderr": 0.04319223625811331
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.039889037033362836,
"acc_norm": 0.5986842105263158,
"acc_norm_stderr": 0.039889037033362836
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6150943396226415,
"acc_stderr": 0.02994649856769995,
"acc_norm": 0.6150943396226415,
"acc_norm_stderr": 0.02994649856769995
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm_stderr": 0.04076663253918567
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_stderr": 0.03772446857518026,
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"acc_norm_stderr": 0.03772446857518026
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"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.047240073523838876
},
"harness|hendrycksTest-computer_security|5": {
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"acc_stderr": 0.04648231987117316,
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"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm": 0.4297872340425532,
"acc_norm_stderr": 0.03236214467715564
},
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"acc_norm_stderr": 0.04144311810878151
},
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"acc_norm_stderr": 0.04306241259127153
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"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.654320987654321,
"acc_norm_stderr": 0.026462487777001872
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.46099290780141844,
"acc_stderr": 0.02973659252642444,
"acc_norm": 0.46099290780141844,
"acc_norm_stderr": 0.02973659252642444
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4602346805736636,
"acc_stderr": 0.01272978538659857,
"acc_norm": 0.4602346805736636,
"acc_norm_stderr": 0.01272978538659857
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5698529411764706,
"acc_stderr": 0.030074971917302875,
"acc_norm": 0.5698529411764706,
"acc_norm_stderr": 0.030074971917302875
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5996732026143791,
"acc_stderr": 0.01982184368827176,
"acc_norm": 0.5996732026143791,
"acc_norm_stderr": 0.01982184368827176
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5959183673469388,
"acc_stderr": 0.031414708025865885,
"acc_norm": 0.5959183673469388,
"acc_norm_stderr": 0.031414708025865885
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.746268656716418,
"acc_stderr": 0.03076944496729602,
"acc_norm": 0.746268656716418,
"acc_norm_stderr": 0.03076944496729602
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"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": 0.7777777777777778,
"acc_stderr": 0.031885780176863984,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.031885780176863984
},
"harness|truthfulqa:mc|0": {
"mc1": 0.28151774785801714,
"mc1_stderr": 0.01574402724825605,
"mc2": 0.4191985438925104,
"mc2_stderr": 0.014270484892545822
}
}
```
### 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_yeontaek__Platypus2-13B-LoRa-v2
|
[
"region:us"
] |
2023-08-29T10:21:57+00:00
|
{"pretty_name": "Evaluation run of yeontaek/Platypus2-13B-LoRa-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [yeontaek/Platypus2-13B-LoRa-v2](https://huggingface.co/yeontaek/Platypus2-13B-LoRa-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_yeontaek__Platypus2-13B-LoRa-v2\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T11:20:59.240376](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2-13B-LoRa-v2/blob/main/results_2023-08-29T11%3A20%3A59.240376.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.571991245483798,\n \"acc_stderr\": 0.034294067141786025,\n \"acc_norm\": 0.5761375119651778,\n \"acc_norm_stderr\": 0.03427336583128381,\n \"mc1\": 0.28151774785801714,\n \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.4191985438925104,\n \"mc2_stderr\": 0.014270484892545822\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5563139931740614,\n \"acc_stderr\": 0.014518421825670444,\n \"acc_norm\": 0.5947098976109215,\n \"acc_norm_stderr\": 0.014346869060229328\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6179047998406691,\n \"acc_stderr\": 0.004849065962692132,\n \"acc_norm\": 0.8241386178052181,\n \"acc_norm_stderr\": 0.003799241408502969\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5986842105263158,\n \"acc_stderr\": 0.039889037033362836,\n \"acc_norm\": 0.5986842105263158,\n \"acc_norm_stderr\": 0.039889037033362836\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\": 0.6150943396226415,\n \"acc_stderr\": 0.02994649856769995,\n \"acc_norm\": 0.6150943396226415,\n \"acc_norm_stderr\": 0.02994649856769995\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04076663253918567\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n \"acc_stderr\": 0.03772446857518026,\n \"acc_norm\": 0.5722543352601156,\n \"acc_norm_stderr\": 0.03772446857518026\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.047240073523838876,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.047240073523838876\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.043727482902780064,\n \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.043727482902780064\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4482758620689655,\n \"acc_stderr\": 0.04144311810878151,\n \"acc_norm\": 0.4482758620689655,\n \"acc_norm_stderr\": 0.04144311810878151\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3386243386243386,\n \"acc_stderr\": 0.02437319786798306,\n \"acc_norm\": 0.3386243386243386,\n \"acc_norm_stderr\": 0.02437319786798306\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6709677419354839,\n \"acc_stderr\": 0.02672949906834996,\n \"acc_norm\": 0.6709677419354839,\n \"acc_norm_stderr\": 0.02672949906834996\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646836,\n \"acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646836\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\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.027309140588230172,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230172\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.031499305777849054,\n \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.031499305777849054\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7688073394495413,\n \"acc_stderr\": 0.01807575024163315,\n \"acc_norm\": 0.7688073394495413,\n \"acc_norm_stderr\": 0.01807575024163315\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159263,\n \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159263\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6591928251121076,\n \"acc_stderr\": 0.03181149747055359,\n \"acc_norm\": 0.6591928251121076,\n \"acc_norm_stderr\": 0.03181149747055359\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969637,\n \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969637\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n \"acc_stderr\": 0.04432804055291517,\n \"acc_norm\": 0.32142857142857145,\n \"acc_norm_stderr\": 0.04432804055291517\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\": 0.811965811965812,\n \"acc_stderr\": 0.025598193686652244,\n \"acc_norm\": 0.811965811965812,\n \"acc_norm_stderr\": 0.025598193686652244\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7611749680715197,\n \"acc_stderr\": 0.015246803197398682,\n \"acc_norm\": 0.7611749680715197,\n \"acc_norm_stderr\": 0.015246803197398682\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.025305258131879716,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.025305258131879716\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41675977653631285,\n \"acc_stderr\": 0.016489134962438954,\n \"acc_norm\": 0.41675977653631285,\n \"acc_norm_stderr\": 0.016489134962438954\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6241830065359477,\n \"acc_stderr\": 0.027732834353363947,\n \"acc_norm\": 0.6241830065359477,\n \"acc_norm_stderr\": 0.027732834353363947\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.662379421221865,\n \"acc_stderr\": 0.02685882587948854,\n \"acc_norm\": 0.662379421221865,\n \"acc_norm_stderr\": 0.02685882587948854\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.026462487777001872,\n \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.026462487777001872\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4602346805736636,\n \"acc_stderr\": 0.01272978538659857,\n \"acc_norm\": 0.4602346805736636,\n \"acc_norm_stderr\": 0.01272978538659857\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5698529411764706,\n \"acc_stderr\": 0.030074971917302875,\n \"acc_norm\": 0.5698529411764706,\n \"acc_norm_stderr\": 0.030074971917302875\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5996732026143791,\n \"acc_stderr\": 0.01982184368827176,\n \"acc_norm\": 0.5996732026143791,\n \"acc_norm_stderr\": 0.01982184368827176\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5959183673469388,\n \"acc_stderr\": 0.031414708025865885,\n \"acc_norm\": 0.5959183673469388,\n \"acc_norm_stderr\": 0.031414708025865885\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.746268656716418,\n \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.746268656716418,\n \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4397590361445783,\n \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.031885780176863984,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.031885780176863984\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.28151774785801714,\n \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.4191985438925104,\n \"mc2_stderr\": 0.014270484892545822\n }\n}\n```", "repo_url": "https://huggingface.co/yeontaek/Platypus2-13B-LoRa-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_08_29T11_20_59.240376", "path": ["**/details_harness|arc:challenge|25_2023-08-29T11:20:59.240376.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T11:20:59.240376.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": 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|
2023-08-29T10:22:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of yeontaek/Platypus2-13B-LoRa-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 yeontaek/Platypus2-13B-LoRa-v2 on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T11:20:59.240376:
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 yeontaek/Platypus2-13B-LoRa-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 yeontaek/Platypus2-13B-LoRa-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T11:20:59.240376:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 Card for Evaluation run of yeontaek/Platypus2-13B-LoRa-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 yeontaek/Platypus2-13B-LoRa-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T11:20:59.240376:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 yeontaek/Platypus2-13B-LoRa-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 yeontaek/Platypus2-13B-LoRa-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T11:20:59.240376:### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
35341e2d175aa6dcee3c403a0d4e057953ce6ac6
|
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<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="https://www.healthsupplement24x7.com/get-cinnachroma">✅<strong>Click Here To Visit – OFFICIAL WEBSITE</strong>✅</a></span></h3>
<p><strong>Short review on <a href="https://cinnachroma-reviews.blogspot.com/2023/08/cinnachroma-blood-sugar-support.html">CinnaChroma</a> :</strong> The prevalence of prediabetes looms large, affecting approximately 84 million individuals, posing a potential risk of progressing into full-fledged diabetes within a mere five-year timeframe. This escalating concern is exacerbated by the alarming statistic that over 114 million Americans stand either at the brink of Type 2 diabetes or are already grappling with its challenges.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiAc7OkzGYExa_6hwPzKr3iePyadlQ52jaoAaGRCsrm38czVRK5RchcslOWvZ_GjVuqxRPUdBB1cV807Wx3xMMNSsKGJ2jrN-orhJjAucBY5a_8WnG6L1vXY_0e4nAYJGzyOqU34xQscQTFXT0lwzdgSoaQDNn4SQQVoBCZvOxz9KNEAMCsOrk42n1Rwv_r/w640-h426/6-bottles.jpg" alt="" width="640" height="426" border="0" data-original-height="600" data-original-width="900" /></a></span></h2>
<p><a href="https://sites.google.com/view/cinnachroma-bloodsugar-formula/home">CinnaChroma</a> is a revolutionary dietary supplement formulated by Dr. Scott Saunders and Joe Barton. It is manufactured by Barton Nutrition.The formula used in <a href="https://lookerstudio.google.com/reporting/b40e7013-7cea-4468-9b35-1de94d474e62">CinnaChroma</a> is to support a healthy glucose metabolism to fully balance optimal blood sugar levels and significantly reduce type 2 diabetes.<a href="https://colab.research.google.com/drive/1UtY4GO9PpWQIwfyOUINonJQ947yEbQXz">CinnaChroma</a> is even formulated using ingredients that can help you manage your weight and get rid of fat in a safe and natural process.</p>
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<h2 style="text-align: left;"><strong>What is CinnaChroma?</strong></h2>
<p style="text-align: left;"><a href="https://cinnachroma-updates.clubeo.com/calendar/2023/08/28/cinnachroma-no-1-in-us-forces-to-quickly-decrease-blood-sugar-levels-without-causing-harmful-effects">CinnaChroma</a> emerges as a noteworthy nutritional supplement that holds the promise of facilitating glycemic regulation within the body. The manufacturer's claim asserts that this supplement integrates the well-established properties of cinnamon and chromium, both clinically recognized for their potential to reduce blood sugar levels, into a single, potent formulation. By combining the blood sugar-regulating prowess of these elements, <a href="https://cinnachroma-updates.clubeo.com">CinnaChroma</a> positions itself as a potential metabolic powerhouse harnessed from nature's resources.Central to <a href="https://cinnachroma-updates.clubeo.com/page/cinnachroma-new-updates-powerfully-helps-reduce-a1c-blood-sugar-spikes-in-people-with-type-2.html">CinnaChroma</a>'s composition is its foundation in cinnamon, renowned for its traditional role as a sugar blocker. This formulation, when coupled with the presence of five other specific nutrients scientifically demonstrated to aid in carbohydrate digestion and absorption, amplifies the supplement's potential benefits. This synergy between natural ingredients and contemporary medical insights creates a comprehensive approach to managing carbohydrate intake.</p>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgzsIFQUxm0hutZpaJZS_hxGYpf4wZuIRL3-PrAt2xDYzmatt2QqD_xygt7P9xco6Kt31aKAC_38PmG_xEL-yf7x3Fg0MUm671RrGUEt7z3k316ecMOFEHtDkdDYckgmx5zk5Mdiz9Hd4DZNfb6yhYgcFBDpKIouL-Y7clWSw6KdQ2DFKOe2iwQRbnRw42o/w640-h480/CinnaChroma%209.png" alt="" width="640" height="480" border="0" data-original-height="1050" data-original-width="1400" /></a></h2>
<p style="text-align: left;">When adhering to the recommended usage guidelines, <a href="https://huggingface.co/datasets/cinnachromacapsule/cinnachroma/blob/main/README.md">CinnaChroma</a> offers the enticing prospect of indulging in a broader array of foods without harboring concerns about triggering diabetes. Beyond merely facilitating carbohydrate digestion, the unique amalgamation of specific elements within <a href="https://huggingface.co/cinnachromacapsule/cinnachroma/blob/main/README.md">CinnaChroma</a> is designed to optimize sugar profile management. This multifaceted approach addresses not only the digestion of carbs but also the metabolism of sweets and junk foods.</p>
<h2 style="text-align: left;"><strong>Who Created CinnaChroma?</strong></h2>
<p style="text-align: left;">Joe Barton created <a href="https://cinnachromareviews.hashnode.dev/cinnachroma">CinnaChroma</a> from Barton Nutrition and Dr. Scott Saunders. Joe and Dr. Saunders Dr. Saunders say they think the same. They want to help people find alternative medicinal support for their diseases or reduce their risk of developing them. In addition, they say that they want to help as many people as possible by putting their knowledge and studies to work. And for this reason, they spend many nights studying research trials and developing natural formulas that use ingredients from all corners of the world.</p>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtm9ren99MNJag8dxsWebZJcttmmu6KKzDOhIA_k2DwxV5-lhtqzFyq1z2AxL4DKTu4qxcQUh0cfKiXWiajZJ6ZOm914ovfTaCuyrM49eJmohQ2M0a2ln2Bel6griqNv-ELsNuJ3oIpjcKLph09nu8byVB5B5205J6n2BPJ3GUdF4RdMXVMAquKSlQR9Aw/w640-h316/CinnaChroma%206.jpg" alt="" width="640" height="316" border="0" data-original-height="733" data-original-width="1487" /></a></h2>
<h2 style="text-align: left;"><strong>How does CinnaChroma work?</strong></h2>
<p style="text-align: left;"><a href="https://devfolio.co/@cinnachromaus">CinnaChroma</a> operates through a multi-faceted mechanism that leverages the individual and combined properties of its key ingredients, cinnamon and chromium, alongside other supporting nutrients. The supplement's working principle revolves around facilitating glycemic regulation, optimizing sugar profiles, and aiding in the management of carbohydrate intake. Here's a breakdown of how <a href="https://pdfhost.io/v/esXX8lq.P_CinnaChroma_New_Updates_Powerfully_Helps_Reduce_A1C_and_Blood_Sugar_Spikes_in_People_With_Type_2">CinnaChroma</a> works:</p>
<ol style="text-align: left;">
<li><strong>Blood Sugar Regulation with Cinnamon: </strong>Cinnamon, a well-known traditional spice, has been linked to blood sugar regulation. It contains compounds that enhance insulin sensitivity, potentially leading to improved glucose utilization by cells. This can result in more stable blood sugar levels after meals.</li>
<li><strong>Chromium's Role in Insulin Function:</strong> Chromium, an essential trace mineral, plays a role in enhancing insulin's effectiveness in the body. Insulin is a hormone that helps regulate blood sugar levels by facilitating the uptake of glucose into cells for energy.</li>
<li><strong>Carbohydrate Digestion and Absorption: </strong><a href="https://cinnachroma-reviews.company.site/">CinnaChroma</a>'s formulation includes five other nutrients that support the digestion and absorption of carbohydrates. These nutrients can assist the body in efficiently breaking down complex carbohydrates into simpler sugars, allowing for smoother absorption and preventing rapid spikes in blood sugar levels.</li>
<li><strong>Sugar Profile Management: </strong>The supplement's unique blend of ingredients contributes to optimizing sugar profiles. By combining cinnamon's potential to modulate post-meal blood sugar spikes with chromium's role in insulin enhancement, <a href="https://cinnachroma-official.jimdosite.com/">CinnaChroma</a> aims to create a balanced environment for blood sugar management.</li>
<li><strong>Indulgence without Concern: </strong><a href="https://soundcloud.com/cinnachroma-550145605/advance-blood-sugar-supporter-supports-healthy-blood-glicose-metabolism-in-body">CinnaChroma</a>'s comprehensive approach offers users the possibility to consume a wider range of foods, including carbohydrates, sweets, and junk foods, with reduced apprehension about their impact on blood sugar levels.</li>
<li><strong>Quality Manufacturing: </strong>The supplement is manufactured in an FDA-regulated facility in the United States, ensuring that the highest quality and safety standards are maintained during production.</li>
</ol>
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<h2 style="text-align: left;"><strong>Ingredients in CinnaChroma.</strong></h2>
<p style="text-align: left;"><a href="https://www.ivoox.com/cinnachroma-new-updates-is-it-really-works-audios-mp3_rf_115028023_1.html">CinnaChroma</a> is meticulously crafted with a selection of natural vitamins and minerals, designed to seamlessly integrate into diverse lifestyles and wellness routines. Developed in collaboration with Barton Nutrition's esteemed medical and nutrition advisor, Dr. Scott Saunders, <a href="https://cinnachroma-official.bandcamp.com/track/cinnachroma-no-1-in-us-forces-to-quickly-decrease-blood-sugar-levels-without-causing-harmful-effects">CinnaChroma</a> harmonizes the potent properties of cinnamon bark extract with other health-enhancing components to effectively manage glucose levels. Several key ingredients within <a href="https://www.podcasts.com/cinnachroma-official">CinnaChroma</a> contribute to its health-promoting attributes, each offering specific benefits:</p>
<p style="text-align: left;"><strong>Cinnamon Bark : </strong>Cinnamon Bark stands out for its effectiveness in reducing the risk factors associated with diabetes and cardiovascular diseases. A clinical trial published in Diabetes Care in 2003 underscored that cassia cinnamon yields favorable outcomes by decreasing blood glucose and cholesterol levels in individuals with type 2 diabetes. Consumed in moderation, cinnamon bark proves advantageous for overall health.</p>
<p style="text-align: left;"><strong>Chromium : </strong>Chromium supplementation potentially assists diabetics in their quest to lower blood sugar levels. Among various forms of chromium supplements, chromium picolinate emerges as the most effective. While research indicates that chromium can indeed lower glucose levels and enhance insulin sensitivity, it's noteworthy that not all studies have consistently demonstrated this benefit.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiEzvUY0YuBwg8fphsSkNIimG9IlSe8YqqlOHkAsij2KUykYTnsHOB_GPv64XNwC5AMHuDUanpfiUlFkWBRLTNfLKgMZxYM03taoyip22W0TJhj5HbGNjTxYpqV5iraWmV16KVhMX2QEQPR8hwD1KIRopD8JyI1Jwuc2wtHF0wF9pRGRUZm3oS4Njk6c4mj/w640-h302/CinnaChroma%204.png" alt="" width="640" height="302" border="0" data-original-height="661" data-original-width="1400" /></a></span></h2>
<p style="text-align: left;"><strong>Vanadium : </strong>Vanadium offers potential relief for diabetic neuropathy and mitigating pain arising from free radical damage. Supported by animal studies and limited human trials, vanadium exhibits the capacity to reduce blood sugar levels and bolster insulin sensitivity in type 2 diabetics. In a specific study involving individuals with type 2 diabetes, vanadium showcased its ability to reduce both total and LDL cholesterol levels.</p>
<p style="text-align: left;"><strong>Selenium : </strong>Selenium, an indispensable trace element, plays a pivotal role in the intricate defense mechanism against oxidative stress. The antioxidant attributes of selenium have the potential to inhibit the progression of diabetes. Existing evidence suggests that maintaining appropriate selenium levels is essential for facilitating insulin secretion.</p>
<p style="text-align: left;"><strong>Vitamin-K2 : </strong>Clinically recognized for its role in blood clotting, Vitamin K also offers intriguing insights into diabetes management. A series of human studies have spotlighted the capacity of vitamin K2 supplementation to improve insulin sensitivity. Furthermore, vitamin K2 supplementation has demonstrated the capability to lower the risk of developing diabetes.</p>
<h2 style="text-align: left;"><strong>Benefits of CinnaChroma .</strong></h2>
<p style="text-align: left;"><a href="https://www.deviantart.com/cinnachromacapsule/art/CinnaChroma-New-Updates-2023-Legit-or-hoax-979599933">CinnaChroma</a> offers a range of potential benefits attributed to its carefully curated blend of natural ingredients. These benefits align with the supplement's goal of promoting glycemic regulation and supporting overall well-being. Here are some key advantages associated with using CinnaChroma:</p>
<ul style="text-align: left;">
<li style="text-align: left;"><strong>Glycemic Regulation:</strong> <a href="https://haitiliberte.com/advert/cinnachroma-new-updates-powerfully-helps-reduce-a1c-blood-sugar-spikes-in-people-with-type-2/">CinnaChroma</a>'s core objective is to facilitate glycemic regulation, helping to maintain stable blood sugar levels. The inclusion of cinnamon and chromium, along with other nutrients, is designed to support this crucial aspect of metabolic health.</li>
<li style="text-align: left;"><strong>Blood Sugar Management: </strong>The supplement's formulation, which includes cinnamon bark extract, chromium, and other nutrients, aims to assist in managing blood sugar levels. This could potentially be valuable for individuals with prediabetes, Type 2 diabetes, or those aiming to prevent such conditions.</li>
<li style="text-align: left;"><strong>Carbohydrate Digestion:</strong> <a href="https://community.weddingwire.in/forum/cinnachroma-price-updates-2023-provides-you-positive-body-mechanism-to-supports-best-blood-sugar-levels--t143426">CinnaChroma</a>'s unique blend of ingredients supports efficient carbohydrate digestion. This can aid in breaking down complex carbohydrates into simpler sugars, potentially preventing rapid spikes in blood sugar levels.</li>
<li style="text-align: left;"><strong>Insulin Sensitivity: </strong>The inclusion of chromium and other components that enhance insulin sensitivity can contribute to more effective utilization of glucose by cells. This, in turn, may help in managing blood sugar levels and reducing the risk of insulin resistance.</li>
</ul>
<p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjuT4uEbltx4lzh1abpuU2FlKnBU6IRjk4qo5-ODHbK9aQvuyFPf4gzpKIvsI8THGhYpWUPxkNV8-IIR6TjqWYUxuWZ8aSrM6uMsCQ1igUXtH1Bz2JyUP0IhChUxzlEhWtz6-1IRapFagQxjP_3ciJ3lDsaWQuaqnnd-uTKFL3d3zhti8VTp7AdDP52EN4l/w640-h640/CinnaChroma%208.png" alt="" width="640" height="640" border="0" data-original-height="1400" data-original-width="1400" /></a></p>
<ul style="text-align: left;">
<li style="text-align: left;"><strong>Metabolic Support: </strong>The synergistic action of the various ingredients in <a href="https://www.weddingwire.com/wedding-forums/cinnachroma-advance-blood-sugar-supporter-supports-healthy-blood-glicose-metabolism-in-body/caeb5a61144907aa.html">CinnaChroma</a> may provide comprehensive metabolic support, contributing to the efficient utilization of nutrients and energy.</li>
<li style="text-align: left;"><strong>Antioxidant Protection: </strong>Certain ingredients, such as selenium, offer antioxidant qualities that can help protect cells from oxidative stress. This protection is particularly valuable in mitigating the risk factors associated with diabetes and related complications.</li>
<li style="text-align: left;"><strong>Potential Cardiovascular Benefits: </strong>The reduction in cholesterol levels associated with certain components, like cinnamon bark extract and vanadium, could contribute to improved cardiovascular health, which is often linked to diabetes management.</li>
<li style="text-align: left;"><strong>Holistic Approach to Diabetes Management: </strong><a href="https://forums.hitched.co.uk/chat/forums/thread/cinnachromaprice-updates-2023-provides-you-positive-body-mechanism-to-supports-best-blood-sugar-levels-1115611/">CinnaChroma</a>'s composition is aimed at tackling various facets of diabetes management, including blood sugar regulation, insulin sensitivity, and carbohydrate metabolism. This holistic approach aligns with the multifaceted nature of diabetes care.</li>
<li style="text-align: left;"><strong>Natural Ingredients: </strong>The reliance on natural ingredients, coupled with the absence of unwanted fillers or byproducts, underlines the supplement's commitment to offering benefits without undesirable side effects.</li>
<li style="text-align: left;"><strong>Expert Collaboration:</strong> The involvement of medical and nutrition experts in the development of <a href="https://medium.com/@cinnachromacapsule/cinnachroma-advance-blood-sugar-supporter-supports-healthy-blood-glicose-metabolism-in-body-4e5583d18a1f">CinnaChroma</a>, including Dr. Scott Saunders from Barton Nutrition, lends credibility to its potential benefits and safety.</li>
</ul>
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<h2><strong>Pros of CinnaChroma:</strong></h2>
<ul>
<li data-aria-level="1" data-aria-posinset="1" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>The groundbreaking supplement can significantly reduce the risk of obesity, type 2 diabetes, and other heart diseases.</strong></li>
<li data-aria-level="1" data-aria-posinset="2" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><a href="https://sketchfab.com/3d-models/cinnachroma-official-reviews-usa-legit-or-hoax-b30685690aae4f169d0745d9b5ff7646">CinnaChroma</a> can fully regulate your blood sugar levels and also maintain healthy levels of blood pressure and cholesterol.</li>
<li data-aria-level="1" data-aria-posinset="3" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>It can increase your insulin production and sensitivity while decreasing your insulin resistance.</strong></li>
<li data-aria-level="1" data-aria-posinset="4" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4">The <a href="https://www.remotehub.com/cinnachroma.capsule">CinnaChroma</a> can improve blood circulation throughout the body.</li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>It contains rich amounts of antioxidants that eliminate free radicals, oxidative stress, and other toxic pollutants.</strong></li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4">CinnaChroma can speed up the anti-inflammatory response of the body.</li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>It can help you lose weight by getting rid of the fat buildup, especially in the stubborn places of your body.</strong></li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4">CinnaChroma increases and fully supports glucose metabolism.</li>
</ul>
<h2><strong>Recommended dosage of <a href="https://rentry.co/cinnachroma-official">CinnaChroma</a>.<br /></strong></h2>
<p><a href="https://hackmd.io/@cinnachromacapsule/CinnaChroma">CinnaChroma</a> comes in convenient 30-day supply bottles, simplifying adherence to users health routine. To enjoy the benefits, take one capsule daily. It's crucial not to exceed the recommended dosage, as overconsumption could have detrimental health effects. Individuals who have experienced negative reactions to herbal supplements in the past should refrain from taking this supplement.CinnaChroma is not suitable for individuals under the age of 18. Pregnant or nursing women should avoid this supplement, as it may pose risks to their health.</p>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj9PKW0WDvK9rIuIq2UyWyDQL2Jw4P5_xaidn5dmXEVGCM8O3f6ENMYPDzOvv9TWCQuWWxaWWi9qvfuJTzgPv3bNn4K2piqghx4_IoLv0xikbahIc-Dl1fAjxnlTx4SmSOuRrSdNWkVMX_Ul4nXIM19VXRP8ODlcuSVstYsUNKhQa-1brpH2oQEjDQlDINT/w640-h250/CinnaChroma%201.jpg" alt="" width="640" height="250" border="0" data-original-height="1062" data-original-width="2727" /></a></h2>
<p>If users are currently taking any over-the-counter medications or have underlying medical conditions, it is advised to abstain from using this supplement.Prior to incorporating this dietary supplement into ones routine, seeking consultation with a healthcare professional is recommended. This precaution ensures that potential adverse reactions are minimized and ones health remains a priority.</p>
<h2><strong>Detail Pricing of <a href="https://sites.google.com/view/cinnachroma-capsules/home">CinnaChroma</a>.</strong></h2>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzqZuIx6TPW-zBi9OO5IgBybBByJ_Ew3uE3e3dh7l6o5eZjbD4KkgAOwW2yHbtWz3O3cR6YUDt5pHzI3lAHCP2ykx0iy9Vfddc1TgP796mqHk-Uv_-H0bbyDW9Vak28RT5vL_uxbkbz7GSqs1d8w0fj85i2EZzVyKGEr4kkCglSTb037IiWjaiCHeoO4Ve/w640-h436/Screenshot%20(534).png" alt="" width="640" height="436" border="0" data-original-height="980" data-original-width="1436" /></a></h2>
<ul>
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</ul>
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<h3><strong>Free Bonus Is:</strong></h3>
<p>The Blood Sugar Solution Kit is Barton Publishing's #1 Top Selling Blood Sugar Support Program.Created under Dr. Scott Saunders, MD's guidance, this system helps balance blood sugar, support A1C, and combat erratic blood sugar's root-causes. With its easy-to-follow simple instructions, this Solution Kit has helped over half a million folks get off the regular “run of the mill” solutions for erratic blood sugar and enjoy stable blood sugar levels.Over half a million amazed users have made The Barton Blood Sugar Solution Kit our #1 most popular product in Barton Publishing's history!And you'll clearly see why since this proven blood sugar support program is broken down into manageable, progressive phases that anyone with high blood sugar can use.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMzqbR_EWu3EXemXqPmaYdHOWojr_ex8p1zOflsn8FdHRijr4rHkylfJKX8KjmGt_VD1WzeyuCyqe6KTaYCt_BGkxKWN0DD5Wl_NE6V9qs5-iusjdYNcuWQ2OMQCtV74Hty6nCvMR7klYrlPOiDCLxZaHdhmtN5tgHt5HCFl2t3XcHQJLKVPk80TAN6r32/w640-h390/Screenshot%20(535).png" alt="" width="640" height="390" border="0" data-original-height="731" data-original-width="1200" /></a></span></h2>
<h2><strong>Money back guarantee on <a href="https://cinnachroma-offers.clubeo.com">CinnaChroma</a>.</strong></h2>
<p>Here's how it works: Go ahead and claim our best deal ever. Take full advantage of our exclusive 3 or 6 month supply bundle, where you can save up to $360 right now. And when you do, you'll have a full 365 days to try it out - risk-free.And if for any reason you don't see or feel the results you demand and deserve, or even if you just don't like the color of the bottle... whatever the reason, simply return it within 365 days for a full refund.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-cinnachroma"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgNxiY4KUzaE6B-LB5omteweDWwbuaaTTBQBHHHg6-e-rno_FTsqnvgZS0_cT3sfRaeP1KV6Ze3HvipuQHVdpDwqLlG-jwvKxeMiZJcLRIflJ7mciUjGupEcKGWMOtu8U0IkO_qgetNt5JaNZUgiZ4lGcwqlwMGbjZTi8jg5tlrdQ0pVRvk155ITk8ee4qP/w640-h456/CinnaChroma%2011.png" alt="" width="640" height="456" border="0" data-original-height="422" data-original-width="591" /></a></span></h2>
<h2><strong>Final words on CinnaChroma.</strong></h2>
<p>CinnaChroma is an excellent option for individuals dealing with Type 2 diabetes. It combines a potent blend of compounds that purify the body of harmful toxins and amplify insulin sensitivity. This results in an enhanced supply of oxygen in the bloodstream, leading to heightened vitality throughout the day.Consistent consumption of CinnaChroma prompts the liver to generate insulin more efficiently, aiding in the removal of glucose from the bloodstream. Additionally, it optimizes and elevates the efficiency of the body's metabolic processes. The result is an increase in daytime caloric expenditure, contributing to weight loss and an augmented level of energy.</p>
<h2 style="text-align: center;"><strong><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="https://www.healthsupplement24x7.com/get-cinnachroma">➦➦Visit the Official Website Today and Grab Your Bottle!</a></span></strong></h2>
<p><a href="https://cinnachromareviews.blogspot.com/2023/08/cinnachroma.html">https://cinnachromareviews.blogspot.com/2023/08/cinnachroma.html</a></p>
<p><a href="https://cinnachroma-reviews.blogspot.com/2023/08/cinnachroma-blood-sugar-support.html">https://cinnachroma-reviews.blogspot.com/2023/08/cinnachroma-blood-sugar-support.html</a></p>
<p><a href="https://sites.google.com/view/cinnachroma-bloodsugar-formula/home">https://sites.google.com/view/cinnachroma-bloodsugar-formula/home</a></p>
<p><a href="https://lookerstudio.google.com/reporting/b40e7013-7cea-4468-9b35-1de94d474e62">https://lookerstudio.google.com/reporting/b40e7013-7cea-4468-9b35-1de94d474e62</a></p>
<p><a href="https://colab.research.google.com/drive/1UtY4GO9PpWQIwfyOUINonJQ947yEbQXz">https://colab.research.google.com/drive/1UtY4GO9PpWQIwfyOUINonJQ947yEbQXz</a></p>
<p><a href="https://cinnachroma-updates.clubeo.com/calendar/2023/08/28/cinnachroma-no-1-in-us-forces-to-quickly-decrease-blood-sugar-levels-without-causing-harmful-effects">https://cinnachroma-updates.clubeo.com/calendar/2023/08/28/cinnachroma-no-1-in-us-forces-to-quickly-decrease-blood-sugar-levels-without-causing-harmful-effects</a></p>
<p><a href="https://cinnachroma-updates.clubeo.com">https://cinnachroma-updates.clubeo.com</a></p>
<p><a href="https://huggingface.co/datasets/cinnachromacapsule/cinnachroma/blob/main/README.md">https://huggingface.co/datasets/cinnachromacapsule/cinnachroma/blob/main/README.md</a></p>
<p><a href="https://cinnachromareviews.hashnode.dev/cinnachroma">https://cinnachromareviews.hashnode.dev/cinnachroma</a></p>
<p><a href="https://devfolio.co/@cinnachromaus">https://devfolio.co/@cinnachromaus</a></p>
<p><a href="https://pdfhost.io/v/esXX8lq.P_CinnaChroma_New_Updates_Powerfully_Helps_Reduce_A1C_and_Blood_Sugar_Spikes_in_People_With_Type_2">https://pdfhost.io/v/esXX8lq.P_CinnaChroma_New_Updates_Powerfully_Helps_Reduce_A1C_and_Blood_Sugar_Spikes_in_People_With_Type_2</a></p>
<p><a href="https://cinnachroma-reviews.company.site/">https://cinnachroma-reviews.company.site/</a></p>
<p><a href="https://cinnachroma-official.jimdosite.com/">https://cinnachroma-official.jimdosite.com/</a></p>
<p><a href="https://soundcloud.com/cinnachroma-550145605/advance-blood-sugar-supporter-supports-healthy-blood-glicose-metabolism-in-body">https://soundcloud.com/cinnachroma-550145605/advance-blood-sugar-supporter-supports-healthy-blood-glicose-metabolism-in-body</a></p>
<p><a href="https://www.ivoox.com/cinnachroma-new-updates-is-it-really-works-audios-mp3_rf_115028023_1.html">https://www.ivoox.com/cinnachroma-new-updates-is-it-really-works-audios-mp3_rf_115028023_1.html</a></p>
<p><a href="https://cinnachroma-official.bandcamp.com/track/cinnachroma-no-1-in-us-forces-to-quickly-decrease-blood-sugar-levels-without-causing-harmful-effects">https://cinnachroma-official.bandcamp.com/track/cinnachroma-no-1-in-us-forces-to-quickly-decrease-blood-sugar-levels-without-causing-harmful-effects</a></p>
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cinnachromausa/cinnachroma
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2023-08-29T10:37:59+00:00
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<h1><strong><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL – Official Website Link – Click Here</a></span></strong></h1>
<p><strong> Product Name - <a href="URL /></strong></p>
<p><strong> Quantity Per Bottle - 30 Capsules/Jar<br /></strong></p>
<p><strong> Category - Blood Sugar Support<br /></strong></p>
<p><strong> Compostion - Natural Components Only</strong></p>
<p><strong> Results - In Few Days</strong></p>
<p><strong> Availability – Official Website <span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL
<p><strong> Rating: - 4.8/5.0 </strong></p>
<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here To Visit – OFFICIAL WEBSITE</strong></a></span></h3>
<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here To Visit – OFFICIAL WEBSITE</strong></a></span></h3>
<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here To Visit – OFFICIAL WEBSITE</strong></a></span></h3>
<p><strong>Short review on <a href="URL :</strong> The prevalence of prediabetes looms large, affecting approximately 84 million individuals, posing a potential risk of progressing into full-fledged diabetes within a mere five-year timeframe. This escalating concern is exacerbated by the alarming statistic that over 114 million Americans stand either at the brink of Type 2 diabetes or are already grappling with its challenges.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="426" border="0" data-original-height="600" data-original-width="900" /></a></span></h2>
<p><a href="URL is a revolutionary dietary supplement formulated by Dr. Scott Saunders and Joe Barton. It is manufactured by Barton Nutrition.The formula used in <a href="URL is to support a healthy glucose metabolism to fully balance optimal blood sugar levels and significantly reduce type 2 diabetes.<a href="URL is even formulated using ingredients that can help you manage your weight and get rid of fat in a safe and natural process.</p>
<h2 style="text-align: center;"><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL The Official Website To Get CinnaChroma Now!</strong></a></span></h2>
<h2 style="text-align: left;"><strong>What is CinnaChroma?</strong></h2>
<p style="text-align: left;"><a href="URL emerges as a noteworthy nutritional supplement that holds the promise of facilitating glycemic regulation within the body. The manufacturer's claim asserts that this supplement integrates the well-established properties of cinnamon and chromium, both clinically recognized for their potential to reduce blood sugar levels, into a single, potent formulation. By combining the blood sugar-regulating prowess of these elements, <a href="URL">CinnaChroma</a> positions itself as a potential metabolic powerhouse harnessed from nature's resources.Central to <a href="URL composition is its foundation in cinnamon, renowned for its traditional role as a sugar blocker. This formulation, when coupled with the presence of five other specific nutrients scientifically demonstrated to aid in carbohydrate digestion and absorption, amplifies the supplement's potential benefits. This synergy between natural ingredients and contemporary medical insights creates a comprehensive approach to managing carbohydrate intake.</p>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="480" border="0" data-original-height="1050" data-original-width="1400" /></a></h2>
<p style="text-align: left;">When adhering to the recommended usage guidelines, <a href="URL offers the enticing prospect of indulging in a broader array of foods without harboring concerns about triggering diabetes. Beyond merely facilitating carbohydrate digestion, the unique amalgamation of specific elements within <a href="URL is designed to optimize sugar profile management. This multifaceted approach addresses not only the digestion of carbs but also the metabolism of sweets and junk foods.</p>
<h2 style="text-align: left;"><strong>Who Created CinnaChroma?</strong></h2>
<p style="text-align: left;">Joe Barton created <a href="URL from Barton Nutrition and Dr. Scott Saunders. Joe and Dr. Saunders Dr. Saunders say they think the same. They want to help people find alternative medicinal support for their diseases or reduce their risk of developing them. In addition, they say that they want to help as many people as possible by putting their knowledge and studies to work. And for this reason, they spend many nights studying research trials and developing natural formulas that use ingredients from all corners of the world.</p>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="316" border="0" data-original-height="733" data-original-width="1487" /></a></h2>
<h2 style="text-align: left;"><strong>How does CinnaChroma work?</strong></h2>
<p style="text-align: left;"><a href="URL operates through a multi-faceted mechanism that leverages the individual and combined properties of its key ingredients, cinnamon and chromium, alongside other supporting nutrients. The supplement's working principle revolves around facilitating glycemic regulation, optimizing sugar profiles, and aiding in the management of carbohydrate intake. Here's a breakdown of how <a href="URL works:</p>
<ol style="text-align: left;">
<li><strong>Blood Sugar Regulation with Cinnamon: </strong>Cinnamon, a well-known traditional spice, has been linked to blood sugar regulation. It contains compounds that enhance insulin sensitivity, potentially leading to improved glucose utilization by cells. This can result in more stable blood sugar levels after meals.</li>
<li><strong>Chromium's Role in Insulin Function:</strong> Chromium, an essential trace mineral, plays a role in enhancing insulin's effectiveness in the body. Insulin is a hormone that helps regulate blood sugar levels by facilitating the uptake of glucose into cells for energy.</li>
<li><strong>Carbohydrate Digestion and Absorption: </strong><a href="URL formulation includes five other nutrients that support the digestion and absorption of carbohydrates. These nutrients can assist the body in efficiently breaking down complex carbohydrates into simpler sugars, allowing for smoother absorption and preventing rapid spikes in blood sugar levels.</li>
<li><strong>Sugar Profile Management: </strong>The supplement's unique blend of ingredients contributes to optimizing sugar profiles. By combining cinnamon's potential to modulate post-meal blood sugar spikes with chromium's role in insulin enhancement, <a href="URL aims to create a balanced environment for blood sugar management.</li>
<li><strong>Indulgence without Concern: </strong><a href="URL comprehensive approach offers users the possibility to consume a wider range of foods, including carbohydrates, sweets, and junk foods, with reduced apprehension about their impact on blood sugar levels.</li>
<li><strong>Quality Manufacturing: </strong>The supplement is manufactured in an FDA-regulated facility in the United States, ensuring that the highest quality and safety standards are maintained during production.</li>
</ol>
<h2 style="text-align: center;"><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL your CinnaChroma Here & Get Great Discount!</strong></a></span></h2>
<h2 style="text-align: left;"><strong>Ingredients in CinnaChroma.</strong></h2>
<p style="text-align: left;"><a href="URL is meticulously crafted with a selection of natural vitamins and minerals, designed to seamlessly integrate into diverse lifestyles and wellness routines. Developed in collaboration with Barton Nutrition's esteemed medical and nutrition advisor, Dr. Scott Saunders, <a href="URL harmonizes the potent properties of cinnamon bark extract with other health-enhancing components to effectively manage glucose levels. Several key ingredients within <a href="URL contribute to its health-promoting attributes, each offering specific benefits:</p>
<p style="text-align: left;"><strong>Cinnamon Bark : </strong>Cinnamon Bark stands out for its effectiveness in reducing the risk factors associated with diabetes and cardiovascular diseases. A clinical trial published in Diabetes Care in 2003 underscored that cassia cinnamon yields favorable outcomes by decreasing blood glucose and cholesterol levels in individuals with type 2 diabetes. Consumed in moderation, cinnamon bark proves advantageous for overall health.</p>
<p style="text-align: left;"><strong>Chromium : </strong>Chromium supplementation potentially assists diabetics in their quest to lower blood sugar levels. Among various forms of chromium supplements, chromium picolinate emerges as the most effective. While research indicates that chromium can indeed lower glucose levels and enhance insulin sensitivity, it's noteworthy that not all studies have consistently demonstrated this benefit.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="302" border="0" data-original-height="661" data-original-width="1400" /></a></span></h2>
<p style="text-align: left;"><strong>Vanadium : </strong>Vanadium offers potential relief for diabetic neuropathy and mitigating pain arising from free radical damage. Supported by animal studies and limited human trials, vanadium exhibits the capacity to reduce blood sugar levels and bolster insulin sensitivity in type 2 diabetics. In a specific study involving individuals with type 2 diabetes, vanadium showcased its ability to reduce both total and LDL cholesterol levels.</p>
<p style="text-align: left;"><strong>Selenium : </strong>Selenium, an indispensable trace element, plays a pivotal role in the intricate defense mechanism against oxidative stress. The antioxidant attributes of selenium have the potential to inhibit the progression of diabetes. Existing evidence suggests that maintaining appropriate selenium levels is essential for facilitating insulin secretion.</p>
<p style="text-align: left;"><strong>Vitamin-K2 : </strong>Clinically recognized for its role in blood clotting, Vitamin K also offers intriguing insights into diabetes management. A series of human studies have spotlighted the capacity of vitamin K2 supplementation to improve insulin sensitivity. Furthermore, vitamin K2 supplementation has demonstrated the capability to lower the risk of developing diabetes.</p>
<h2 style="text-align: left;"><strong>Benefits of CinnaChroma .</strong></h2>
<p style="text-align: left;"><a href="URL offers a range of potential benefits attributed to its carefully curated blend of natural ingredients. These benefits align with the supplement's goal of promoting glycemic regulation and supporting overall well-being. Here are some key advantages associated with using CinnaChroma:</p>
<ul style="text-align: left;">
<li style="text-align: left;"><strong>Glycemic Regulation:</strong> <a href="URL core objective is to facilitate glycemic regulation, helping to maintain stable blood sugar levels. The inclusion of cinnamon and chromium, along with other nutrients, is designed to support this crucial aspect of metabolic health.</li>
<li style="text-align: left;"><strong>Blood Sugar Management: </strong>The supplement's formulation, which includes cinnamon bark extract, chromium, and other nutrients, aims to assist in managing blood sugar levels. This could potentially be valuable for individuals with prediabetes, Type 2 diabetes, or those aiming to prevent such conditions.</li>
<li style="text-align: left;"><strong>Carbohydrate Digestion:</strong> <a href="URL unique blend of ingredients supports efficient carbohydrate digestion. This can aid in breaking down complex carbohydrates into simpler sugars, potentially preventing rapid spikes in blood sugar levels.</li>
<li style="text-align: left;"><strong>Insulin Sensitivity: </strong>The inclusion of chromium and other components that enhance insulin sensitivity can contribute to more effective utilization of glucose by cells. This, in turn, may help in managing blood sugar levels and reducing the risk of insulin resistance.</li>
</ul>
<p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="640" border="0" data-original-height="1400" data-original-width="1400" /></a></p>
<ul style="text-align: left;">
<li style="text-align: left;"><strong>Metabolic Support: </strong>The synergistic action of the various ingredients in <a href="URL may provide comprehensive metabolic support, contributing to the efficient utilization of nutrients and energy.</li>
<li style="text-align: left;"><strong>Antioxidant Protection: </strong>Certain ingredients, such as selenium, offer antioxidant qualities that can help protect cells from oxidative stress. This protection is particularly valuable in mitigating the risk factors associated with diabetes and related complications.</li>
<li style="text-align: left;"><strong>Potential Cardiovascular Benefits: </strong>The reduction in cholesterol levels associated with certain components, like cinnamon bark extract and vanadium, could contribute to improved cardiovascular health, which is often linked to diabetes management.</li>
<li style="text-align: left;"><strong>Holistic Approach to Diabetes Management: </strong><a href="URL composition is aimed at tackling various facets of diabetes management, including blood sugar regulation, insulin sensitivity, and carbohydrate metabolism. This holistic approach aligns with the multifaceted nature of diabetes care.</li>
<li style="text-align: left;"><strong>Natural Ingredients: </strong>The reliance on natural ingredients, coupled with the absence of unwanted fillers or byproducts, underlines the supplement's commitment to offering benefits without undesirable side effects.</li>
<li style="text-align: left;"><strong>Expert Collaboration:</strong> The involvement of medical and nutrition experts in the development of <a href="URL including Dr. Scott Saunders from Barton Nutrition, lends credibility to its potential benefits and safety.</li>
</ul>
<h2 style="text-align: center;"><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here Your CinnaChroma & Grab The Big Discount Right Now!</strong></a></span></h2>
<h2><strong>Pros of CinnaChroma:</strong></h2>
<ul>
<li data-aria-level="1" data-aria-posinset="1" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>The groundbreaking supplement can significantly reduce the risk of obesity, type 2 diabetes, and other heart diseases.</strong></li>
<li data-aria-level="1" data-aria-posinset="2" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><a href="URL can fully regulate your blood sugar levels and also maintain healthy levels of blood pressure and cholesterol.</li>
<li data-aria-level="1" data-aria-posinset="3" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>It can increase your insulin production and sensitivity while decreasing your insulin resistance.</strong></li>
<li data-aria-level="1" data-aria-posinset="4" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4">The <a href="URL can improve blood circulation throughout the body.</li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>It contains rich amounts of antioxidants that eliminate free radicals, oxidative stress, and other toxic pollutants.</strong></li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4">CinnaChroma can speed up the anti-inflammatory response of the body.</li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4"><strong>It can help you lose weight by getting rid of the fat buildup, especially in the stubborn places of your body.</strong></li>
<li data-aria-level="1" data-aria-posinset="5" data-font="Calibri" data-leveltext="●" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769242":[8226],"469777803":"left","469777804":"●","469777815":"multilevel"}" data-listid="4">CinnaChroma increases and fully supports glucose metabolism.</li>
</ul>
<h2><strong>Recommended dosage of <a href="URL /></strong></h2>
<p><a href="URL comes in convenient 30-day supply bottles, simplifying adherence to users health routine. To enjoy the benefits, take one capsule daily. It's crucial not to exceed the recommended dosage, as overconsumption could have detrimental health effects. Individuals who have experienced negative reactions to herbal supplements in the past should refrain from taking this supplement.CinnaChroma is not suitable for individuals under the age of 18. Pregnant or nursing women should avoid this supplement, as it may pose risks to their health.</p>
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="250" border="0" data-original-height="1062" data-original-width="2727" /></a></h2>
<p>If users are currently taking any over-the-counter medications or have underlying medical conditions, it is advised to abstain from using this supplement.Prior to incorporating this dietary supplement into ones routine, seeking consultation with a healthcare professional is recommended. This precaution ensures that potential adverse reactions are minimized and ones health remains a priority.</p>
<h2><strong>Detail Pricing of <a href="URL
<h2 style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="436" border="0" data-original-height="980" data-original-width="1436" /></a></h2>
<ul>
<li><strong>Buy One Bottle of <a href="URL $59/bottle + Small Shipping Fee.</strong></li>
<li><strong> Buy Three Bottles of CinnaChroma $147 [USD 59/bottle] + Free Shipping + Digital Book as Bonus.</strong></li>
<li><strong><span style="background-color: yellow; color: maroon;"> Buy Six Bottles of <a href="URL $234 [USD 39/bottle] + Free Shipping + Digital Book as Bonus.</span> <span style="color: lime;"></span><br /></strong></li>
</ul>
<h2 style="text-align: center;"><strong><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Click Here To Visit the Official Website & Buy CinnaChroma!</a></span></strong></h2>
<h3><strong>Free Bonus Is:</strong></h3>
<p>The Blood Sugar Solution Kit is Barton Publishing's #1 Top Selling Blood Sugar Support Program.Created under Dr. Scott Saunders, MD's guidance, this system helps balance blood sugar, support A1C, and combat erratic blood sugar's root-causes. With its easy-to-follow simple instructions, this Solution Kit has helped over half a million folks get off the regular “run of the mill” solutions for erratic blood sugar and enjoy stable blood sugar levels.Over half a million amazed users have made The Barton Blood Sugar Solution Kit our #1 most popular product in Barton Publishing's history!And you'll clearly see why since this proven blood sugar support program is broken down into manageable, progressive phases that anyone with high blood sugar can use.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="390" border="0" data-original-height="731" data-original-width="1200" /></a></span></h2>
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<p>Here's how it works: Go ahead and claim our best deal ever. Take full advantage of our exclusive 3 or 6 month supply bundle, where you can save up to $360 right now. And when you do, you'll have a full 365 days to try it out - risk-free.And if for any reason you don't see or feel the results you demand and deserve, or even if you just don't like the color of the bottle... whatever the reason, simply return it within 365 days for a full refund.</p>
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[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
1e4c8e24fb8e5d78a5d1ac84c29c4dc47b58eea6
|
# Dataset Card for Evaluation run of synapsoft/Llama-2-7b-hf-flan2022-1.2M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/synapsoft/Llama-2-7b-hf-flan2022-1.2M
- **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 [synapsoft/Llama-2-7b-hf-flan2022-1.2M](https://huggingface.co/synapsoft/Llama-2-7b-hf-flan2022-1.2M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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 agregated 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_synapsoft__Llama-2-7b-hf-flan2022-1.2M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T06:59:34.296378](https://huggingface.co/datasets/open-llm-leaderboard/details_synapsoft__Llama-2-7b-hf-flan2022-1.2M/blob/main/results_2023-10-13T06-59-34.296378.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": {
"em": 0.25870385906040266,
"em_stderr": 0.004484736946763185,
"f1": 0.2965908137583894,
"f1_stderr": 0.004480084563201026,
"acc": 0.40002920104621126,
"acc_stderr": 0.008888005892783395
},
"harness|drop|3": {
"em": 0.25870385906040266,
"em_stderr": 0.004484736946763185,
"f1": 0.2965908137583894,
"f1_stderr": 0.004480084563201026
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407052
},
"harness|winogrande|5": {
"acc": 0.755327545382794,
"acc_stderr": 0.012082125654159738
}
}
```
### 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_synapsoft__Llama-2-7b-hf-flan2022-1.2M
|
[
"region:us"
] |
2023-08-29T10:39:05+00:00
|
{"pretty_name": "Evaluation run of synapsoft/Llama-2-7b-hf-flan2022-1.2M", "dataset_summary": "Dataset automatically created during the evaluation run of model [synapsoft/Llama-2-7b-hf-flan2022-1.2M](https://huggingface.co/synapsoft/Llama-2-7b-hf-flan2022-1.2M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 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 agregated 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_synapsoft__Llama-2-7b-hf-flan2022-1.2M\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T06:59:34.296378](https://huggingface.co/datasets/open-llm-leaderboard/details_synapsoft__Llama-2-7b-hf-flan2022-1.2M/blob/main/results_2023-10-13T06-59-34.296378.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 \"em\": 0.25870385906040266,\n \"em_stderr\": 0.004484736946763185,\n \"f1\": 0.2965908137583894,\n \"f1_stderr\": 0.004480084563201026,\n \"acc\": 0.40002920104621126,\n \"acc_stderr\": 0.008888005892783395\n },\n \"harness|drop|3\": {\n \"em\": 0.25870385906040266,\n \"em_stderr\": 0.004484736946763185,\n \"f1\": 0.2965908137583894,\n \"f1_stderr\": 0.004480084563201026\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \"acc_stderr\": 0.005693886131407052\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.755327545382794,\n \"acc_stderr\": 0.012082125654159738\n }\n}\n```", "repo_url": "https://huggingface.co/synapsoft/Llama-2-7b-hf-flan2022-1.2M", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-13T05:59:47+00:00
|
[] |
[] |
TAGS
#region-us
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# Dataset Card for Evaluation run of synapsoft/Llama-2-7b-hf-flan2022-1.2M
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model synapsoft/Llama-2-7b-hf-flan2022-1.2M on the Open LLM Leaderboard.
The dataset is composed of 64 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 agregated 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 2023-10-13T06:59:34.296378(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 synapsoft/Llama-2-7b-hf-flan2022-1.2M",
"## 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 synapsoft/Llama-2-7b-hf-flan2022-1.2M on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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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"### Source Data",
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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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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model synapsoft/Llama-2-7b-hf-flan2022-1.2M on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 synapsoft/Llama-2-7b-hf-flan2022-1.2M## 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 synapsoft/Llama-2-7b-hf-flan2022-1.2M on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-13T06:59:34.296378(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and 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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c6cfabad9b7d8b8ee9776a837cf04deba54cd412
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
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).
### 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]
|
PetraAI/autotrain-data-zalmati-ai
|
[
"task_categories:text-classification",
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"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"task_categories:sentence-similarity",
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"task_categories:audio-to-audio",
"task_categories:audio-classification",
"task_categories:voice-activity-detection",
"task_categories:depth-estimation",
"task_categories:image-classification",
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:unconditional-image-generation",
"task_categories:robotics",
"task_categories:reinforcement-learning",
"task_categories:tabular-classification",
"task_categories:video-classification",
"task_categories:tabular-to-text",
"task_categories:multiple-choice",
"task_categories:text-retrieval",
"task_categories:time-series-forecasting",
"task_categories:text-to-video",
"task_categories:visual-question-answering",
"task_categories:zero-shot-image-classification",
"task_categories:graph-ml",
"task_categories:table-to-text",
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-to-image",
"task_categories:tabular-regression",
"size_categories:100K<n<1M",
"language:ar",
"language:en",
"license:apache-2.0",
"chemistry",
"medical",
"code",
"art",
"music",
"biology",
"finance",
"legal",
"climate",
"region:us"
] |
2023-08-29T10:41:34+00:00
|
{"language": ["ar", "en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "translation", "summarization", "conversational", "feature-extraction", "text-generation", "text2text-generation", "fill-mask", "sentence-similarity", "text-to-speech", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "voice-activity-detection", "depth-estimation", "image-classification", "object-detection", "image-segmentation", "unconditional-image-generation", "robotics", "reinforcement-learning", "tabular-classification", "video-classification", "tabular-to-text", "multiple-choice", "text-retrieval", "time-series-forecasting", "text-to-video", "visual-question-answering", "zero-shot-image-classification", "graph-ml", "table-to-text", "text-to-image", "image-to-text", "image-to-image", "tabular-regression"], "pretty_name": "Zalmati-Autotrain", "tags": ["chemistry", "medical", "code", "art", "music", "biology", "finance", "legal", "climate"]}
|
2023-09-05T12:47:18+00:00
|
[] |
[
"ar",
"en"
] |
TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #task_categories-fill-mask #task_categories-sentence-similarity #task_categories-text-to-speech #task_categories-automatic-speech-recognition #task_categories-audio-to-audio #task_categories-audio-classification #task_categories-voice-activity-detection #task_categories-depth-estimation #task_categories-image-classification #task_categories-object-detection #task_categories-image-segmentation #task_categories-unconditional-image-generation #task_categories-robotics #task_categories-reinforcement-learning #task_categories-tabular-classification #task_categories-video-classification #task_categories-tabular-to-text #task_categories-multiple-choice #task_categories-text-retrieval #task_categories-time-series-forecasting #task_categories-text-to-video #task_categories-visual-question-answering #task_categories-zero-shot-image-classification #task_categories-graph-ml #task_categories-table-to-text #task_categories-text-to-image #task_categories-image-to-text #task_categories-image-to-image #task_categories-tabular-regression #size_categories-100K<n<1M #language-Arabic #language-English #license-apache-2.0 #chemistry #medical #code #art #music #biology #finance #legal #climate #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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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ea9a336f545a2d20fd5569a88fa3a785198737be
|
# Dataset Card for "autotree_automl_MagicTelescope_gosdt_l512_d3_sd3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_MagicTelescope_gosdt_l512_d3_sd3
|
[
"region:us"
] |
2023-08-29T10:41:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6767200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 676720000, "num_examples": 10000}], "download_size": 2606790213, "dataset_size": 7443920000}}
|
2023-08-29T10:44:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_MagicTelescope_gosdt_l512_d3_sd3"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
4fdff45c0baddf91d463e181f375087114d17e56
|
# Dataset Card for "autotree_automl_credit_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_credit_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T10:52:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 205680000, "num_examples": 10000}, {"name": "validation", "num_bytes": 205680000, "num_examples": 10000}], "download_size": 125441058, "dataset_size": 411360000}}
|
2023-08-30T12:22:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_credit_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_credit_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_credit_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_credit_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
21ea2f35412e13dc076035c78aa2c2dedccfcfe8
|
# Dataset Card for H&M Clothes captions
_Dataset used to train/finetune [Clothes text to image model]
Captions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data?select=images)
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
---
|
wbensvage/clothes_desc
|
[
"task_categories:text-to-image",
"annotations_creators:human generated by using detail_desc and color",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n=1K",
"source_datasets:www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-08-29T10:55:35+00:00
|
{"annotations_creators": ["human generated by using detail_desc and color"], "language_creators": ["other"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["n=1K"], "source_datasets": ["www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "H&M Clothes captions", "tags": []}
|
2023-08-29T18:14:36+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-to-image #annotations_creators-human generated by using detail_desc and color #language_creators-other #multilinguality-monolingual #size_categories-n=1K #source_datasets-www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations #language-English #license-apache-2.0 #region-us
|
# Dataset Card for H&M Clothes captions
_Dataset used to train/finetune [Clothes text to image model]
Captions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (URL
For each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.
---
|
[
"# Dataset Card for H&M Clothes captions\n\n_Dataset used to train/finetune [Clothes text to image model]\n\nCaptions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (URL\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.\n---"
] |
[
"TAGS\n#task_categories-text-to-image #annotations_creators-human generated by using detail_desc and color #language_creators-other #multilinguality-monolingual #size_categories-n=1K #source_datasets-www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for H&M Clothes captions\n\n_Dataset used to train/finetune [Clothes text to image model]\n\nCaptions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (URL\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.\n---"
] |
[
108,
157
] |
[
"passage: TAGS\n#task_categories-text-to-image #annotations_creators-human generated by using detail_desc and color #language_creators-other #multilinguality-monolingual #size_categories-n=1K #source_datasets-www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations #language-English #license-apache-2.0 #region-us \n# Dataset Card for H&M Clothes captions\n\n_Dataset used to train/finetune [Clothes text to image model]\n\nCaptions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (URL\n\nFor each row the dataset contains 'image' and 'text' keys. 'image' is a varying size PIL jpeg, and 'text' is the accompanying text caption. Only a train split is provided.\n---"
] |
e72f919251afc134b72602612a2a7a04eb5f2bd5
|
# German OpenAssistant Conversations Dataset (OASST-DE)
With the goal of advancing open-source, german-language LLM research, we present
OASST-DE: a high quality subset of a recent (25.08.23) dump from the [OpenAssistant website](https://www.open-assistant.io/)
translated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under [dataset creation.](#dataset-creation-process)
For more details on the OpenAssistant Project, look at the [first OASST dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), [the Open-Assistant GitHub repo](https://github.com/LAION-AI/Open-Assistant)
or [our paper](https://arxiv.org/abs/2304.07327).
This dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster.
Check out LeoLM-Chat trained with OASST-DE ([7b](https://huggingface.co/LeoLM/leo-hessianai-7b-chat), [13b](https://huggingface.co/LeoLM/leo-hessianai-13b-chat)) finetuned on OASST-DE and read [their blog post](https://laion.ai/blog/leo-lm/)) for more info on LeoLM.
## Dataset Creation Process
This dataset was created from a recent OASST dump by following these steps:
- Filter for Top1 response trees with assistant response leaves
- Filter first prompt quality >= 0.5
- Filter total conversation length < 1900 tokens to fit in GPT3.5 context length
- Filter for `'lang' == 'de'` -> add to dataset
- Filter for `'lang' == 'en'` (other languages often result in failed translations)
- Translate using GPT-3.5-turbo API (total cost ~15$).
This results in around 3.7k samples of high-quality assistant conversations.
## Dataset Structure
This dataset has only one `'conversation'` field. Each example is a list of an alternating conversation between `'prompter'` and `'assistant'`,
where each entry is a dict with `'text'` and `'role'` fields:
```json
"conversation": [
{"role": "prompter", "text": "Moin, wie geht's dir?"},
{"role": "assistant", "text": "Moin Moin! Mir geht es gut, und dir?"},
...
]
```
## Usage with 🤗Datasets:
```python
from datasets import load_dataset
ds = load_dataset("OpenAssistant/OASST-DE", split="train")
print(ds[0]["conversation"])
```
|
OpenAssistant/OASST-DE
|
[
"size_categories:1K<n<10K",
"language:de",
"license:apache-2.0",
"arxiv:2304.07327",
"region:us"
] |
2023-08-29T11:04:11+00:00
|
{"language": ["de"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "conversation", "list": [{"name": "role", "dtype": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 8022604.792326268, "num_examples": 3721}], "download_size": 4325950, "dataset_size": 8022604.792326268}}
|
2023-11-13T10:24:29+00:00
|
[
"2304.07327"
] |
[
"de"
] |
TAGS
#size_categories-1K<n<10K #language-German #license-apache-2.0 #arxiv-2304.07327 #region-us
|
# German OpenAssistant Conversations Dataset (OASST-DE)
With the goal of advancing open-source, german-language LLM research, we present
OASST-DE: a high quality subset of a recent (25.08.23) dump from the OpenAssistant website
translated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under dataset creation.
For more details on the OpenAssistant Project, look at the first OASST dataset (OASST1), the Open-Assistant GitHub repo
or our paper.
This dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster.
Check out LeoLM-Chat trained with OASST-DE (7b, 13b) finetuned on OASST-DE and read their blog post) for more info on LeoLM.
## Dataset Creation Process
This dataset was created from a recent OASST dump by following these steps:
- Filter for Top1 response trees with assistant response leaves
- Filter first prompt quality >= 0.5
- Filter total conversation length < 1900 tokens to fit in GPT3.5 context length
- Filter for ''lang' == 'de'' -> add to dataset
- Filter for ''lang' == 'en'' (other languages often result in failed translations)
- Translate using GPT-3.5-turbo API (total cost ~15$).
This results in around 3.7k samples of high-quality assistant conversations.
## Dataset Structure
This dataset has only one ''conversation'' field. Each example is a list of an alternating conversation between ''prompter'' and ''assistant'',
where each entry is a dict with ''text'' and ''role'' fields:
## Usage with Datasets:
|
[
"# German OpenAssistant Conversations Dataset (OASST-DE)\nWith the goal of advancing open-source, german-language LLM research, we present \nOASST-DE: a high quality subset of a recent (25.08.23) dump from the OpenAssistant website\ntranslated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under dataset creation.\nFor more details on the OpenAssistant Project, look at the first OASST dataset (OASST1), the Open-Assistant GitHub repo\nor our paper.\n\nThis dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster. \nCheck out LeoLM-Chat trained with OASST-DE (7b, 13b) finetuned on OASST-DE and read their blog post) for more info on LeoLM.",
"## Dataset Creation Process\nThis dataset was created from a recent OASST dump by following these steps:\n- Filter for Top1 response trees with assistant response leaves\n- Filter first prompt quality >= 0.5\n- Filter total conversation length < 1900 tokens to fit in GPT3.5 context length\n- Filter for ''lang' == 'de'' -> add to dataset\n- Filter for ''lang' == 'en'' (other languages often result in failed translations)\n- Translate using GPT-3.5-turbo API (total cost ~15$).\n\nThis results in around 3.7k samples of high-quality assistant conversations.",
"## Dataset Structure\nThis dataset has only one ''conversation'' field. Each example is a list of an alternating conversation between ''prompter'' and ''assistant'',\nwhere each entry is a dict with ''text'' and ''role'' fields:",
"## Usage with Datasets:"
] |
[
"TAGS\n#size_categories-1K<n<10K #language-German #license-apache-2.0 #arxiv-2304.07327 #region-us \n",
"# German OpenAssistant Conversations Dataset (OASST-DE)\nWith the goal of advancing open-source, german-language LLM research, we present \nOASST-DE: a high quality subset of a recent (25.08.23) dump from the OpenAssistant website\ntranslated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under dataset creation.\nFor more details on the OpenAssistant Project, look at the first OASST dataset (OASST1), the Open-Assistant GitHub repo\nor our paper.\n\nThis dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster. \nCheck out LeoLM-Chat trained with OASST-DE (7b, 13b) finetuned on OASST-DE and read their blog post) for more info on LeoLM.",
"## Dataset Creation Process\nThis dataset was created from a recent OASST dump by following these steps:\n- Filter for Top1 response trees with assistant response leaves\n- Filter first prompt quality >= 0.5\n- Filter total conversation length < 1900 tokens to fit in GPT3.5 context length\n- Filter for ''lang' == 'de'' -> add to dataset\n- Filter for ''lang' == 'en'' (other languages often result in failed translations)\n- Translate using GPT-3.5-turbo API (total cost ~15$).\n\nThis results in around 3.7k samples of high-quality assistant conversations.",
"## Dataset Structure\nThis dataset has only one ''conversation'' field. Each example is a list of an alternating conversation between ''prompter'' and ''assistant'',\nwhere each entry is a dict with ''text'' and ''role'' fields:",
"## Usage with Datasets:"
] |
[
39,
213,
137,
60,
8
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #language-German #license-apache-2.0 #arxiv-2304.07327 #region-us \n# German OpenAssistant Conversations Dataset (OASST-DE)\nWith the goal of advancing open-source, german-language LLM research, we present \nOASST-DE: a high quality subset of a recent (25.08.23) dump from the OpenAssistant website\ntranslated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under dataset creation.\nFor more details on the OpenAssistant Project, look at the first OASST dataset (OASST1), the Open-Assistant GitHub repo\nor our paper.\n\nThis dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster. \nCheck out LeoLM-Chat trained with OASST-DE (7b, 13b) finetuned on OASST-DE and read their blog post) for more info on LeoLM.## Dataset Creation Process\nThis dataset was created from a recent OASST dump by following these steps:\n- Filter for Top1 response trees with assistant response leaves\n- Filter first prompt quality >= 0.5\n- Filter total conversation length < 1900 tokens to fit in GPT3.5 context length\n- Filter for ''lang' == 'de'' -> add to dataset\n- Filter for ''lang' == 'en'' (other languages often result in failed translations)\n- Translate using GPT-3.5-turbo API (total cost ~15$).\n\nThis results in around 3.7k samples of high-quality assistant conversations.## Dataset Structure\nThis dataset has only one ''conversation'' field. Each example is a list of an alternating conversation between ''prompter'' and ''assistant'',\nwhere each entry is a dict with ''text'' and ''role'' fields:## Usage with Datasets:"
] |
aed005295f12ea02ed4f8190d56c4c679248be7d
|
# Dataset Card for "CoT-Collection-standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/CoT-Collection-standardized
|
[
"region:us"
] |
2023-08-29T11:05:31+00:00
|
{"dataset_info": {"features": [{"name": "message", "dtype": "string"}, {"name": "message_type", "dtype": "string"}, {"name": "message_id", "dtype": "int64"}, {"name": "conversation_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2149718484, "num_examples": 3675842}], "download_size": 1206341432, "dataset_size": 2149718484}}
|
2023-08-30T19:37:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "CoT-Collection-standardized"
More Information needed
|
[
"# Dataset Card for \"CoT-Collection-standardized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"CoT-Collection-standardized\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"CoT-Collection-standardized\"\n\nMore Information needed"
] |
ffcafe5f98f1536fdfc63fae73d207dbc7528a8f
|
# Dataset Card for Evaluation run of TheBloke/Llama-2-7B-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Llama-2-7B-GPTQ
- **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 [TheBloke/Llama-2-7B-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated 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_TheBloke__Llama-2-7B-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T20:13:14.412039](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-7B-GPTQ/blob/main/results_2023-10-21T20-13-14.412039.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": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573356367,
"f1": 0.054487206375839085,
"f1_stderr": 0.001279202944739141,
"acc": 0.38965983773074364,
"acc_stderr": 0.009246673557602019
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573356367,
"f1": 0.054487206375839085,
"f1_stderr": 0.001279202944739141
},
"harness|gsm8k|5": {
"acc": 0.050037907505686124,
"acc_stderr": 0.006005442354577735
},
"harness|winogrande|5": {
"acc": 0.7292817679558011,
"acc_stderr": 0.012487904760626303
}
}
```
### 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_TheBloke__Llama-2-7B-GPTQ
|
[
"region:us"
] |
2023-08-29T11:13:55+00:00
|
{"pretty_name": "Evaluation run of TheBloke/Llama-2-7B-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/Llama-2-7B-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_TheBloke__Llama-2-7B-GPTQ\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-21T20:13:14.412039](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-7B-GPTQ/blob/main/results_2023-10-21T20-13-14.412039.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 \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573356367,\n \"f1\": 0.054487206375839085,\n \"f1_stderr\": 0.001279202944739141,\n \"acc\": 0.38965983773074364,\n \"acc_stderr\": 0.009246673557602019\n },\n \"harness|drop|3\": {\n \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573356367,\n \"f1\": 0.054487206375839085,\n \"f1_stderr\": 0.001279202944739141\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.050037907505686124,\n \"acc_stderr\": 0.006005442354577735\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7292817679558011,\n \"acc_stderr\": 0.012487904760626303\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/Llama-2-7B-GPTQ", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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|
2023-10-21T19:13:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/Llama-2-7B-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/Llama-2-7B-GPTQ on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-21T20:13:14.412039(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 TheBloke/Llama-2-7B-GPTQ",
"## 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 TheBloke/Llama-2-7B-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-21T20:13:14.412039(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 TheBloke/Llama-2-7B-GPTQ",
"## 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 TheBloke/Llama-2-7B-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-21T20:13:14.412039(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 TheBloke/Llama-2-7B-GPTQ## 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 TheBloke/Llama-2-7B-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-21T20:13:14.412039(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
b9b21fc38fa6cf7361bf916a22ee48c119940670
|
# Dataset Card for Evaluation run of None
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/None
- **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 [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_bigscience__bloomz",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T12:14:13.875692](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz/blob/main/results_2023-08-29T12%3A14%3A13.875692.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": 0.47917755403252543,
"acc_stderr": 0.03572101484290109,
"acc_norm": 0.48335485520551164,
"acc_norm_stderr": 0.0357085480998606,
"mc1": 0.2607099143206854,
"mc1_stderr": 0.015368841620766372,
"mc2": 0.4393940961026447,
"mc2_stderr": 0.015292532701908591
},
"harness|arc:challenge|25": {
"acc": 0.5042662116040956,
"acc_stderr": 0.014610858923956955,
"acc_norm": 0.5537542662116041,
"acc_norm_stderr": 0.014526705548539982
},
"harness|hellaswag|10": {
"acc": 0.5553674566819359,
"acc_stderr": 0.004959094146471527,
"acc_norm": 0.7523401712806214,
"acc_norm_stderr": 0.004307709682499536
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"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": 0.5,
"acc_stderr": 0.04068942293855797,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04068942293855797
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5735849056603773,
"acc_stderr": 0.030437794342983052,
"acc_norm": 0.5735849056603773,
"acc_norm_stderr": 0.030437794342983052
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.04174752578923185,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.04174752578923185
},
"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": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"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": 0.43352601156069365,
"acc_stderr": 0.03778621079092055,
"acc_norm": 0.43352601156069365,
"acc_norm_stderr": 0.03778621079092055
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.04858083574266344,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266344
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4425531914893617,
"acc_stderr": 0.03246956919789958,
"acc_norm": 0.4425531914893617,
"acc_norm_stderr": 0.03246956919789958
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.32456140350877194,
"acc_stderr": 0.04404556157374767,
"acc_norm": 0.32456140350877194,
"acc_norm_stderr": 0.04404556157374767
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.46206896551724136,
"acc_stderr": 0.041546596717075474,
"acc_norm": 0.46206896551724136,
"acc_norm_stderr": 0.041546596717075474
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.32275132275132273,
"acc_stderr": 0.024078943243597016,
"acc_norm": 0.32275132275132273,
"acc_norm_stderr": 0.024078943243597016
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30952380952380953,
"acc_stderr": 0.041349130183033156,
"acc_norm": 0.30952380952380953,
"acc_norm_stderr": 0.041349130183033156
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.532258064516129,
"acc_stderr": 0.028384747788813332,
"acc_norm": 0.532258064516129,
"acc_norm_stderr": 0.028384747788813332
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.39408866995073893,
"acc_stderr": 0.034381579670365446,
"acc_norm": 0.39408866995073893,
"acc_norm_stderr": 0.034381579670365446
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.49696969696969695,
"acc_stderr": 0.03904272341431857,
"acc_norm": 0.49696969696969695,
"acc_norm_stderr": 0.03904272341431857
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6616161616161617,
"acc_stderr": 0.03371124142626303,
"acc_norm": 0.6616161616161617,
"acc_norm_stderr": 0.03371124142626303
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.616580310880829,
"acc_stderr": 0.03508984236295342,
"acc_norm": 0.616580310880829,
"acc_norm_stderr": 0.03508984236295342
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.47435897435897434,
"acc_stderr": 0.02531764972644865,
"acc_norm": 0.47435897435897434,
"acc_norm_stderr": 0.02531764972644865
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.29259259259259257,
"acc_stderr": 0.02773896963217609,
"acc_norm": 0.29259259259259257,
"acc_norm_stderr": 0.02773896963217609
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5672268907563025,
"acc_stderr": 0.032183581077426124,
"acc_norm": 0.5672268907563025,
"acc_norm_stderr": 0.032183581077426124
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.0386155754625517,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.0386155754625517
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6623853211009174,
"acc_stderr": 0.020275265986638924,
"acc_norm": 0.6623853211009174,
"acc_norm_stderr": 0.020275265986638924
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4537037037037037,
"acc_stderr": 0.033953227263757976,
"acc_norm": 0.4537037037037037,
"acc_norm_stderr": 0.033953227263757976
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.03506612560524866,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.03506612560524866
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6919831223628692,
"acc_stderr": 0.0300523893356057,
"acc_norm": 0.6919831223628692,
"acc_norm_stderr": 0.0300523893356057
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.4977578475336323,
"acc_stderr": 0.033557465352232634,
"acc_norm": 0.4977578475336323,
"acc_norm_stderr": 0.033557465352232634
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.4961832061068702,
"acc_stderr": 0.043851623256015534,
"acc_norm": 0.4961832061068702,
"acc_norm_stderr": 0.043851623256015534
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.4793388429752066,
"acc_stderr": 0.04560456086387235,
"acc_norm": 0.4793388429752066,
"acc_norm_stderr": 0.04560456086387235
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5740740740740741,
"acc_stderr": 0.047803436269367894,
"acc_norm": 0.5740740740740741,
"acc_norm_stderr": 0.047803436269367894
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.49079754601226994,
"acc_stderr": 0.03927705600787443,
"acc_norm": 0.49079754601226994,
"acc_norm_stderr": 0.03927705600787443
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.047184714852195886,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.047184714852195886
},
"harness|hendrycksTest-management|5": {
"acc": 0.6310679611650486,
"acc_stderr": 0.0477761518115674,
"acc_norm": 0.6310679611650486,
"acc_norm_stderr": 0.0477761518115674
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.717948717948718,
"acc_stderr": 0.02948036054954119,
"acc_norm": 0.717948717948718,
"acc_norm_stderr": 0.02948036054954119
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6232439335887612,
"acc_stderr": 0.01732829290730305,
"acc_norm": 0.6232439335887612,
"acc_norm_stderr": 0.01732829290730305
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5,
"acc_stderr": 0.026919095102908273,
"acc_norm": 0.5,
"acc_norm_stderr": 0.026919095102908273
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2770949720670391,
"acc_stderr": 0.014968772435812145,
"acc_norm": 0.2770949720670391,
"acc_norm_stderr": 0.014968772435812145
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.49673202614379086,
"acc_stderr": 0.02862930519400354,
"acc_norm": 0.49673202614379086,
"acc_norm_stderr": 0.02862930519400354
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4919614147909968,
"acc_stderr": 0.028394421370984545,
"acc_norm": 0.4919614147909968,
"acc_norm_stderr": 0.028394421370984545
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4691358024691358,
"acc_stderr": 0.02776768960683393,
"acc_norm": 0.4691358024691358,
"acc_norm_stderr": 0.02776768960683393
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.34397163120567376,
"acc_stderr": 0.028338017428611324,
"acc_norm": 0.34397163120567376,
"acc_norm_stderr": 0.028338017428611324
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3116036505867014,
"acc_stderr": 0.011829039182849648,
"acc_norm": 0.3116036505867014,
"acc_norm_stderr": 0.011829039182849648
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4742647058823529,
"acc_stderr": 0.030332578094555033,
"acc_norm": 0.4742647058823529,
"acc_norm_stderr": 0.030332578094555033
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4950980392156863,
"acc_stderr": 0.020226862710039473,
"acc_norm": 0.4950980392156863,
"acc_norm_stderr": 0.020226862710039473
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.4909090909090909,
"acc_stderr": 0.04788339768702861,
"acc_norm": 0.4909090909090909,
"acc_norm_stderr": 0.04788339768702861
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5265306122448979,
"acc_stderr": 0.03196412734523272,
"acc_norm": 0.5265306122448979,
"acc_norm_stderr": 0.03196412734523272
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.5671641791044776,
"acc_stderr": 0.0350349092367328,
"acc_norm": 0.5671641791044776,
"acc_norm_stderr": 0.0350349092367328
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4578313253012048,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.4578313253012048,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.03834234744164993,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.03834234744164993
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2607099143206854,
"mc1_stderr": 0.015368841620766372,
"mc2": 0.4393940961026447,
"mc2_stderr": 0.015292532701908591
}
}
```
### 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_bigscience__bloomz
|
[
"region:us"
] |
2023-08-29T11:14:32+00:00
|
{"pretty_name": "Evaluation run of None", "dataset_summary": "Dataset automatically created during the evaluation run of model [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_bigscience__bloomz\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T12:14:13.875692](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz/blob/main/results_2023-08-29T12%3A14%3A13.875692.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\": 0.47917755403252543,\n \"acc_stderr\": 0.03572101484290109,\n \"acc_norm\": 0.48335485520551164,\n \"acc_norm_stderr\": 0.0357085480998606,\n \"mc1\": 0.2607099143206854,\n \"mc1_stderr\": 0.015368841620766372,\n \"mc2\": 0.4393940961026447,\n \"mc2_stderr\": 0.015292532701908591\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5042662116040956,\n \"acc_stderr\": 0.014610858923956955,\n \"acc_norm\": 0.5537542662116041,\n \"acc_norm_stderr\": 0.014526705548539982\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5553674566819359,\n \"acc_stderr\": 0.004959094146471527,\n \"acc_norm\": 0.7523401712806214,\n \"acc_norm_stderr\": 0.004307709682499536\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480863,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480863\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04068942293855797,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04068942293855797\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5735849056603773,\n \"acc_stderr\": 0.030437794342983052,\n \"acc_norm\": 0.5735849056603773,\n \"acc_norm_stderr\": 0.030437794342983052\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.04174752578923185,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.04174752578923185\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\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\": 0.43352601156069365,\n \"acc_stderr\": 0.03778621079092055,\n \"acc_norm\": 0.43352601156069365,\n \"acc_norm_stderr\": 0.03778621079092055\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266344,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266344\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.03246956919789958,\n \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.03246956919789958\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.32275132275132273,\n \"acc_stderr\": 0.024078943243597016,\n \"acc_norm\": 0.32275132275132273,\n \"acc_norm_stderr\": 0.024078943243597016\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n \"acc_stderr\": 0.041349130183033156,\n \"acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.041349130183033156\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.532258064516129,\n \"acc_stderr\": 0.028384747788813332,\n \"acc_norm\": 0.532258064516129,\n \"acc_norm_stderr\": 0.028384747788813332\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.39408866995073893,\n \"acc_stderr\": 0.034381579670365446,\n \"acc_norm\": 0.39408866995073893,\n \"acc_norm_stderr\": 0.034381579670365446\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.49696969696969695,\n \"acc_stderr\": 0.03904272341431857,\n \"acc_norm\": 0.49696969696969695,\n \"acc_norm_stderr\": 0.03904272341431857\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.6616161616161617,\n \"acc_stderr\": 0.03371124142626303,\n \"acc_norm\": 0.6616161616161617,\n \"acc_norm_stderr\": 0.03371124142626303\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.616580310880829,\n \"acc_stderr\": 0.03508984236295342,\n \"acc_norm\": 0.616580310880829,\n \"acc_norm_stderr\": 0.03508984236295342\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.47435897435897434,\n \"acc_stderr\": 0.02531764972644865,\n \"acc_norm\": 0.47435897435897434,\n \"acc_norm_stderr\": 0.02531764972644865\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.29259259259259257,\n \"acc_stderr\": 0.02773896963217609,\n \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.02773896963217609\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5672268907563025,\n \"acc_stderr\": 0.032183581077426124,\n \"acc_norm\": 0.5672268907563025,\n \"acc_norm_stderr\": 0.032183581077426124\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\": 0.6623853211009174,\n \"acc_stderr\": 0.020275265986638924,\n \"acc_norm\": 0.6623853211009174,\n \"acc_norm_stderr\": 0.020275265986638924\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.03506612560524866,\n \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.03506612560524866\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6919831223628692,\n \"acc_stderr\": 0.0300523893356057,\n \"acc_norm\": 0.6919831223628692,\n \"acc_norm_stderr\": 0.0300523893356057\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4977578475336323,\n \"acc_stderr\": 0.033557465352232634,\n \"acc_norm\": 0.4977578475336323,\n \"acc_norm_stderr\": 0.033557465352232634\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.4793388429752066,\n \"acc_stderr\": 0.04560456086387235,\n \"acc_norm\": 0.4793388429752066,\n \"acc_norm_stderr\": 0.04560456086387235\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.047803436269367894,\n \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.047803436269367894\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.49079754601226994,\n \"acc_stderr\": 0.03927705600787443,\n \"acc_norm\": 0.49079754601226994,\n \"acc_norm_stderr\": 0.03927705600787443\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6310679611650486,\n \"acc_stderr\": 0.0477761518115674,\n \"acc_norm\": 0.6310679611650486,\n \"acc_norm_stderr\": 0.0477761518115674\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.717948717948718,\n \"acc_stderr\": 0.02948036054954119,\n \"acc_norm\": 0.717948717948718,\n \"acc_norm_stderr\": 0.02948036054954119\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6232439335887612,\n \"acc_stderr\": 0.01732829290730305,\n \"acc_norm\": 0.6232439335887612,\n \"acc_norm_stderr\": 0.01732829290730305\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.026919095102908273,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.026919095102908273\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2770949720670391,\n \"acc_stderr\": 0.014968772435812145,\n \"acc_norm\": 0.2770949720670391,\n \"acc_norm_stderr\": 0.014968772435812145\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.49673202614379086,\n \"acc_stderr\": 0.02862930519400354,\n \"acc_norm\": 0.49673202614379086,\n \"acc_norm_stderr\": 0.02862930519400354\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4919614147909968,\n \"acc_stderr\": 0.028394421370984545,\n \"acc_norm\": 0.4919614147909968,\n \"acc_norm_stderr\": 0.028394421370984545\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4691358024691358,\n \"acc_stderr\": 0.02776768960683393,\n \"acc_norm\": 0.4691358024691358,\n \"acc_norm_stderr\": 0.02776768960683393\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611324,\n \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611324\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3116036505867014,\n \"acc_stderr\": 0.011829039182849648,\n \"acc_norm\": 0.3116036505867014,\n \"acc_norm_stderr\": 0.011829039182849648\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4742647058823529,\n \"acc_stderr\": 0.030332578094555033,\n \"acc_norm\": 0.4742647058823529,\n \"acc_norm_stderr\": 0.030332578094555033\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.4950980392156863,\n \"acc_stderr\": 0.020226862710039473,\n \"acc_norm\": 0.4950980392156863,\n \"acc_norm_stderr\": 0.020226862710039473\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4909090909090909,\n \"acc_stderr\": 0.04788339768702861,\n \"acc_norm\": 0.4909090909090909,\n \"acc_norm_stderr\": 0.04788339768702861\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5265306122448979,\n \"acc_stderr\": 0.03196412734523272,\n \"acc_norm\": 0.5265306122448979,\n \"acc_norm_stderr\": 0.03196412734523272\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5671641791044776,\n \"acc_stderr\": 0.0350349092367328,\n \"acc_norm\": 0.5671641791044776,\n \"acc_norm_stderr\": 0.0350349092367328\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.4578313253012048,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.4578313253012048,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.03834234744164993,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.03834234744164993\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2607099143206854,\n \"mc1_stderr\": 0.015368841620766372,\n \"mc2\": 0.4393940961026447,\n \"mc2_stderr\": 0.015292532701908591\n }\n}\n```", "repo_url": "https://huggingface.co/None", "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_08_29T12_14_13.875692", "path": ["**/details_harness|arc:challenge|25_2023-08-29T12:14:13.875692.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T12:14:13.875692.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T12_14_13.875692", "path": ["**/details_harness|hellaswag|10_2023-08-29T12:14:13.875692.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T12:14:13.875692.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_29T12_14_13.875692", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T12:14:13.875692.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T12:14:13.875692.parquet", 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|
2023-08-29T11:14:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of None
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model None on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T12:14:13.875692(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 None",
"## 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 None on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T12:14:13.875692(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 Card for Evaluation run of None",
"## 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 None on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T12:14:13.875692(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 None## 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 None on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T12:14:13.875692(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
b585d61e3a79ebf5f3da5a60c6be195603118073
|
# Dataset Card for Evaluation run of None
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/None
- **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 [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_bigscience__bloom",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T12:19:54.390376](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloom/blob/main/results_2023-08-29T12%3A19%3A54.390376.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": 0.315597821758106,
"acc_stderr": 0.0334554445358342,
"acc_norm": 0.31957868125391004,
"acc_norm_stderr": 0.03344403068302842,
"mc1": 0.2521419828641371,
"mc1_stderr": 0.015201522246299962,
"mc2": 0.3975962282334165,
"mc2_stderr": 0.013579754303009808
},
"harness|arc:challenge|25": {
"acc": 0.4658703071672355,
"acc_stderr": 0.014577311315231102,
"acc_norm": 0.5042662116040956,
"acc_norm_stderr": 0.014610858923956948
},
"harness|hellaswag|10": {
"acc": 0.5676160127464649,
"acc_stderr": 0.004943945069611452,
"acc_norm": 0.7640908185620394,
"acc_norm_stderr": 0.0042369801453443065
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.04135176749720386
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.25,
"acc_stderr": 0.03523807393012047,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03523807393012047
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3169811320754717,
"acc_stderr": 0.028637235639800925,
"acc_norm": 0.3169811320754717,
"acc_norm_stderr": 0.028637235639800925
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2847222222222222,
"acc_stderr": 0.03773809990686934,
"acc_norm": 0.2847222222222222,
"acc_norm_stderr": 0.03773809990686934
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.18,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.24855491329479767,
"acc_stderr": 0.03295304696818318,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.03295304696818318
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.20588235294117646,
"acc_stderr": 0.04023382273617749,
"acc_norm": 0.20588235294117646,
"acc_norm_stderr": 0.04023382273617749
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3276595744680851,
"acc_stderr": 0.030683020843231004,
"acc_norm": 0.3276595744680851,
"acc_norm_stderr": 0.030683020843231004
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.040493392977481425,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.040493392977481425
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.31724137931034485,
"acc_stderr": 0.03878352372138623,
"acc_norm": 0.31724137931034485,
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"harness|truthfulqa:mc|0": {
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"mc2_stderr": 0.013579754303009808
}
}
```
### 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_bigscience__bloom
|
[
"region:us"
] |
2023-08-29T11:20:12+00:00
|
{"pretty_name": "Evaluation run of None", "dataset_summary": "Dataset automatically created during the evaluation run of model [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_bigscience__bloom\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T12:19:54.390376](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloom/blob/main/results_2023-08-29T12%3A19%3A54.390376.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\": 0.315597821758106,\n \"acc_stderr\": 0.0334554445358342,\n \"acc_norm\": 0.31957868125391004,\n \"acc_norm_stderr\": 0.03344403068302842,\n \"mc1\": 0.2521419828641371,\n \"mc1_stderr\": 0.015201522246299962,\n \"mc2\": 0.3975962282334165,\n \"mc2_stderr\": 0.013579754303009808\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4658703071672355,\n \"acc_stderr\": 0.014577311315231102,\n \"acc_norm\": 0.5042662116040956,\n \"acc_norm_stderr\": 0.014610858923956948\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5676160127464649,\n \"acc_stderr\": 0.004943945069611452,\n \"acc_norm\": 0.7640908185620394,\n \"acc_norm_stderr\": 0.0042369801453443065\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03523807393012047,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03523807393012047\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.3169811320754717,\n \"acc_stderr\": 0.028637235639800925,\n \"acc_norm\": 0.3169811320754717,\n \"acc_norm_stderr\": 0.028637235639800925\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2847222222222222,\n \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.2847222222222222,\n \"acc_norm_stderr\": 0.03773809990686934\n },\n \"harness|hendrycksTest-college_chemistry|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_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-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617749,\n \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617749\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231004,\n \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231004\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n \"acc_stderr\": 0.040493392977481425,\n \"acc_norm\": 0.24561403508771928,\n \"acc_norm_stderr\": 0.040493392977481425\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.31724137931034485,\n \"acc_stderr\": 0.03878352372138623,\n \"acc_norm\": 0.31724137931034485,\n \"acc_norm_stderr\": 0.03878352372138623\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2804232804232804,\n \"acc_stderr\": 0.02313528797432563,\n \"acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.02313528797432563\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n \"acc_stderr\": 0.03852273364924315,\n \"acc_norm\": 0.24603174603174602,\n \"acc_norm_stderr\": 0.03852273364924315\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.2967741935483871,\n \"acc_stderr\": 0.02598850079241188,\n \"acc_norm\": 0.2967741935483871,\n \"acc_norm_stderr\": 0.02598850079241188\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.28078817733990147,\n \"acc_stderr\": 0.03161856335358609,\n \"acc_norm\": 0.28078817733990147,\n \"acc_norm_stderr\": 0.03161856335358609\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.2787878787878788,\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\": 0.3787878787878788,\n \"acc_stderr\": 0.03456088731993747,\n \"acc_norm\": 0.3787878787878788,\n \"acc_norm_stderr\": 0.03456088731993747\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.29533678756476683,\n \"acc_stderr\": 0.0329229663915514,\n \"acc_norm\": 0.29533678756476683,\n \"acc_norm_stderr\": 0.0329229663915514\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2230769230769231,\n \"acc_stderr\": 0.021107730127243998,\n \"acc_norm\": 0.2230769230769231,\n \"acc_norm_stderr\": 0.021107730127243998\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085622,\n \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085622\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": 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"harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T12:19:54.390376.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T12_19_54.390376", "path": ["results_2023-08-29T12:19:54.390376.parquet"]}, {"split": "latest", "path": ["results_2023-08-29T12:19:54.390376.parquet"]}]}]}
|
2023-08-29T11:20:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of None
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model None on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T12:19:54.390376(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 None",
"## 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 None on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T12:19:54.390376(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 None",
"## 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 None on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T12:19:54.390376(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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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6,
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10,
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9,
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8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of None## 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 None on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T12:19:54.390376(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
ad357d77e2990b61b3bfd32cf4deb83c22d85978
|
# Dataset Card for "pubmed_physics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_physics
|
[
"region:us"
] |
2023-08-29T11:36:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4045589.405, "num_examples": 995}], "download_size": 2215468, "dataset_size": 4045589.405}}
|
2023-08-29T11:36:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_physics"
More Information needed
|
[
"# Dataset Card for \"pubmed_physics\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmed_physics\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pubmed_physics\"\n\nMore Information needed"
] |
2f1c4b2f0354ec6bd4f286abd3876468144c10d0
|
# Dataset Card for "95de681c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/95de681c
|
[
"region:us"
] |
2023-08-29T11:39:25+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1338, "dataset_size": 182}}
|
2023-08-29T11:39:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "95de681c"
More Information needed
|
[
"# Dataset Card for \"95de681c\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"95de681c\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"95de681c\"\n\nMore Information needed"
] |
4aa49d21ed2a38a5532acae8f372b971c0e169ad
|
# Dataset Card for "autotree_automl_bank-marketing_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_bank-marketing_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T11:47:22+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 174960000, "num_examples": 10000}, {"name": "validation", "num_bytes": 174960000, "num_examples": 10000}], "download_size": 72788389, "dataset_size": 349920000}}
|
2023-08-30T12:22:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_bank-marketing_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_bank-marketing_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_bank-marketing_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
33
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_bank-marketing_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
ac77ba58fc3828ded1d094111e6075f713c9a5cb
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
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).
### 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]
|
JamalSQ/JamalSQLab
|
[
"task_categories:text-generation",
"task_categories:token-classification",
"task_categories:text2text-generation",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:aa",
"language:sr",
"language:en",
"license:osl-3.0",
"code",
"chemistry",
"legal",
"not-for-all-audiences",
"finance",
"region:us"
] |
2023-08-29T11:57:41+00:00
|
{"language": ["aa", "sr", "en"], "license": "osl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "token-classification", "text2text-generation", "question-answering"], "pretty_name": "Sherminator", "tags": ["code", "chemistry", "legal", "not-for-all-audiences", "finance"]}
|
2023-08-30T08:12:39+00:00
|
[] |
[
"aa",
"sr",
"en"
] |
TAGS
#task_categories-text-generation #task_categories-token-classification #task_categories-text2text-generation #task_categories-question-answering #size_categories-100K<n<1M #language-Afar #language-Serbian #language-English #license-osl-3.0 #code #chemistry #legal #not-for-all-audiences #finance #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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#task_categories-text-generation #task_categories-token-classification #task_categories-text2text-generation #task_categories-question-answering #size_categories-100K<n<1M #language-Afar #language-Serbian #language-English #license-osl-3.0 #code #chemistry #legal #not-for-all-audiences #finance #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
108,
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32,
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6,
6,
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5,
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4,
10,
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8,
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7,
8,
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[
"passage: TAGS\n#task_categories-text-generation #task_categories-token-classification #task_categories-text2text-generation #task_categories-question-answering #size_categories-100K<n<1M #language-Afar #language-Serbian #language-English #license-osl-3.0 #code #chemistry #legal #not-for-all-audiences #finance #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
2f37f30da0f23b04109aa939f08a538023181869
|
# Dataset Card for "autotree_automl_electricity_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_electricity_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T12:01:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 174960000, "num_examples": 10000}, {"name": "validation", "num_bytes": 174960000, "num_examples": 10000}], "download_size": 101429747, "dataset_size": 349920000}}
|
2023-08-30T12:43:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_electricity_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_electricity_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_electricity_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
32
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_electricity_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
e199b6a9f29d51d4edc9cb994408648a5452d32a
|
# Dataset Card for "autotree_automl_heloc_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_heloc_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T12:10:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 328560000, "num_examples": 10000}, {"name": "validation", "num_bytes": 328560000, "num_examples": 10000}], "download_size": 133253810, "dataset_size": 657120000}}
|
2023-08-31T06:10:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_heloc_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_heloc_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_heloc_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
32
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_heloc_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
e047686d2864196a24f91dbc65c5f3cbbb5e13e9
|
# Dataset Card for "autotree_automl_california_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_california_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T12:10:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 185200000, "num_examples": 10000}, {"name": "validation", "num_bytes": 185200000, "num_examples": 10000}], "download_size": 149978405, "dataset_size": 370400000}}
|
2023-08-30T13:39:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_california_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_california_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_california_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
33
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_california_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
efc8f438ebe47ecebb602b9442761253d38064be
|
# Dataset Card for "prompt_injection_hackaprompt_gpt35"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
imoxto/prompt_injection_hackaprompt_gpt35
|
[
"region:us"
] |
2023-08-29T12:21:17+00:00
|
{"dataset_info": {"features": [{"name": "labels", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 271856355, "num_examples": 227042}], "download_size": 35972535, "dataset_size": 271856355}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T12:21:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prompt_injection_hackaprompt_gpt35"
More Information needed
|
[
"# Dataset Card for \"prompt_injection_hackaprompt_gpt35\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prompt_injection_hackaprompt_gpt35\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prompt_injection_hackaprompt_gpt35\"\n\nMore Information needed"
] |
76de43703f71d1ec99e7a49f462cca2abf2c5a7b
|
# データセットの概要
以下の、主語を拙者、語尾をござるに変更したデータセットを元に、語尾を「なのじゃ」に変換したデータセットである。
---
license: cc-by-sa-3.0
---
This dataset was using "bbz662bbz/databricks-dolly-15k-ja-gozaru"
This dataset is licensed under CC BY SA 3.0
bbz662bbz/databricks-dolly-15k-ja-gozaru
https://huggingface.co/datasets/bbz662bbz/databricks-dolly-15k-ja-gozaru
|
ToPo-ToPo/databricks-dolly-15k-ja-nanoja
|
[
"region:us"
] |
2023-08-29T12:25:27+00:00
|
{}
|
2023-09-26T00:17:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# データセットの概要
以下の、主語を拙者、語尾をござるに変更したデータセットを元に、語尾を「なのじゃ」に変換したデータセットである。
---
license: cc-by-sa-3.0
---
This dataset was using "bbz662bbz/databricks-dolly-15k-ja-gozaru"
This dataset is licensed under CC BY SA 3.0
bbz662bbz/databricks-dolly-15k-ja-gozaru
URL
|
[
"# データセットの概要\n以下の、主語を拙者、語尾をござるに変更したデータセットを元に、語尾を「なのじゃ」に変換したデータセットである。\n\n---\nlicense: cc-by-sa-3.0\n---\nThis dataset was using \"bbz662bbz/databricks-dolly-15k-ja-gozaru\"\nThis dataset is licensed under CC BY SA 3.0\n\nbbz662bbz/databricks-dolly-15k-ja-gozaru\nURL"
] |
[
"TAGS\n#region-us \n",
"# データセットの概要\n以下の、主語を拙者、語尾をござるに変更したデータセットを元に、語尾を「なのじゃ」に変換したデータセットである。\n\n---\nlicense: cc-by-sa-3.0\n---\nThis dataset was using \"bbz662bbz/databricks-dolly-15k-ja-gozaru\"\nThis dataset is licensed under CC BY SA 3.0\n\nbbz662bbz/databricks-dolly-15k-ja-gozaru\nURL"
] |
[
6,
115
] |
[
"passage: TAGS\n#region-us \n# データセットの概要\n以下の、主語を拙者、語尾をござるに変更したデータセットを元に、語尾を「なのじゃ」に変換したデータセットである。\n\n---\nlicense: cc-by-sa-3.0\n---\nThis dataset was using \"bbz662bbz/databricks-dolly-15k-ja-gozaru\"\nThis dataset is licensed under CC BY SA 3.0\n\nbbz662bbz/databricks-dolly-15k-ja-gozaru\nURL"
] |
e126ed1ba8de0714a57b228a8f84fa10f8d7abd1
|
# Dataset Card for "autotree_automl_covertype_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_covertype_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T12:41:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 205680000, "num_examples": 10000}, {"name": "validation", "num_bytes": 205680000, "num_examples": 10000}], "download_size": 149993354, "dataset_size": 411360000}}
|
2023-08-30T13:27:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_covertype_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_covertype_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_covertype_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
32
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_covertype_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
ebdcdf92b9e88392115c8b857e183585b7143a90
|
# Dataset Card for "autotree_automl_pol_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_pol_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T12:58:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 369520000, "num_examples": 10000}, {"name": "validation", "num_bytes": 369520000, "num_examples": 10000}], "download_size": 84319622, "dataset_size": 739040000}}
|
2023-08-30T13:16:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_pol_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_pol_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_pol_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_pol_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
61c4d96867e5a1311a23d3f85afcce90bcea773d
|
# Dataset Card for "weather_negative_positive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
davidadamczyk/weather_negative_positive
|
[
"region:us"
] |
2023-08-29T13:09:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25846.8, "num_examples": 150}, {"name": "test", "num_bytes": 17231.2, "num_examples": 100}], "download_size": 31304, "dataset_size": 43078.0}}
|
2023-08-29T13:09:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "weather_negative_positive"
More Information needed
|
[
"# Dataset Card for \"weather_negative_positive\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"weather_negative_positive\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"weather_negative_positive\"\n\nMore Information needed"
] |
4ba76f579d092afaf06c8be163e64874a5468ed3
|
# Dataset Card for "evol_instruct_code_filtered_38k"
Filtered version of `nickrosh/Evol-Instruct-Code-80k-v1`, with manual filtering, and automatic filtering based on quality and learning value classifiers.
|
vikp/evol_instruct_code_filtered_39k
|
[
"region:us"
] |
2023-08-29T13:35:42+00:00
|
{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "quality_prob", "dtype": "float64"}, {"name": "learning_prob", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 56854896.038860105, "num_examples": 39078}], "download_size": 37822990, "dataset_size": 56854896.038860105}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T16:35:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "evol_instruct_code_filtered_38k"
Filtered version of 'nickrosh/Evol-Instruct-Code-80k-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers.
|
[
"# Dataset Card for \"evol_instruct_code_filtered_38k\"\n\nFiltered version of 'nickrosh/Evol-Instruct-Code-80k-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"evol_instruct_code_filtered_38k\"\n\nFiltered version of 'nickrosh/Evol-Instruct-Code-80k-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers."
] |
[
6,
62
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"evol_instruct_code_filtered_38k\"\n\nFiltered version of 'nickrosh/Evol-Instruct-Code-80k-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers."
] |
38cf9960c895b0fa1507e653590d8bf19f098809
|
# Dataset Card for "retrieved_claims_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/retrieved_claims_val
|
[
"region:us"
] |
2023-08-29T13:37:57+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "retrieved_evidence", "sequence": "string"}, {"name": "retrieval_score", "sequence": "float64"}, {"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "lines", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6038497, "num_examples": 1500}], "download_size": 2990657, "dataset_size": 6038497}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T13:58:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "retrieved_claims_val"
More Information needed
|
[
"# Dataset Card for \"retrieved_claims_val\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"retrieved_claims_val\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"retrieved_claims_val\"\n\nMore Information needed"
] |
e6b3f8efb0af90782107a6df3aaa5c4a660382da
|
# Dataset Card for "retrieved_claims_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/retrieved_claims_test
|
[
"region:us"
] |
2023-08-29T13:37:59+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "retrieved_evidence", "sequence": "string"}, {"name": "retrieval_score", "sequence": "float64"}, {"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "lines", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6050543, "num_examples": 1500}], "download_size": 2972631, "dataset_size": 6050543}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T13:58:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "retrieved_claims_test"
More Information needed
|
[
"# Dataset Card for \"retrieved_claims_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"retrieved_claims_test\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"retrieved_claims_test\"\n\nMore Information needed"
] |
c3cd3c7ecda703241a67aec43c44b750da4141ae
|
# Dataset Card for Evaluation run of TheBloke/Llama-2-13B-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Llama-2-13B-GPTQ
- **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 [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific 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 agregated 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_TheBloke__Llama-2-13B-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T16:26:14.370378](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ/blob/main/results_2023-10-27T16-26-14.370378.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": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368251,
"f1": 0.06011535234899329,
"f1_stderr": 0.0013639179977941345,
"acc": 0.43730302009426913,
"acc_stderr": 0.010347143848267699
},
"harness|drop|3": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368251,
"f1": 0.06011535234899329,
"f1_stderr": 0.0013639179977941345
},
"harness|gsm8k|5": {
"acc": 0.11296436694465505,
"acc_stderr": 0.00871933902883308
},
"harness|winogrande|5": {
"acc": 0.7616416732438832,
"acc_stderr": 0.011974948667702316
}
}
```
### 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_TheBloke__Llama-2-13B-GPTQ
|
[
"region:us"
] |
2023-08-29T14:04:45+00:00
|
{"pretty_name": "Evaluation run of TheBloke/Llama-2-13B-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_TheBloke__Llama-2-13B-GPTQ\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T16:26:14.370378](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ/blob/main/results_2023-10-27T16-26-14.370378.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 \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368251,\n \"f1\": 0.06011535234899329,\n \"f1_stderr\": 0.0013639179977941345,\n \"acc\": 0.43730302009426913,\n \"acc_stderr\": 0.010347143848267699\n },\n \"harness|drop|3\": {\n \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368251,\n \"f1\": 0.06011535234899329,\n \"f1_stderr\": 0.0013639179977941345\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11296436694465505,\n \"acc_stderr\": 0.00871933902883308\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702316\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/Llama-2-13B-GPTQ", "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_08_29T15_04_20.709230", "path": ["**/details_harness|arc:challenge|25_2023-08-29T15:04:20.709230.parquet"]}, {"split": "2023_08_30T10_42_39.395336", "path": ["**/details_harness|arc:challenge|25_2023-08-30T10:42:39.395336.parquet"]}, {"split": "2023_08_31T11_12_42.998068", "path": ["**/details_harness|arc:challenge|25_2023-08-31T11:12:42.998068.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-31T11:12:42.998068.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T16_26_14.370378", "path": ["**/details_harness|drop|3_2023-10-27T16-26-14.370378.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T16-26-14.370378.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T16_26_14.370378", "path": ["**/details_harness|gsm8k|5_2023-10-27T16-26-14.370378.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T16-26-14.370378.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T15_04_20.709230", "path": ["**/details_harness|hellaswag|10_2023-08-29T15:04:20.709230.parquet"]}, {"split": "2023_08_30T10_42_39.395336", "path": ["**/details_harness|hellaswag|10_2023-08-30T10:42:39.395336.parquet"]}, {"split": "2023_08_31T11_12_42.998068", "path": ["**/details_harness|hellaswag|10_2023-08-31T11:12:42.998068.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-31T11:12:42.998068.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_29T15_04_20.709230", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T15:04:20.709230.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T15:04:20.709230.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T15:04:20.709230.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T15:04:20.709230.parquet", 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|
2023-10-27T15:26:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/Llama-2-13B-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/Llama-2-13B-GPTQ on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific 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 agregated 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 2023-10-27T16:26:14.370378(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 TheBloke/Llama-2-13B-GPTQ",
"## 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 TheBloke/Llama-2-13B-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-27T16:26:14.370378(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 TheBloke/Llama-2-13B-GPTQ",
"## 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 TheBloke/Llama-2-13B-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-27T16:26:14.370378(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 TheBloke/Llama-2-13B-GPTQ## 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 TheBloke/Llama-2-13B-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-10-27T16:26:14.370378(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
a0c0f2cf32a4d373026110b2d920402bb1709cc5
|
# Dataset Card for "concise536"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cestwc/concise536
|
[
"region:us"
] |
2023-08-29T14:13:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "cite", "dtype": "string"}, {"name": "wordy", "dtype": "string"}, {"name": "concise", "sequence": "string"}, {"name": "category", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "delete", "dtype": {"class_label": {"names": {"0": "not required", "1": "required"}}}}, {"name": "replace", "dtype": {"class_label": {"names": {"0": "not required", "1": "required"}}}}, {"name": "rewrite", "dtype": {"class_label": {"names": {"0": "not required", "1": "required"}}}}], "splits": [{"name": "validation", "num_bytes": 3692, "num_examples": 14}, {"name": "test", "num_bytes": 161635, "num_examples": 536}], "download_size": 79866, "dataset_size": 165327}}
|
2023-08-29T14:13:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "concise536"
More Information needed
|
[
"# Dataset Card for \"concise536\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"concise536\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"concise536\"\n\nMore Information needed"
] |
5f63c8a5317c71da56f09632e9c20bc30c9d3a14
|
# Dataset Card for Evaluation run of xzuyn/LLaMa-2-PeanutButter_v4-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xzuyn/LLaMa-2-PeanutButter_v4-7B
- **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 [xzuyn/LLaMa-2-PeanutButter_v4-7B](https://huggingface.co/xzuyn/LLaMa-2-PeanutButter_v4-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_xzuyn__LLaMa-2-PeanutButter_v4-7B",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T15:15:59.631802](https://huggingface.co/datasets/open-llm-leaderboard/details_xzuyn__LLaMa-2-PeanutButter_v4-7B/blob/main/results_2023-08-29T15%3A15%3A59.631802.json):
```python
{
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"acc_norm": 0.4793512530654778,
"acc_norm_stderr": 0.03541409593269912,
"mc1": 0.26805385556915545,
"mc1_stderr": 0.015506204722834557,
"mc2": 0.42310904021377665,
"mc2_stderr": 0.015624011969941223
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014609667440892567,
"acc_norm": 0.5486348122866894,
"acc_norm_stderr": 0.014542104569955265
},
"harness|hellaswag|10": {
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"acc_stderr": 0.004846886929763466,
"acc_norm": 0.8078072097191794,
"acc_norm_stderr": 0.003932184843841659
},
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"acc_norm": 0.31,
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},
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"acc_norm_stderr": 0.04304979692464243
},
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"acc_norm_stderr": 0.040260970832965585
},
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},
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"acc_stderr": 0.030762134874500482,
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"acc_norm_stderr": 0.030762134874500482
},
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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-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.42310904021377665,
"mc2_stderr": 0.015624011969941223
}
}
```
### 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_xzuyn__LLaMa-2-PeanutButter_v4-7B
|
[
"region:us"
] |
2023-08-29T14:16:23+00:00
|
{"pretty_name": "Evaluation run of xzuyn/LLaMa-2-PeanutButter_v4-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [xzuyn/LLaMa-2-PeanutButter_v4-7B](https://huggingface.co/xzuyn/LLaMa-2-PeanutButter_v4-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_xzuyn__LLaMa-2-PeanutButter_v4-7B\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T15:15:59.631802](https://huggingface.co/datasets/open-llm-leaderboard/details_xzuyn__LLaMa-2-PeanutButter_v4-7B/blob/main/results_2023-08-29T15%3A15%3A59.631802.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4754535953456773,\n \"acc_stderr\": 0.03543074449128995,\n \"acc_norm\": 0.4793512530654778,\n \"acc_norm_stderr\": 0.03541409593269912,\n \"mc1\": 0.26805385556915545,\n \"mc1_stderr\": 0.015506204722834557,\n \"mc2\": 0.42310904021377665,\n \"mc2_stderr\": 0.015624011969941223\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.507679180887372,\n \"acc_stderr\": 0.014609667440892567,\n \"acc_norm\": 0.5486348122866894,\n \"acc_norm_stderr\": 0.014542104569955265\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6188010356502689,\n \"acc_stderr\": 0.004846886929763466,\n \"acc_norm\": 0.8078072097191794,\n \"acc_norm_stderr\": 0.003932184843841659\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.040260970832965585,\n \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.040260970832965585\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.4867924528301887,\n \"acc_stderr\": 0.030762134874500482,\n \"acc_norm\": 0.4867924528301887,\n \"acc_norm_stderr\": 0.030762134874500482\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04181210050035455,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04181210050035455\n },\n \"harness|hendrycksTest-college_chemistry|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_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\": 0.35,\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\": 0.43352601156069365,\n \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.43352601156069365,\n \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.42127659574468085,\n \"acc_stderr\": 0.03227834510146267,\n \"acc_norm\": 0.42127659574468085,\n \"acc_norm_stderr\": 0.03227834510146267\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\": 0.4482758620689655,\n \"acc_stderr\": 0.041443118108781506,\n \"acc_norm\": 0.4482758620689655,\n \"acc_norm_stderr\": 0.041443118108781506\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.023636975996101796,\n \"acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.023636975996101796\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n \"acc_stderr\": 0.04073524322147126,\n \"acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.04073524322147126\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5032258064516129,\n \"acc_stderr\": 0.028443414226438316,\n \"acc_norm\": 0.5032258064516129,\n \"acc_norm_stderr\": 0.028443414226438316\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3694581280788177,\n \"acc_stderr\": 0.03395970381998573,\n \"acc_norm\": 0.3694581280788177,\n \"acc_norm_stderr\": 0.03395970381998573\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6242424242424243,\n \"acc_stderr\": 0.03781887353205982,\n \"acc_norm\": 0.6242424242424243,\n \"acc_norm_stderr\": 0.03781887353205982\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5909090909090909,\n \"acc_stderr\": 0.03502975799413007,\n \"acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.03502975799413007\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7202072538860104,\n \"acc_stderr\": 0.032396370467357036,\n \"acc_norm\": 0.7202072538860104,\n \"acc_norm_stderr\": 0.032396370467357036\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.025294608023986476,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.025294608023986476\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945287,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945287\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.4579831932773109,\n \"acc_stderr\": 0.03236361111951941,\n \"acc_norm\": 0.4579831932773109,\n \"acc_norm_stderr\": 0.03236361111951941\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6385321100917432,\n \"acc_stderr\": 0.020598082009937374,\n \"acc_norm\": 0.6385321100917432,\n \"acc_norm_stderr\": 0.020598082009937374\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5686274509803921,\n \"acc_stderr\": 0.03476099060501636,\n \"acc_norm\": 0.5686274509803921,\n \"acc_norm_stderr\": 0.03476099060501636\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5949367088607594,\n \"acc_stderr\": 0.03195514741370671,\n \"acc_norm\": 0.5949367088607594,\n \"acc_norm_stderr\": 0.03195514741370671\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5381165919282511,\n \"acc_stderr\": 0.03346015011973228,\n \"acc_norm\": 0.5381165919282511,\n \"acc_norm_stderr\": 0.03346015011973228\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5114503816793893,\n \"acc_stderr\": 0.043841400240780176,\n \"acc_norm\": 0.5114503816793893,\n \"acc_norm_stderr\": 0.043841400240780176\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.5619834710743802,\n \"acc_stderr\": 0.04529146804435792,\n \"acc_norm\": 0.5619834710743802,\n \"acc_norm_stderr\": 0.04529146804435792\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.04826217294139894,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.04826217294139894\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.49693251533742333,\n \"acc_stderr\": 0.03928297078179663,\n \"acc_norm\": 0.49693251533742333,\n \"acc_norm_stderr\": 0.03928297078179663\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6019417475728155,\n \"acc_stderr\": 0.04846748253977239,\n \"acc_norm\": 0.6019417475728155,\n \"acc_norm_stderr\": 0.04846748253977239\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.688034188034188,\n \"acc_stderr\": 0.030351527323344948,\n \"acc_norm\": 0.688034188034188,\n \"acc_norm_stderr\": 0.030351527323344948\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6155810983397191,\n \"acc_stderr\": 0.01739568874281962,\n \"acc_norm\": 0.6155810983397191,\n \"acc_norm_stderr\": 0.01739568874281962\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.476878612716763,\n \"acc_stderr\": 0.026890297881303128,\n \"acc_norm\": 0.476878612716763,\n \"acc_norm_stderr\": 0.026890297881303128\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2994413407821229,\n \"acc_stderr\": 0.015318257745976708,\n \"acc_norm\": 0.2994413407821229,\n \"acc_norm_stderr\": 0.015318257745976708\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5261437908496732,\n \"acc_stderr\": 0.028590752958852387,\n \"acc_norm\": 0.5261437908496732,\n \"acc_norm_stderr\": 0.028590752958852387\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5787781350482315,\n \"acc_stderr\": 0.02804339985821063,\n \"acc_norm\": 0.5787781350482315,\n \"acc_norm_stderr\": 0.02804339985821063\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5061728395061729,\n \"acc_stderr\": 0.027818623962583295,\n \"acc_norm\": 0.5061728395061729,\n \"acc_norm_stderr\": 0.027818623962583295\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3723404255319149,\n \"acc_stderr\": 0.02883892147125146,\n \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.02883892147125146\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.36766623207301175,\n \"acc_stderr\": 0.012314845910071691,\n \"acc_norm\": 0.36766623207301175,\n \"acc_norm_stderr\": 0.012314845910071691\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.030290619180485694,\n \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.030290619180485694\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.434640522875817,\n \"acc_stderr\": 0.02005426920072646,\n \"acc_norm\": 0.434640522875817,\n \"acc_norm_stderr\": 0.02005426920072646\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.509090909090909,\n \"acc_stderr\": 0.04788339768702861,\n \"acc_norm\": 0.509090909090909,\n \"acc_norm_stderr\": 0.04788339768702861\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.4897959183673469,\n \"acc_stderr\": 0.03200255347893783,\n \"acc_norm\": 0.4897959183673469,\n \"acc_norm_stderr\": 0.03200255347893783\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6169154228855721,\n \"acc_stderr\": 0.0343751933733825,\n \"acc_norm\": 0.6169154228855721,\n \"acc_norm_stderr\": 0.0343751933733825\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.42168674698795183,\n \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7076023391812866,\n \"acc_stderr\": 0.03488647713457923,\n \"acc_norm\": 0.7076023391812866,\n \"acc_norm_stderr\": 0.03488647713457923\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26805385556915545,\n \"mc1_stderr\": 0.015506204722834557,\n \"mc2\": 0.42310904021377665,\n \"mc2_stderr\": 0.015624011969941223\n }\n}\n```", "repo_url": "https://huggingface.co/xzuyn/LLaMa-2-PeanutButter_v4-7B", "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_08_29T15_15_59.631802", "path": ["**/details_harness|arc:challenge|25_2023-08-29T15:15:59.631802.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T15:15:59.631802.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T15_15_59.631802", "path": ["**/details_harness|hellaswag|10_2023-08-29T15:15:59.631802.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T15:15:59.631802.parquet"]}]}, {"config_name": "harness_hendrycksTest", "data_files": [{"split": "2023_08_29T15_15_59.631802", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T15:15:59.631802.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T15:15:59.631802.parquet", 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|
2023-08-29T14:17:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of xzuyn/LLaMa-2-PeanutButter_v4-7B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model xzuyn/LLaMa-2-PeanutButter_v4-7B on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T15:15:59.631802:
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 xzuyn/LLaMa-2-PeanutButter_v4-7B",
"## 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 xzuyn/LLaMa-2-PeanutButter_v4-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T15:15:59.631802:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 xzuyn/LLaMa-2-PeanutButter_v4-7B",
"## 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 xzuyn/LLaMa-2-PeanutButter_v4-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T15:15:59.631802:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 xzuyn/LLaMa-2-PeanutButter_v4-7B## 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 xzuyn/LLaMa-2-PeanutButter_v4-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T15:15:59.631802:### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
186e4c4c0d2a525a0436d42a6519cf7db38a43e7
|
# Dataset Card for "evol_codealpaca_filtered_86k"
Filtered version of `theblackcat102/evol-codealpaca-v1`, with manual filtering, and automatic filtering based on quality and learning value classifiers.
|
vikp/evol_codealpaca_filtered_87k
|
[
"region:us"
] |
2023-08-29T14:17:12+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "quality_prob", "dtype": "float64"}, {"name": "learning_prob", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 194291512.64351812, "num_examples": 87705}], "download_size": 107933444, "dataset_size": 194291512.64351812}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T16:25:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "evol_codealpaca_filtered_86k"
Filtered version of 'theblackcat102/evol-codealpaca-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers.
|
[
"# Dataset Card for \"evol_codealpaca_filtered_86k\"\n\nFiltered version of 'theblackcat102/evol-codealpaca-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"evol_codealpaca_filtered_86k\"\n\nFiltered version of 'theblackcat102/evol-codealpaca-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers."
] |
[
6,
59
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"evol_codealpaca_filtered_86k\"\n\nFiltered version of 'theblackcat102/evol-codealpaca-v1', with manual filtering, and automatic filtering based on quality and learning value classifiers."
] |
717b67b300dab0136055089068448d4cc739bce4
|
# Dataset Card for "fwv2_random_rare_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_rare_train_10_eval_10
|
[
"region:us"
] |
2023-08-29T14:31:21+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3025, "num_examples": 30}, {"name": "train_doc2id", "num_bytes": 1821, "num_examples": 20}, {"name": "train_id2doc", "num_bytes": 1881, "num_examples": 20}, {"name": "train_find_word", "num_bytes": 1144, "num_examples": 10}, {"name": "eval_find_word", "num_bytes": 1136, "num_examples": 10}, {"name": "id_context_mapping", "num_bytes": 1241, "num_examples": 20}], "download_size": 0, "dataset_size": 10248}}
|
2023-08-29T15:07:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_rare_train_10_eval_10"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_rare_train_10_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_rare_train_10_eval_10\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_rare_train_10_eval_10\"\n\nMore Information needed"
] |
f059c5f0b3d627976b69afd1ea5ca0deea0d623e
|
# Dataset Card for "FloCo_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mrm8488/FloCo_train
|
[
"region:us"
] |
2023-08-29T14:37:42+00:00
|
{"dataset_info": {"features": [{"name": "common_id", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1530119, "num_examples": 10102}], "download_size": 843087, "dataset_size": 1530119}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T14:37:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FloCo_train"
More Information needed
|
[
"# Dataset Card for \"FloCo_train\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FloCo_train\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FloCo_train\"\n\nMore Information needed"
] |
87faa7d705fe809d0430f2e8938bb68110812119
|
# Dataset Card for "FloCo_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mrm8488/FloCo_val
|
[
"region:us"
] |
2023-08-29T14:37:51+00:00
|
{"dataset_info": {"features": [{"name": "common_id", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 156818, "num_examples": 594}], "download_size": 67865, "dataset_size": 156818}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T14:37:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FloCo_val"
More Information needed
|
[
"# Dataset Card for \"FloCo_val\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FloCo_val\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FloCo_val\"\n\nMore Information needed"
] |
8cb9b15ee7ef92261fde0ca7b0193bb91f95aa04
|
# Dataset Card for "FloCo_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mrm8488/FloCo_test
|
[
"region:us"
] |
2023-08-29T14:38:00+00:00
|
{"dataset_info": {"features": [{"name": "common_id", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 268542, "num_examples": 1188}], "download_size": 133517, "dataset_size": 268542}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T14:38:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FloCo_test"
More Information needed
|
[
"# Dataset Card for \"FloCo_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FloCo_test\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FloCo_test\"\n\nMore Information needed"
] |
4fb0ef1284bf8c316dff3889f6cffc573aed9c3e
|
# Dataset Card for Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1
- **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 [DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1](https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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 agregated 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T05:14:36.225763](https://huggingface.co/datasets/open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1/blob/main/results_2023-10-25T05-14-36.225763.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": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834515013,
"f1": 0.060823196308725104,
"f1_stderr": 0.0014310538463956732,
"acc": 0.3286670500301285,
"acc_stderr": 0.007440585256579462
},
"harness|drop|3": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834515013,
"f1": 0.060823196308725104,
"f1_stderr": 0.0014310538463956732
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245434
},
"harness|winogrande|5": {
"acc": 0.654301499605367,
"acc_stderr": 0.01336659695193438
}
}
```
### 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1
|
[
"region:us"
] |
2023-08-29T14:41:23+00:00
|
{"pretty_name": "Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1", "dataset_summary": "Dataset automatically created during the evaluation run of model [DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1](https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 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 agregated 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T05:14:36.225763](https://huggingface.co/datasets/open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1/blob/main/results_2023-10-25T05-14-36.225763.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 \"em\": 0.0025167785234899327,\n \"em_stderr\": 0.0005131152834515013,\n \"f1\": 0.060823196308725104,\n \"f1_stderr\": 0.0014310538463956732,\n \"acc\": 0.3286670500301285,\n \"acc_stderr\": 0.007440585256579462\n },\n \"harness|drop|3\": {\n \"em\": 0.0025167785234899327,\n \"em_stderr\": 0.0005131152834515013,\n \"f1\": 0.060823196308725104,\n \"f1_stderr\": 0.0014310538463956732\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \"acc_stderr\": 0.0015145735612245434\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.654301499605367,\n \"acc_stderr\": 0.01336659695193438\n }\n}\n```", "repo_url": "https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": 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|
2023-10-25T04:14:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1 on the Open LLM Leaderboard.
The dataset is composed of 64 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 agregated 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 2023-10-25T05:14:36.225763(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1",
"## 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 DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-25T05:14:36.225763(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"### Data Instances",
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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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"# Dataset Card for Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1",
"## 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 DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-25T05:14:36.225763(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"### Data Instances",
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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 DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1## 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 DanielSc4/RedPajama-INCITE-Chat-3B-v1-RL-LoRA-8bit-test1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-25T05:14:36.225763(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
a2bd61d9d7771115690c6c78a6e05c2e105549b7
|
# Dataset Card for "fwv2_random_rare_train_100_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_rare_train_100_eval_100
|
[
"region:us"
] |
2023-08-29T14:41:45+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30323, "num_examples": 300}, {"name": "train_doc2id", "num_bytes": 18225, "num_examples": 200}, {"name": "train_id2doc", "num_bytes": 18825, "num_examples": 200}, {"name": "train_find_word", "num_bytes": 11498, "num_examples": 100}, {"name": "eval_find_word", "num_bytes": 11344, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 12425, "num_examples": 200}], "download_size": 0, "dataset_size": 102640}}
|
2023-08-29T15:09:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_rare_train_100_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_rare_train_100_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_rare_train_100_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_rare_train_100_eval_100\"\n\nMore Information needed"
] |
4cbbdfd01720371251cdcdf991348e550c03566d
|
Taken from a bunch of datasets, including PRM800K and camelAI
Has:
Math
Chemistry
Physics
|
NobodyExistsOnTheInternet/beforeeconsconvo
|
[
"license:mit",
"region:us"
] |
2023-08-29T14:43:20+00:00
|
{"license": "mit"}
|
2023-08-29T14:52:40+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
Taken from a bunch of datasets, including PRM800K and camelAI
Has:
Math
Chemistry
Physics
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
45fb364ab858ffd3bf72b8f16c73252cd3d33fdd
|
# Dataset Card for "fwv2_random_rare_train_1000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_rare_train_1000_eval_100
|
[
"region:us"
] |
2023-08-29T14:44:20+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 218225, "num_examples": 2100}, {"name": "train_doc2id", "num_bytes": 100243, "num_examples": 1100}, {"name": "train_id2doc", "num_bytes": 103543, "num_examples": 1100}, {"name": "train_find_word", "num_bytes": 114682, "num_examples": 1000}, {"name": "eval_find_word", "num_bytes": 11342, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 68343, "num_examples": 1100}], "download_size": 0, "dataset_size": 616378}}
|
2023-08-29T15:12:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_rare_train_1000_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_rare_train_1000_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_rare_train_1000_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_rare_train_1000_eval_100\"\n\nMore Information needed"
] |
fd43d9aea2200210507cdc9d126ab79406c5adf6
|
# Dataset Card for "fwv2_squad_rare_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_squad_rare_train_10_eval_10
|
[
"region:us"
] |
2023-08-29T14:49:10+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4812, "num_examples": 30}, {"name": "train_doc2id", "num_bytes": 3534, "num_examples": 20}, {"name": "train_id2doc", "num_bytes": 3594, "num_examples": 20}, {"name": "train_find_word", "num_bytes": 1218, "num_examples": 10}, {"name": "eval_find_word", "num_bytes": 1174, "num_examples": 10}, {"name": "id_context_mapping", "num_bytes": 2954, "num_examples": 20}], "download_size": 25115, "dataset_size": 17286}}
|
2023-08-29T15:14:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_rare_train_10_eval_10"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_rare_train_10_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_rare_train_10_eval_10\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_rare_train_10_eval_10\"\n\nMore Information needed"
] |
7ff3710bee2d358924b25ad78d82cf7d122a7cf9
|
# Dataset Card for Evaluation run of IkariDev/Athena-tmp
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/IkariDev/Athena-tmp
- **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 [IkariDev/Athena-tmp](https://huggingface.co/IkariDev/Athena-tmp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_IkariDev__Athena-tmp",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T15:50:42.106753](https://huggingface.co/datasets/open-llm-leaderboard/details_IkariDev__Athena-tmp/blob/main/results_2023-08-29T15%3A50%3A42.106753.json):
```python
{
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"acc_stderr": 0.03407664559390293,
"acc_norm": 0.5926858740874733,
"acc_norm_stderr": 0.034057449595187576,
"mc1": 0.38922888616891066,
"mc1_stderr": 0.017068552680690328,
"mc2": 0.5536706803409501,
"mc2_stderr": 0.01611557269809252
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014478005694182531,
"acc_norm": 0.5921501706484642,
"acc_norm_stderr": 0.014361097288449696
},
"harness|hellaswag|10": {
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"acc_norm_stderr": 0.003823591814133031
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
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"acc_stderr": 0.043163785995113245,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
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"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849726
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_stderr": 0.030052580579557845,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.030052580579557845
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.7083333333333334,
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},
"harness|hendrycksTest-college_chemistry|5": {
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},
"harness|hendrycksTest-college_computer_science|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-high_school_world_history|5": {
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"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|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-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
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"harness|truthfulqa:mc|0": {
"mc1": 0.38922888616891066,
"mc1_stderr": 0.017068552680690328,
"mc2": 0.5536706803409501,
"mc2_stderr": 0.01611557269809252
}
}
```
### 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_IkariDev__Athena-tmp
|
[
"region:us"
] |
2023-08-29T14:51:06+00:00
|
{"pretty_name": "Evaluation run of IkariDev/Athena-tmp", "dataset_summary": "Dataset automatically created during the evaluation run of model [IkariDev/Athena-tmp](https://huggingface.co/IkariDev/Athena-tmp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_IkariDev__Athena-tmp\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T15:50:42.106753](https://huggingface.co/datasets/open-llm-leaderboard/details_IkariDev__Athena-tmp/blob/main/results_2023-08-29T15%3A50%3A42.106753.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5888874553745688,\n \"acc_stderr\": 0.03407664559390293,\n \"acc_norm\": 0.5926858740874733,\n \"acc_norm_stderr\": 0.034057449595187576,\n \"mc1\": 0.38922888616891066,\n \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5536706803409501,\n \"mc2_stderr\": 0.01611557269809252\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5674061433447098,\n \"acc_stderr\": 0.014478005694182531,\n \"acc_norm\": 0.5921501706484642,\n \"acc_norm_stderr\": 0.014361097288449696\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6218880701055567,\n \"acc_stderr\": 0.004839247332606038,\n \"acc_norm\": 0.8212507468631747,\n \"acc_norm_stderr\": 0.003823591814133031\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\": 0.5185185185185185,\n \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849726,\n \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849726\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.030052580579557845,\n \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.030052580579557845\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n \"acc_stderr\": 0.03800968060554859,\n \"acc_norm\": 0.7083333333333334,\n \"acc_norm_stderr\": 0.03800968060554859\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n 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[{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T15:50:42.106753.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T15_50_42.106753", "path": ["results_2023-08-29T15:50:42.106753.parquet"]}, {"split": "latest", "path": ["results_2023-08-29T15:50:42.106753.parquet"]}]}]}
|
2023-08-29T14:52:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of IkariDev/Athena-tmp
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model IkariDev/Athena-tmp on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T15:50:42.106753:
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 Card for Evaluation run of IkariDev/Athena-tmp",
"## 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 IkariDev/Athena-tmp on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T15:50:42.106753:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 IkariDev/Athena-tmp",
"## 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 IkariDev/Athena-tmp on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T15:50:42.106753:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 IkariDev/Athena-tmp## 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 IkariDev/Athena-tmp on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T15:50:42.106753:### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
f89e589584049d5e0d0c496427700d52fd3bc626
|
# Dataset Card for "fwv2_squad_rare_train_100_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_squad_rare_train_100_eval_100
|
[
"region:us"
] |
2023-08-29T14:51:43+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48776, "num_examples": 300}, {"name": "train_doc2id", "num_bytes": 35788, "num_examples": 200}, {"name": "train_id2doc", "num_bytes": 36388, "num_examples": 200}, {"name": "train_find_word", "num_bytes": 12388, "num_examples": 100}, {"name": "eval_find_word", "num_bytes": 11774, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 29988, "num_examples": 200}], "download_size": 115703, "dataset_size": 175102}}
|
2023-08-29T15:17:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_rare_train_100_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_rare_train_100_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_rare_train_100_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_rare_train_100_eval_100\"\n\nMore Information needed"
] |
798cace1ea623792e19cc1f97c5f51751c81adf0
|
# Dataset Card for "autotree_automl_eye_movements_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_eye_movements_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T14:53:23+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 308080000, "num_examples": 10000}, {"name": "validation", "num_bytes": 308080000, "num_examples": 10000}], "download_size": 210015405, "dataset_size": 616160000}}
|
2023-08-30T16:23:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_eye_movements_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_eye_movements_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_eye_movements_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_eye_movements_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
0b6aba86b0d745b60f98b49216914d6749d3869b
|
# Dataset Card for "fwv2_squad_rare_train_1000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_squad_rare_train_1000_eval_100
|
[
"region:us"
] |
2023-08-29T14:54:18+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 321689, "num_examples": 2100}, {"name": "train_doc2id", "num_bytes": 195355, "num_examples": 1100}, {"name": "train_id2doc", "num_bytes": 198655, "num_examples": 1100}, {"name": "train_find_word", "num_bytes": 123034, "num_examples": 1000}, {"name": "eval_find_word", "num_bytes": 11763, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 163455, "num_examples": 1100}], "download_size": 576167, "dataset_size": 1013951}}
|
2023-08-29T15:20:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_rare_train_1000_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_rare_train_1000_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_rare_train_1000_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_rare_train_1000_eval_100\"\n\nMore Information needed"
] |
d1db63cdd06b281d6045b5c1c6eb0bb3028a1da9
|
# Dataset Card for "ai-incidents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vitaliy-sharandin/ai-incidents
|
[
"region:us"
] |
2023-08-29T15:00:05+00:00
|
{"dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "incident_id", "dtype": "int64"}, {"name": "date", "dtype": "timestamp[ns]"}, {"name": "reports", "dtype": "string"}, {"name": "Alleged deployer of AI system", "dtype": "string"}, {"name": "Alleged developer of AI system", "dtype": "string"}, {"name": "Alleged harmed or nearly harmed parties", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "year", "dtype": "int64"}, {"name": "spacy_negative_outcomes", "dtype": "string"}, {"name": "keybert_negative_outcomes", "dtype": "string"}, {"name": "Cluster", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 271118, "num_examples": 514}], "download_size": 165345, "dataset_size": 271118}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T22:36:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ai-incidents"
More Information needed
|
[
"# Dataset Card for \"ai-incidents\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ai-incidents\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ai-incidents\"\n\nMore Information needed"
] |
cd7464242b1fc194f99eb8aa4a193c634413b4c4
|
# Dataset Card for Evaluation run of Writer/InstructPalmyra-20b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Writer/InstructPalmyra-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 [Writer/InstructPalmyra-20b](https://huggingface.co/Writer/InstructPalmyra-20b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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 agregated 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_Writer__InstructPalmyra-20b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T18:44:54.114721](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__InstructPalmyra-20b/blob/main/results_2023-10-12T18-44-54.114721.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": {
"em": 0.01929530201342282,
"em_stderr": 0.0014087520774405866,
"f1": 0.08355075503355748,
"f1_stderr": 0.0019786281704718585,
"acc": 0.33648760481410367,
"acc_stderr": 0.008897385527705382
},
"harness|drop|3": {
"em": 0.01929530201342282,
"em_stderr": 0.0014087520774405866,
"f1": 0.08355075503355748,
"f1_stderr": 0.0019786281704718585
},
"harness|gsm8k|5": {
"acc": 0.02577710386656558,
"acc_stderr": 0.004365042953621808
},
"harness|winogrande|5": {
"acc": 0.6471981057616417,
"acc_stderr": 0.013429728101788956
}
}
```
### 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_Writer__InstructPalmyra-20b
|
[
"region:us"
] |
2023-08-29T15:05:04+00:00
|
{"pretty_name": "Evaluation run of Writer/InstructPalmyra-20b", "dataset_summary": "Dataset automatically created during the evaluation run of model [Writer/InstructPalmyra-20b](https://huggingface.co/Writer/InstructPalmyra-20b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 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 agregated 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_Writer__InstructPalmyra-20b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-12T18:44:54.114721](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__InstructPalmyra-20b/blob/main/results_2023-10-12T18-44-54.114721.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 \"em\": 0.01929530201342282,\n \"em_stderr\": 0.0014087520774405866,\n \"f1\": 0.08355075503355748,\n \"f1_stderr\": 0.0019786281704718585,\n \"acc\": 0.33648760481410367,\n \"acc_stderr\": 0.008897385527705382\n },\n \"harness|drop|3\": {\n \"em\": 0.01929530201342282,\n \"em_stderr\": 0.0014087520774405866,\n \"f1\": 0.08355075503355748,\n \"f1_stderr\": 0.0019786281704718585\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02577710386656558,\n \"acc_stderr\": 0.004365042953621808\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6471981057616417,\n \"acc_stderr\": 0.013429728101788956\n }\n}\n```", "repo_url": "https://huggingface.co/Writer/InstructPalmyra-20b", "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_08_29T16_04_46.105936", "path": ["**/details_harness|arc:challenge|25_2023-08-29T16:04:46.105936.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T16:04:46.105936.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_12T18_44_54.114721", "path": ["**/details_harness|drop|3_2023-10-12T18-44-54.114721.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-12T18-44-54.114721.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_12T18_44_54.114721", "path": ["**/details_harness|gsm8k|5_2023-10-12T18-44-54.114721.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-12T18-44-54.114721.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T16_04_46.105936", "path": ["**/details_harness|hellaswag|10_2023-08-29T16:04:46.105936.parquet"]}, {"split": "latest", "path": 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"2023_08_29T16_04_46.105936", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T16:04:46.105936.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T16:04:46.105936.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2023_08_29T16_04_46.105936", "path": ["**/details_harness|hendrycksTest-anatomy|5_2023-08-29T16:04:46.105936.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-anatomy|5_2023-08-29T16:04:46.105936.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2023_08_29T16_04_46.105936", "path": ["**/details_harness|hendrycksTest-astronomy|5_2023-08-29T16:04:46.105936.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-astronomy|5_2023-08-29T16:04:46.105936.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2023_08_29T16_04_46.105936", 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["**/details_harness|truthfulqa:mc|0_2023-08-29T16:04:46.105936.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T16:04:46.105936.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_12T18_44_54.114721", "path": ["**/details_harness|winogrande|5_2023-10-12T18-44-54.114721.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-12T18-44-54.114721.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T16_04_46.105936", "path": ["results_2023-08-29T16:04:46.105936.parquet"]}, {"split": "2023_10_12T18_44_54.114721", "path": ["results_2023-10-12T18-44-54.114721.parquet"]}, {"split": "latest", "path": ["results_2023-10-12T18-44-54.114721.parquet"]}]}]}
|
2023-10-12T17:45:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Writer/InstructPalmyra-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 Writer/InstructPalmyra-20b on the Open LLM Leaderboard.
The dataset is composed of 64 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 agregated 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 2023-10-12T18:44:54.114721(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 Card for Evaluation run of Writer/InstructPalmyra-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 Writer/InstructPalmyra-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-12T18:44:54.114721(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
"### Source Data",
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"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## 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 Writer/InstructPalmyra-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-12T18:44:54.114721(note that their might be results for other tasks in 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 Writer/InstructPalmyra-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 Writer/InstructPalmyra-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-12T18:44:54.114721(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
616df2ced1cbb50e7ddb1e1a9e4d8270e373b1c3
|
# Dataset Card for Evaluation run of dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16
- **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 [dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16](https://huggingface.co/dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T21:13:05.130565](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16/blob/main/results_2023-08-29T21%3A13%3A05.130565.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": 0.4690258680406254,
"acc_stderr": 0.03526595221704812,
"acc_norm": 0.47314486143663886,
"acc_norm_stderr": 0.03525068980912334,
"mc1": 0.28151774785801714,
"mc1_stderr": 0.01574402724825605,
"mc2": 0.43311675077794465,
"mc2_stderr": 0.014085064609011494
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014611305705056995,
"acc_norm": 0.5409556313993175,
"acc_norm_stderr": 0.014562291073601227
},
"harness|hellaswag|10": {
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"acc_stderr": 0.00490641198447679,
"acc_norm": 0.7913762198765186,
"acc_norm_stderr": 0.004054944548370489
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4888888888888889,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.4888888888888889,
"acc_norm_stderr": 0.04318275491977976
},
"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": {
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"acc_norm": 0.52,
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
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"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.014242630070574915
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4934640522875817,
"acc_stderr": 0.028627470550556047,
"acc_norm": 0.4934640522875817,
"acc_norm_stderr": 0.028627470550556047
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.594855305466238,
"acc_stderr": 0.027882383791325953,
"acc_norm": 0.594855305466238,
"acc_norm_stderr": 0.027882383791325953
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.027815973433878014,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.027815973433878014
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3617021276595745,
"acc_stderr": 0.028663820147199492,
"acc_norm": 0.3617021276595745,
"acc_norm_stderr": 0.028663820147199492
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3559322033898305,
"acc_stderr": 0.012228645537277566,
"acc_norm": 0.3559322033898305,
"acc_norm_stderr": 0.012228645537277566
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.030320243265004144,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.030320243265004144
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.02008736207670286,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.02008736207670286
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5818181818181818,
"acc_stderr": 0.04724577405731572,
"acc_norm": 0.5818181818181818,
"acc_norm_stderr": 0.04724577405731572
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5265306122448979,
"acc_stderr": 0.03196412734523272,
"acc_norm": 0.5265306122448979,
"acc_norm_stderr": 0.03196412734523272
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6417910447761194,
"acc_stderr": 0.03390393042268813,
"acc_norm": 0.6417910447761194,
"acc_norm_stderr": 0.03390393042268813
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-virology|5": {
"acc": 0.41566265060240964,
"acc_stderr": 0.038367221765980515,
"acc_norm": 0.41566265060240964,
"acc_norm_stderr": 0.038367221765980515
},
"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": 0.28151774785801714,
"mc1_stderr": 0.01574402724825605,
"mc2": 0.43311675077794465,
"mc2_stderr": 0.014085064609011494
}
}
```
### 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_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16
|
[
"region:us"
] |
2023-08-29T15:05:09+00:00
|
{"pretty_name": "Evaluation run of dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16", "dataset_summary": "Dataset automatically created during the evaluation run of model [dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16](https://huggingface.co/dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 agregated 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_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T21:13:05.130565](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16/blob/main/results_2023-08-29T21%3A13%3A05.130565.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\": 0.4690258680406254,\n \"acc_stderr\": 0.03526595221704812,\n \"acc_norm\": 0.47314486143663886,\n \"acc_norm_stderr\": 0.03525068980912334,\n \"mc1\": 0.28151774785801714,\n \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.43311675077794465,\n \"mc2_stderr\": 0.014085064609011494\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.49829351535836175,\n \"acc_stderr\": 0.014611305705056995,\n \"acc_norm\": 0.5409556313993175,\n \"acc_norm_stderr\": 0.014562291073601227\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5910177255526787,\n \"acc_stderr\": 0.00490641198447679,\n \"acc_norm\": 0.7913762198765186,\n \"acc_norm_stderr\": 0.004054944548370489\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\": 0.4888888888888889,\n \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n \"acc_norm_stderr\": 0.04318275491977976\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\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.46037735849056605,\n \"acc_stderr\": 0.030676096599389184,\n \"acc_norm\": 0.46037735849056605,\n \"acc_norm_stderr\": 0.030676096599389184\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04155319955593146,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04155319955593146\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.33,\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\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.03733626655383509,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.03733626655383509\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307809,\n \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307809\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.41702127659574467,\n \"acc_stderr\": 0.032232762667117124,\n \"acc_norm\": 0.41702127659574467,\n \"acc_norm_stderr\": 0.032232762667117124\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n \"acc_stderr\": 0.043391383225798615,\n \"acc_norm\": 0.30701754385964913,\n \"acc_norm_stderr\": 0.043391383225798615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113946,\n \"acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113946\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.04306241259127155,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.04306241259127155\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5096774193548387,\n \"acc_stderr\": 0.02843867799890955,\n \"acc_norm\": 0.5096774193548387,\n \"acc_norm_stderr\": 0.02843867799890955\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n \"acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6121212121212121,\n \"acc_stderr\": 0.038049136539710114,\n \"acc_norm\": 0.6121212121212121,\n \"acc_norm_stderr\": 0.038049136539710114\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.48484848484848486,\n \"acc_stderr\": 0.0356071651653106,\n \"acc_norm\": 0.48484848484848486,\n \"acc_norm_stderr\": 0.0356071651653106\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6683937823834197,\n \"acc_stderr\": 0.03397636541089118,\n \"acc_norm\": 0.6683937823834197,\n \"acc_norm_stderr\": 0.03397636541089118\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.45384615384615384,\n \"acc_stderr\": 0.025242770987126177,\n \"acc_norm\": 0.45384615384615384,\n \"acc_norm_stderr\": 0.025242770987126177\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945284,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945284\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.42016806722689076,\n \"acc_stderr\": 0.03206183783236152,\n \"acc_norm\": 0.42016806722689076,\n \"acc_norm_stderr\": 0.03206183783236152\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.03734535676787198,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.03734535676787198\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6256880733944954,\n \"acc_stderr\": 0.020748959408988306,\n \"acc_norm\": 0.6256880733944954,\n \"acc_norm_stderr\": 0.020748959408988306\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.030851992993257013,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.030851992993257013\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5245098039215687,\n \"acc_stderr\": 0.03505093194348798,\n \"acc_norm\": 0.5245098039215687,\n \"acc_norm_stderr\": 0.03505093194348798\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5949367088607594,\n \"acc_stderr\": 0.03195514741370671,\n \"acc_norm\": 0.5949367088607594,\n \"acc_norm_stderr\": 0.03195514741370671\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5515695067264574,\n \"acc_stderr\": 0.03337883736255098,\n \"acc_norm\": 0.5515695067264574,\n \"acc_norm_stderr\": 0.03337883736255098\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6115702479338843,\n \"acc_stderr\": 0.04449270350068383,\n \"acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.04449270350068383\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.04832853553437056,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.04832853553437056\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.48466257668711654,\n \"acc_stderr\": 0.039265223787088424,\n \"acc_norm\": 0.48466257668711654,\n \"acc_norm_stderr\": 0.039265223787088424\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n \"acc_stderr\": 0.046355501356099754,\n \"acc_norm\": 0.39285714285714285,\n \"acc_norm_stderr\": 0.046355501356099754\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5825242718446602,\n \"acc_stderr\": 0.048828405482122375,\n \"acc_norm\": 0.5825242718446602,\n \"acc_norm_stderr\": 0.048828405482122375\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6923076923076923,\n \"acc_stderr\": 0.03023638994217308,\n \"acc_norm\": 0.6923076923076923,\n \"acc_norm_stderr\": 0.03023638994217308\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6424010217113666,\n \"acc_stderr\": 0.01713948899880328,\n \"acc_norm\": 0.6424010217113666,\n \"acc_norm_stderr\": 0.01713948899880328\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5028901734104047,\n \"acc_stderr\": 0.026918645383239004,\n \"acc_norm\": 0.5028901734104047,\n \"acc_norm_stderr\": 0.026918645383239004\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\": 0.4934640522875817,\n \"acc_stderr\": 0.028627470550556047,\n \"acc_norm\": 0.4934640522875817,\n \"acc_norm_stderr\": 0.028627470550556047\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.594855305466238,\n \"acc_stderr\": 0.027882383791325953,\n \"acc_norm\": 0.594855305466238,\n \"acc_norm_stderr\": 0.027882383791325953\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.027815973433878014,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.027815973433878014\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3617021276595745,\n \"acc_stderr\": 0.028663820147199492,\n \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.028663820147199492\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3559322033898305,\n \"acc_stderr\": 0.012228645537277566,\n \"acc_norm\": 0.3559322033898305,\n \"acc_norm_stderr\": 0.012228645537277566\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.030320243265004144,\n \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.030320243265004144\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.02008736207670286,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.02008736207670286\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5265306122448979,\n \"acc_stderr\": 0.03196412734523272,\n \"acc_norm\": 0.5265306122448979,\n \"acc_norm_stderr\": 0.03196412734523272\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6417910447761194,\n \"acc_stderr\": 0.03390393042268813,\n \"acc_norm\": 0.6417910447761194,\n \"acc_norm_stderr\": 0.03390393042268813\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n 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|
2023-08-29T20:14:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16 on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T21:13:05.130565(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16",
"## 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 dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T21:13:05.130565(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16",
"## 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 dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T21:13:05.130565(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16## 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 dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T21:13:05.130565(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
76c14b0c4e7ff828bca159d05d0bdb73ffcb1510
|
# Dataset Card for Evaluation run of nathan0/mpt_delta_tuned_model_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nathan0/mpt_delta_tuned_model_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 [nathan0/mpt_delta_tuned_model_v2](https://huggingface.co/nathan0/mpt_delta_tuned_model_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_nathan0__mpt_delta_tuned_model_v2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T16:16:19.015155](https://huggingface.co/datasets/open-llm-leaderboard/details_nathan0__mpt_delta_tuned_model_v2/blob/main/results_2023-08-29T16%3A16%3A19.015155.json):
```python
{
"all": {
"acc": 0.2950832179561353,
"acc_stderr": 0.0329561063051657,
"acc_norm": 0.29907052345099405,
"acc_norm_stderr": 0.03294521260985216,
"mc1": 0.23623011015911874,
"mc1_stderr": 0.014869755015871108,
"mc2": 0.35471976554662815,
"mc2_stderr": 0.013741277408130734
},
"harness|arc:challenge|25": {
"acc": 0.45819112627986347,
"acc_stderr": 0.014560220308714697,
"acc_norm": 0.5068259385665529,
"acc_norm_stderr": 0.014610029151379813
},
"harness|hellaswag|10": {
"acc": 0.5774746066520613,
"acc_stderr": 0.004929517011508221,
"acc_norm": 0.7640908185620394,
"acc_norm_stderr": 0.0042369801453443065
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2740740740740741,
"acc_stderr": 0.03853254836552003,
"acc_norm": 0.2740740740740741,
"acc_norm_stderr": 0.03853254836552003
},
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"acc_stderr": 0.0355418036802569,
"acc_norm": 0.2565789473684211,
"acc_norm_stderr": 0.0355418036802569
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3169811320754717,
"acc_stderr": 0.028637235639800918,
"acc_norm": 0.3169811320754717,
"acc_norm_stderr": 0.028637235639800918
},
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"acc": 0.3055555555555556,
"acc_stderr": 0.03852084696008534,
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"acc_norm_stderr": 0.03852084696008534
},
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"acc_stderr": 0.04229525846816505,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm_stderr": 0.048241815132442176
},
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"acc_norm_stderr": 0.04648231987117316
},
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"acc_stderr": 0.03156809362703175,
"acc_norm": 0.21965317919075145,
"acc_norm_stderr": 0.03156809362703175
},
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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-virology|5": {
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"acc_norm_stderr": 0.0371172519074075
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.26900584795321636,
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"acc_norm": 0.26900584795321636,
"acc_norm_stderr": 0.03401052620104089
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23623011015911874,
"mc1_stderr": 0.014869755015871108,
"mc2": 0.35471976554662815,
"mc2_stderr": 0.013741277408130734
}
}
```
### 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_nathan0__mpt_delta_tuned_model_v2
|
[
"region:us"
] |
2023-08-29T15:16:36+00:00
|
{"pretty_name": "Evaluation run of nathan0/mpt_delta_tuned_model_v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [nathan0/mpt_delta_tuned_model_v2](https://huggingface.co/nathan0/mpt_delta_tuned_model_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_nathan0__mpt_delta_tuned_model_v2\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T16:16:19.015155](https://huggingface.co/datasets/open-llm-leaderboard/details_nathan0__mpt_delta_tuned_model_v2/blob/main/results_2023-08-29T16%3A16%3A19.015155.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2950832179561353,\n 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"harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_29T16_16_19.015155", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T16:16:19.015155.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T16:16:19.015155.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T16_16_19.015155", "path": ["results_2023-08-29T16:16:19.015155.parquet"]}, {"split": "latest", "path": ["results_2023-08-29T16:16:19.015155.parquet"]}]}]}
|
2023-08-29T15:17:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of nathan0/mpt_delta_tuned_model_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 nathan0/mpt_delta_tuned_model_v2 on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T16:16:19.015155:
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 Card for Evaluation run of nathan0/mpt_delta_tuned_model_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 nathan0/mpt_delta_tuned_model_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T16:16:19.015155:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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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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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model nathan0/mpt_delta_tuned_model_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T16:16:19.015155:",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of nathan0/mpt_delta_tuned_model_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 nathan0/mpt_delta_tuned_model_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T16:16:19.015155:### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
c5efb2d1c9172e5f8259ef7e5a5533b5705aefe7
|
# Dataset Card for "galleon-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tog/galleon-llama2-1k
|
[
"region:us"
] |
2023-08-29T15:18:45+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 159012.9, "num_examples": 900}, {"name": "test", "num_bytes": 17668.1, "num_examples": 100}], "download_size": 89959, "dataset_size": 176681.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-08-29T15:57:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "galleon-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"galleon-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"galleon-llama2-1k\"\n\nMore Information needed"
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6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"galleon-llama2-1k\"\n\nMore Information needed"
] |
b733ef7ce7945f0cf0a28c76b05a690fd6b76c30
|
# Dataset Card for "galleon-llama2-27k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tog/galleon-llama2-27k
|
[
"region:us"
] |
2023-08-29T15:21:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4280355.9, "num_examples": 24300}, {"name": "test", "num_bytes": 475595.1, "num_examples": 2700}], "download_size": 2318132, "dataset_size": 4755951.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-08-30T07:33:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "galleon-llama2-27k"
More Information needed
|
[
"# Dataset Card for \"galleon-llama2-27k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"galleon-llama2-27k\"\n\nMore Information needed"
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[
6,
19
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"galleon-llama2-27k\"\n\nMore Information needed"
] |
334b3d0a552bd6dfbd69a2dc730ae04f9e1caf22
|
# 📔 **DATASET**
| **Dataset** | Class | Number of Questions |
| ------- | ----------------------------------------------------------------- | ------------------------ |
| **FLAN_CoT(zs)** | Reasoning 、 MATH 、 ScienceQA 、 Commonsense | 91910 |
| **Prm800k** | Reasoning 、 MATH | 6713 |
| **ScienceQA** | ScienceQA | 5177 |
| **SciBench** | ScienceQA | 695 |
| **ReClor** | Reasoning | 1624 |
| **TheoremQA** | Commonsense 、 MATH 、 ScienceQA | 800 |
| **OpenBookQA** | Text_Understanding 、 Reasoning 、 Commonsense 、 ScienceQA | 5957 |
| **ARB** | Reasoning 、 MATH 、 ScienceQA 、 Commonsense 、 Text_Understanding | 605 |
| **Openassistant-guanaco** | Commonsense 、 Text_Understanding 、 Reasoning | 802 |
| **SQuAD 2.0** | Text_Understanding | 87599 |
| **CommonsenseQA** | Commonsense | 9741 |
| **Ethics** | Commonsense | 21759 |
# 📌 **Methon**
## *Dataset Format Definition*
Use "instruction、input、output" tend to lean towards guided datasets. In this format, each sample includes an instruction, an input, and an expected output. The instruction provides guidance on how to process the input to generate the output. This format of dataset is often used to train models to perform specific tasks, as they explicitly indicate the operations the model should perform.
```
{
"input": "",
"output": "",
"instruction": ""
}
```
- ### [FLAN_V2 COT(ZS)](https://huggingface.co/datasets/conceptofmind/cot_submix_original/tree/main)
We only extract the 'zs_opt' from COT and categorize each task.
- ### [CommonsenseQA](https://huggingface.co/datasets/commonsense_qa)
We extracted the question and choices from the original CommonsenseQA dataset and placed them in the instruction. We also wrote the input prompt: "Choose A, B, C, D, or E as your solution."
- ### [SQuAD](https://huggingface.co/datasets/squad)
We used the questions from the SQUAD dataset as instructions and treated the context as the input.
- ### [Ethics](https://huggingface.co/datasets/hendrycks/ethics)
The ethics dataset, which was originally in labeled format, has been transformed into a true or false format. Additionally, the input now includes the instruction "Give true or false according to ethics."
- ### [OTHER](https://github.com/arielnlee/Platypus/tree/main/data_pipeline)
Prm800k, ScienceQA, SciBench, ReClor, TheoremQA, OpenBookQA, ARB, and OpenAssistant-Guanaco datasets adopt the same format as Platypus.
## *Sampling Algorithms*
1. First,we are taking all datasets from COT, ARB, TheoremQA and Ethics. ARB and TheoremQA encompass a wide range of fields and have a relatively low total count. Since COT has high quality, we are including the entire dataset. For the Ethics dataset, we are collecting the entire dataset because we want the model to comprehensively learn more about ethics and security aspects.
2. The remaining datasets were initially categorized into the following four groups for the purpose of **Simple Random Sampling**:
- *Science Questions and Answers* : ScienceQA、SciBench
- *Reasoning & Mathematics* : ReClor、Prm800k
- *Text Comprehension* : OpenBookQA、SQuAD
- *Commonsense* : CommonsenseQA、Openassistant-guanaco
However, we discovered that the total number of datasets in the Science Questions and Answers、Reasoning & Mathematics、and Commonsense categories did not exceed 30,000. As a result, only the Text Comprehension category underwent Simple Random Sampling, while the others were taken in their entirety.
# 🏁 **Feature Work**
- In the future, we intend to utilize Stratified Sampling due to the imbalance in the number of questions across different datasets, which introduces bias. Conversely, if we opt to randomly sample an equal number of examples from each dataset, it can yield a smaller estimation error for the same total sample size.
- We can even evaluate based on the fine-tuning from the first stage and employ additional scripting techniques to enhance the quality of the dataset.
|
huangyt/FINETUNE1
|
[
"license:openrail",
"region:us"
] |
2023-08-29T15:24:16+00:00
|
{"license": "openrail", "pretty_name": "Finetune1"}
|
2023-09-01T05:31:33+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
!Change can be sunshine if you let it in..png
DATASET
=======
Dataset: FLAN\_CoT(zs), Class: Reasoning 、 MATH 、 ScienceQA 、 Commonsense, Number of Questions: 91910
Dataset: Prm800k, Class: Reasoning 、 MATH, Number of Questions: 6713
Dataset: ScienceQA, Class: ScienceQA, Number of Questions: 5177
Dataset: SciBench, Class: ScienceQA, Number of Questions: 695
Dataset: ReClor, Class: Reasoning, Number of Questions: 1624
Dataset: TheoremQA, Class: Commonsense 、 MATH 、 ScienceQA, Number of Questions: 800
Dataset: OpenBookQA, Class: Text\_Understanding 、 Reasoning 、 Commonsense 、 ScienceQA, Number of Questions: 5957
Dataset: ARB, Class: Reasoning 、 MATH 、 ScienceQA 、 Commonsense 、 Text\_Understanding, Number of Questions: 605
Dataset: Openassistant-guanaco, Class: Commonsense 、 Text\_Understanding 、 Reasoning, Number of Questions: 802
Dataset: SQuAD 2.0, Class: Text\_Understanding, Number of Questions: 87599
Dataset: CommonsenseQA, Class: Commonsense, Number of Questions: 9741
Dataset: Ethics, Class: Commonsense, Number of Questions: 21759
Methon
======
*Dataset Format Definition*
---------------------------
Use "instruction、input、output" tend to lean towards guided datasets. In this format, each sample includes an instruction, an input, and an expected output. The instruction provides guidance on how to process the input to generate the output. This format of dataset is often used to train models to perform specific tasks, as they explicitly indicate the operations the model should perform.
* ### FLAN\_V2 COT(ZS)
We only extract the 'zs\_opt' from COT and categorize each task.
* ### CommonsenseQA
We extracted the question and choices from the original CommonsenseQA dataset and placed them in the instruction. We also wrote the input prompt: "Choose A, B, C, D, or E as your solution."
* ### SQuAD
We used the questions from the SQUAD dataset as instructions and treated the context as the input.
* ### Ethics
The ethics dataset, which was originally in labeled format, has been transformed into a true or false format. Additionally, the input now includes the instruction "Give true or false according to ethics."
* ### OTHER
Prm800k, ScienceQA, SciBench, ReClor, TheoremQA, OpenBookQA, ARB, and OpenAssistant-Guanaco datasets adopt the same format as Platypus.
*Sampling Algorithms*
---------------------
1. First,we are taking all datasets from COT, ARB, TheoremQA and Ethics. ARB and TheoremQA encompass a wide range of fields and have a relatively low total count. Since COT has high quality, we are including the entire dataset. For the Ethics dataset, we are collecting the entire dataset because we want the model to comprehensively learn more about ethics and security aspects.
2. The remaining datasets were initially categorized into the following four groups for the purpose of Simple Random Sampling:
* *Science Questions and Answers* : ScienceQA、SciBench
* *Reasoning & Mathematics* : ReClor、Prm800k
* *Text Comprehension* : OpenBookQA、SQuAD
* *Commonsense* : CommonsenseQA、Openassistant-guanacoHowever, we discovered that the total number of datasets in the Science Questions and Answers、Reasoning & Mathematics、and Commonsense categories did not exceed 30,000. As a result, only the Text Comprehension category underwent Simple Random Sampling, while the others were taken in their entirety.
Feature Work
============
* In the future, we intend to utilize Stratified Sampling due to the imbalance in the number of questions across different datasets, which introduces bias. Conversely, if we opt to randomly sample an equal number of examples from each dataset, it can yield a smaller estimation error for the same total sample size.
* We can even evaluate based on the fine-tuning from the first stage and employ additional scripting techniques to enhance the quality of the dataset.
|
[
"### FLAN\\_V2 COT(ZS)\n\n\nWe only extract the 'zs\\_opt' from COT and categorize each task.\n* ### CommonsenseQA\n\n\nWe extracted the question and choices from the original CommonsenseQA dataset and placed them in the instruction. We also wrote the input prompt: \"Choose A, B, C, D, or E as your solution.\"\n* ### SQuAD\n\n\nWe used the questions from the SQUAD dataset as instructions and treated the context as the input.\n* ### Ethics\n\n\nThe ethics dataset, which was originally in labeled format, has been transformed into a true or false format. Additionally, the input now includes the instruction \"Give true or false according to ethics.\"\n* ### OTHER\n\n\nPrm800k, ScienceQA, SciBench, ReClor, TheoremQA, OpenBookQA, ARB, and OpenAssistant-Guanaco datasets adopt the same format as Platypus.\n\n\n*Sampling Algorithms*\n---------------------\n\n\n1. First,we are taking all datasets from COT, ARB, TheoremQA and Ethics. ARB and TheoremQA encompass a wide range of fields and have a relatively low total count. Since COT has high quality, we are including the entire dataset. For the Ethics dataset, we are collecting the entire dataset because we want the model to comprehensively learn more about ethics and security aspects.\n2. The remaining datasets were initially categorized into the following four groups for the purpose of Simple Random Sampling:\n\n\n\t* *Science Questions and Answers* : ScienceQA、SciBench\n\t* *Reasoning & Mathematics* : ReClor、Prm800k\n\t* *Text Comprehension* : OpenBookQA、SQuAD\n\t* *Commonsense* : CommonsenseQA、Openassistant-guanacoHowever, we discovered that the total number of datasets in the Science Questions and Answers、Reasoning & Mathematics、and Commonsense categories did not exceed 30,000. As a result, only the Text Comprehension category underwent Simple Random Sampling, while the others were taken in their entirety.\n\n\nFeature Work\n============\n\n\n* In the future, we intend to utilize Stratified Sampling due to the imbalance in the number of questions across different datasets, which introduces bias. Conversely, if we opt to randomly sample an equal number of examples from each dataset, it can yield a smaller estimation error for the same total sample size.\n* We can even evaluate based on the fine-tuning from the first stage and employ additional scripting techniques to enhance the quality of the dataset."
] |
[
"TAGS\n#license-openrail #region-us \n",
"### FLAN\\_V2 COT(ZS)\n\n\nWe only extract the 'zs\\_opt' from COT and categorize each task.\n* ### CommonsenseQA\n\n\nWe extracted the question and choices from the original CommonsenseQA dataset and placed them in the instruction. We also wrote the input prompt: \"Choose A, B, C, D, or E as your solution.\"\n* ### SQuAD\n\n\nWe used the questions from the SQUAD dataset as instructions and treated the context as the input.\n* ### Ethics\n\n\nThe ethics dataset, which was originally in labeled format, has been transformed into a true or false format. Additionally, the input now includes the instruction \"Give true or false according to ethics.\"\n* ### OTHER\n\n\nPrm800k, ScienceQA, SciBench, ReClor, TheoremQA, OpenBookQA, ARB, and OpenAssistant-Guanaco datasets adopt the same format as Platypus.\n\n\n*Sampling Algorithms*\n---------------------\n\n\n1. First,we are taking all datasets from COT, ARB, TheoremQA and Ethics. ARB and TheoremQA encompass a wide range of fields and have a relatively low total count. Since COT has high quality, we are including the entire dataset. For the Ethics dataset, we are collecting the entire dataset because we want the model to comprehensively learn more about ethics and security aspects.\n2. The remaining datasets were initially categorized into the following four groups for the purpose of Simple Random Sampling:\n\n\n\t* *Science Questions and Answers* : ScienceQA、SciBench\n\t* *Reasoning & Mathematics* : ReClor、Prm800k\n\t* *Text Comprehension* : OpenBookQA、SQuAD\n\t* *Commonsense* : CommonsenseQA、Openassistant-guanacoHowever, we discovered that the total number of datasets in the Science Questions and Answers、Reasoning & Mathematics、and Commonsense categories did not exceed 30,000. As a result, only the Text Comprehension category underwent Simple Random Sampling, while the others were taken in their entirety.\n\n\nFeature Work\n============\n\n\n* In the future, we intend to utilize Stratified Sampling due to the imbalance in the number of questions across different datasets, which introduces bias. Conversely, if we opt to randomly sample an equal number of examples from each dataset, it can yield a smaller estimation error for the same total sample size.\n* We can even evaluate based on the fine-tuning from the first stage and employ additional scripting techniques to enhance the quality of the dataset."
] |
[
12,
621
] |
[
"passage: TAGS\n#license-openrail #region-us \n"
] |
b3a81354f1b345ebc34892e2b12adbc4e6dc89dd
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_default-of-credit-card-clients_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T15:24:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 308080000, "num_examples": 10000}, {"name": "validation", "num_bytes": 308080000, "num_examples": 10000}], "download_size": 181794530, "dataset_size": 616160000}}
|
2023-08-30T15:40:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
40
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_default-of-credit-card-clients_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
f1cd45ed66f9e7d60438c95a037ba09027a3f9a4
|
# Dataset Card for "eu_test6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KatMarie/eu_test6
|
[
"region:us"
] |
2023-08-29T15:24:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2429668, "num_examples": 41376}], "download_size": 1661037, "dataset_size": 2429668}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T08:58:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eu_test6"
More Information needed
|
[
"# Dataset Card for \"eu_test6\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eu_test6\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eu_test6\"\n\nMore Information needed"
] |
0036e0bdcf752718e2f3afbecf7e257dd1396e51
|
# Dataset Card for "autotree_automl_MagicTelescope_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_MagicTelescope_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T15:29:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 205680000, "num_examples": 10000}, {"name": "validation", "num_bytes": 205680000, "num_examples": 10000}], "download_size": 185283022, "dataset_size": 411360000}}
|
2023-08-30T15:52:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_MagicTelescope_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_MagicTelescope_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_MagicTelescope_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_MagicTelescope_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
75e05bee017aae00d67afa3df3686a94f73706e3
|
# Dataset Card for "llama_2_optimized_product_titles-esci-temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/llama_2_optimized_product_titles-esci-temp
|
[
"region:us"
] |
2023-08-29T15:39:55+00:00
|
{"dataset_info": {"features": [{"name": "level_0", "dtype": "int64"}, {"name": "index", "dtype": "int64"}, {"name": "product_title", "dtype": "string"}, {"name": "average_score", "dtype": "float64"}, {"name": "total_score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "preds", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1526227, "num_examples": 480}], "download_size": 300628, "dataset_size": 1526227}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T05:12:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama_2_optimized_product_titles-esci-temp"
More Information needed
|
[
"# Dataset Card for \"llama_2_optimized_product_titles-esci-temp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama_2_optimized_product_titles-esci-temp\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama_2_optimized_product_titles-esci-temp\"\n\nMore Information needed"
] |
2a6758c92b5bc8a38d676a6c5a250f6b0c9d4e06
|
# Dataset of Moa Torii
This is the dataset of Moa Torii, containing 18 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 18 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 41 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 18 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 18 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 18 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 18 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 18 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 41 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 41 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 41 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/moa_torii_imocho
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T15:52:38+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:15+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Moa Torii
====================
This is the dataset of Moa Torii, containing 18 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
4ed258da859d8ae765cbab9f3f89adc1205930f0
|
# Dataset Card for "mushi-snli-llama2-3k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Musha-the-Yusha/mushi-snli-llama2-3k
|
[
"region:us"
] |
2023-08-29T15:57:12+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2020257, "num_examples": 10000}], "download_size": 723568, "dataset_size": 2020257}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T16:36:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mushi-snli-llama2-3k"
More Information needed
|
[
"# Dataset Card for \"mushi-snli-llama2-3k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mushi-snli-llama2-3k\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mushi-snli-llama2-3k\"\n\nMore Information needed"
] |
a07b38086b86ea3a405f93549ca1361e37c21079
|
Dump of 2023-08-20 of all french article in wikipedia
https://dumps.wikimedia.org/frwiki/20230820/frwiki-20230820-pages-articles.xml.bz2
|
Kant1/French_Wikipedia_articles
|
[
"task_categories:text-generation",
"language:fr",
"region:us"
] |
2023-08-29T15:59:23+00:00
|
{"language": ["fr"], "task_categories": ["text-generation"]}
|
2023-08-29T16:09:13+00:00
|
[] |
[
"fr"
] |
TAGS
#task_categories-text-generation #language-French #region-us
|
Dump of 2023-08-20 of all french article in wikipedia
URL
|
[] |
[
"TAGS\n#task_categories-text-generation #language-French #region-us \n"
] |
[
23
] |
[
"passage: TAGS\n#task_categories-text-generation #language-French #region-us \n"
] |
4cc4add8a2816db0d9593c067fc27357f21f20c0
|
# Dataset Card for "augmented_notes_9000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jakir057/augmented_notes_9000
|
[
"region:us"
] |
2023-08-29T16:03:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "10", "1": "100", "2": "1000", "3": "2", "4": "20", "5": "200", "6": "5", "7": "50", "8": "500"}}}}], "splits": [{"name": "train", "num_bytes": 62693081.95, "num_examples": 7650}, {"name": "test", "num_bytes": 11383838.7, "num_examples": 1350}], "download_size": 74957422, "dataset_size": 74076920.65}}
|
2023-08-29T16:03:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "augmented_notes_9000"
More Information needed
|
[
"# Dataset Card for \"augmented_notes_9000\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"augmented_notes_9000\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"augmented_notes_9000\"\n\nMore Information needed"
] |
1f5149e078d69aec15098811565812130ec6b6d6
|
# Dataset of Ayaka Tachibana
This is the dataset of Ayaka Tachibana, containing 54 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 54 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 115 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 54 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 54 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 54 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 54 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 54 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 115 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 115 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 115 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/ayaka_tachibana_imocho
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T16:05:11+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:17+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Ayaka Tachibana
==========================
This is the dataset of Ayaka Tachibana, containing 54 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
9eac7ee55d3e775ec82234d02ee5428ba3860583
|
# Dataset Card for "code_instructions_filtered_7k"
Filtered version of `sahil2801/code_instructions_120k` based on manual, quality, and learning value filters.
|
vikp/code_instructions_filtered_7k
|
[
"region:us"
] |
2023-08-29T16:11:40+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "quality_prob", "dtype": "float64"}, {"name": "learning_prob", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3935708.9048315734, "num_examples": 7526}], "download_size": 2442024, "dataset_size": 3935708.9048315734}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T16:15:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "code_instructions_filtered_7k"
Filtered version of 'sahil2801/code_instructions_120k' based on manual, quality, and learning value filters.
|
[
"# Dataset Card for \"code_instructions_filtered_7k\"\n\nFiltered version of 'sahil2801/code_instructions_120k' based on manual, quality, and learning value filters."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"code_instructions_filtered_7k\"\n\nFiltered version of 'sahil2801/code_instructions_120k' based on manual, quality, and learning value filters."
] |
[
6,
46
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"code_instructions_filtered_7k\"\n\nFiltered version of 'sahil2801/code_instructions_120k' based on manual, quality, and learning value filters."
] |
0d6b81f9d3c33c7b15452e3c275795f4d53f076c
|
# Dataset of Neko
This is the dataset of Neko, containing 28 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 28 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 68 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 28 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 28 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 28 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 28 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 28 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 68 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 68 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 68 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/neko_imocho
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T16:12:41+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:19+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Neko
===============
This is the dataset of Neko, containing 28 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
00743408d6a974779586e82379f16e609af68f68
|
# Bangumi Image Base of Seitokai Yakuindomo
This is the image base of bangumi Seitokai Yakuindomo, we detected 32 characters, 7180 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 114 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 1717 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 233 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 48 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 129 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 49 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 52 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 347 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 1238 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 230 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 49 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 88 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 217 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 23 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 935 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 38 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 30 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 20 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 243 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 13 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 708 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 65 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 10 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 14 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 69 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 43 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 23 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 35 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 23 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 145 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 39 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 193 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/seitokaiyakuindomo
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-08-29T16:22:59+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-30T18:12:11+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Seitokai Yakuindomo
=========================================
This is the image base of bangumi Seitokai Yakuindomo, we detected 32 characters, 7180 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
bc52144eee78d18d4809df84404c06fa4c45a8f9
|
# Dataset Card for Evaluation run of TheBloke/WizardLM-13B-V1.1-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ
- **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 [TheBloke/WizardLM-13B-V1.1-GPTQ](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_TheBloke__WizardLM-13B-V1.1-GPTQ_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-07T10:04:12.671111](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-13B-V1.1-GPTQ_public/blob/main/results_2023-11-07T10-04-12.671111.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": {
"em": 0.16851929530201343,
"em_stderr": 0.0038334566477606904,
"f1": 0.22963611577181164,
"f1_stderr": 0.0038748826707742656,
"acc": 0.41269994189709863,
"acc_stderr": 0.009890824821681563
},
"harness|drop|3": {
"em": 0.16851929530201343,
"em_stderr": 0.0038334566477606904,
"f1": 0.22963611577181164,
"f1_stderr": 0.0038748826707742656
},
"harness|gsm8k|5": {
"acc": 0.08112206216830932,
"acc_stderr": 0.007520395797922653
},
"harness|winogrande|5": {
"acc": 0.744277821625888,
"acc_stderr": 0.012261253845440473
}
}
```
### 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_TheBloke__WizardLM-13B-V1.1-GPTQ
|
[
"region:us"
] |
2023-08-29T16:24:32+00:00
|
{"pretty_name": "Evaluation run of TheBloke/WizardLM-13B-V1.1-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/WizardLM-13B-V1.1-GPTQ](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 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_TheBloke__WizardLM-13B-V1.1-GPTQ_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-07T10:04:12.671111](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-13B-V1.1-GPTQ_public/blob/main/results_2023-11-07T10-04-12.671111.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 \"em\": 0.16851929530201343,\n \"em_stderr\": 0.0038334566477606904,\n \"f1\": 0.22963611577181164,\n \"f1_stderr\": 0.0038748826707742656,\n \"acc\": 0.41269994189709863,\n \"acc_stderr\": 0.009890824821681563\n },\n \"harness|drop|3\": {\n \"em\": 0.16851929530201343,\n \"em_stderr\": 0.0038334566477606904,\n \"f1\": 0.22963611577181164,\n \"f1_stderr\": 0.0038748826707742656\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08112206216830932,\n \"acc_stderr\": 0.007520395797922653\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440473\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T12_40_32.771713", "path": ["**/details_harness|drop|3_2023-11-05T12-40-32.771713.parquet"]}, {"split": "2023_11_07T10_04_12.671111", "path": ["**/details_harness|drop|3_2023-11-07T10-04-12.671111.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-07T10-04-12.671111.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T12_40_32.771713", "path": ["**/details_harness|gsm8k|5_2023-11-05T12-40-32.771713.parquet"]}, {"split": "2023_11_07T10_04_12.671111", "path": ["**/details_harness|gsm8k|5_2023-11-07T10-04-12.671111.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-07T10-04-12.671111.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_05T12_40_32.771713", "path": ["**/details_harness|winogrande|5_2023-11-05T12-40-32.771713.parquet"]}, {"split": "2023_11_07T10_04_12.671111", "path": ["**/details_harness|winogrande|5_2023-11-07T10-04-12.671111.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-07T10-04-12.671111.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_05T12_40_32.771713", "path": ["results_2023-11-05T12-40-32.771713.parquet"]}, {"split": "2023_11_07T10_04_12.671111", "path": ["results_2023-11-07T10-04-12.671111.parquet"]}, {"split": "latest", "path": ["results_2023-11-07T10-04-12.671111.parquet"]}]}]}
|
2023-12-01T14:35:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/WizardLM-13B-V1.1-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/WizardLM-13B-V1.1-GPTQ on the Open LLM Leaderboard.
The dataset is composed of 3 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 2023-11-07T10:04:12.671111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 TheBloke/WizardLM-13B-V1.1-GPTQ",
"## 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 TheBloke/WizardLM-13B-V1.1-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 2023-11-07T10:04:12.671111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 TheBloke/WizardLM-13B-V1.1-GPTQ",
"## 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 TheBloke/WizardLM-13B-V1.1-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 2023-11-07T10:04:12.671111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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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26,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TheBloke/WizardLM-13B-V1.1-GPTQ## 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 TheBloke/WizardLM-13B-V1.1-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 2023-11-07T10:04:12.671111(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
5760e77f5127c785d1526fc260eefdea493c0226
|
# Dataset Card for "above_70yo_elderly_people_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
aviroes/above_70yo_elderly_people_dataset
|
[
"region:us"
] |
2023-08-29T16:26:40+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 204191101.09341103, "num_examples": 4315}, {"name": "test", "num_bytes": 8646317.409757026, "num_examples": 166}], "download_size": 193297105, "dataset_size": 212837418.50316805}}
|
2023-08-29T16:27:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "above_70yo_elderly_people_dataset"
More Information needed
|
[
"# Dataset Card for \"above_70yo_elderly_people_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"above_70yo_elderly_people_dataset\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"above_70yo_elderly_people_dataset\"\n\nMore Information needed"
] |
142c8453f3cef8612fa0ecfab5c7bf8a4e2ea945
|
# Dataset Card for "directv-zocalos_1.0fps_21-08-2023_24-08-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Seenka/directv-zocalos_1.0fps_21-08-2023_24-08-2023
|
[
"region:us"
] |
2023-08-29T16:35:29+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_filename", "dtype": "string"}, {"name": "frame_time", "dtype": "time64[us]"}, {"name": "video_storage_path", "dtype": "string"}, {"name": "zocalo_id", "dtype": "string"}, {"name": "frame_number", "dtype": "int64"}, {"name": "is_L_shape", "dtype": "bool"}, {"name": "horizontal_check", "dtype": "bool"}, {"name": "vertical_check", "dtype": "bool"}, {"name": "black_image", "dtype": "bool"}, {"name": "horizontal_xmin", "dtype": "int64"}, {"name": "horizontal_xmax", "dtype": "int64"}, {"name": "horizontal_ymin", "dtype": "int64"}, {"name": "horizontal_ymax", "dtype": "int64"}, {"name": "vertical_xmin", "dtype": "int64"}, {"name": "vertical_xmax", "dtype": "int64"}, {"name": "vertical_ymin", "dtype": "int64"}, {"name": "vertical_ymax", "dtype": "int64"}, {"name": "cropped_image_horizontal", "dtype": "image"}, {"name": "cropped_image_vertical", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "embedding_horizontal", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 8554665.0, "num_examples": 10}], "download_size": 4263397, "dataset_size": 8554665.0}}
|
2023-08-29T17:07:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "directv-zocalos_1.0fps_21-08-2023_24-08-2023"
More Information needed
|
[
"# Dataset Card for \"directv-zocalos_1.0fps_21-08-2023_24-08-2023\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"directv-zocalos_1.0fps_21-08-2023_24-08-2023\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"directv-zocalos_1.0fps_21-08-2023_24-08-2023\"\n\nMore Information needed"
] |
8d78216dbefe0ff8907565315efcb9dc59a07a66
|
# Dataset Card for Evaluation run of TheBloke/vicuna-13b-v1.3.0-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GPTQ
- **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 [TheBloke/vicuna-13b-v1.3.0-GPTQ](https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_TheBloke__vicuna-13b-v1.3.0-GPTQ_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-07T20:06:54.484278](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__vicuna-13b-v1.3.0-GPTQ_public/blob/main/results_2023-11-07T20-06-54.484278.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": {
"em": 0.00964765100671141,
"em_stderr": 0.0010010258941568287,
"f1": 0.0725954278523494,
"f1_stderr": 0.0016816004855467774,
"acc": 0.4154001410984979,
"acc_stderr": 0.00993538924041841
},
"harness|drop|3": {
"em": 0.00964765100671141,
"em_stderr": 0.0010010258941568287,
"f1": 0.0725954278523494,
"f1_stderr": 0.0016816004855467774
},
"harness|gsm8k|5": {
"acc": 0.0841546626231994,
"acc_stderr": 0.0076470240466032045
},
"harness|winogrande|5": {
"acc": 0.7466456195737964,
"acc_stderr": 0.012223754434233618
}
}
```
### 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_TheBloke__vicuna-13b-v1.3.0-GPTQ
|
[
"region:us"
] |
2023-08-29T16:37:06+00:00
|
{"pretty_name": "Evaluation run of TheBloke/vicuna-13b-v1.3.0-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/vicuna-13b-v1.3.0-GPTQ](https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 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_TheBloke__vicuna-13b-v1.3.0-GPTQ_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-07T20:06:54.484278](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__vicuna-13b-v1.3.0-GPTQ_public/blob/main/results_2023-11-07T20-06-54.484278.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 \"em\": 0.00964765100671141,\n \"em_stderr\": 0.0010010258941568287,\n \"f1\": 0.0725954278523494,\n \"f1_stderr\": 0.0016816004855467774,\n \"acc\": 0.4154001410984979,\n \"acc_stderr\": 0.00993538924041841\n },\n \"harness|drop|3\": {\n \"em\": 0.00964765100671141,\n \"em_stderr\": 0.0010010258941568287,\n \"f1\": 0.0725954278523494,\n \"f1_stderr\": 0.0016816004855467774\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0841546626231994,\n \"acc_stderr\": 0.0076470240466032045\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233618\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GPTQ", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T09_23_24.198168", "path": ["**/details_harness|drop|3_2023-11-05T09-23-24.198168.parquet"]}, {"split": "2023_11_07T20_06_54.484278", "path": ["**/details_harness|drop|3_2023-11-07T20-06-54.484278.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-07T20-06-54.484278.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T09_23_24.198168", "path": ["**/details_harness|gsm8k|5_2023-11-05T09-23-24.198168.parquet"]}, {"split": "2023_11_07T20_06_54.484278", "path": ["**/details_harness|gsm8k|5_2023-11-07T20-06-54.484278.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-07T20-06-54.484278.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_05T09_23_24.198168", "path": ["**/details_harness|winogrande|5_2023-11-05T09-23-24.198168.parquet"]}, {"split": "2023_11_07T20_06_54.484278", "path": ["**/details_harness|winogrande|5_2023-11-07T20-06-54.484278.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-07T20-06-54.484278.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_05T09_23_24.198168", "path": ["results_2023-11-05T09-23-24.198168.parquet"]}, {"split": "2023_11_07T20_06_54.484278", "path": ["results_2023-11-07T20-06-54.484278.parquet"]}, {"split": "latest", "path": ["results_2023-11-07T20-06-54.484278.parquet"]}]}]}
|
2023-12-01T14:28:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/vicuna-13b-v1.3.0-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/vicuna-13b-v1.3.0-GPTQ on the Open LLM Leaderboard.
The dataset is composed of 3 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 2023-11-07T20:06:54.484278(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 TheBloke/vicuna-13b-v1.3.0-GPTQ",
"## 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 TheBloke/vicuna-13b-v1.3.0-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 2023-11-07T20:06:54.484278(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 TheBloke/vicuna-13b-v1.3.0-GPTQ",
"## 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 TheBloke/vicuna-13b-v1.3.0-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 2023-11-07T20:06:54.484278(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
26,
31,
175,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TheBloke/vicuna-13b-v1.3.0-GPTQ## 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 TheBloke/vicuna-13b-v1.3.0-GPTQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 2023-11-07T20:06:54.484278(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
3bfd1971e8d1d0efec72cbd9bee06a07044253b4
|
# Dataset Card for "coding_exercises_filtered"
Coding exercises generated by gpt, then filtered. This has a lot of duplicates - would not recommend using as is.
|
vikp/coding_exercises_filtered
|
[
"region:us"
] |
2023-08-29T16:37:44+00:00
|
{"dataset_info": {"features": [{"name": "exercise", "dtype": "string"}, {"name": "quality_prob", "dtype": "float64"}, {"name": "learning_prob", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 64853781.21095456, "num_examples": 37581}], "download_size": 19925878, "dataset_size": 64853781.21095456}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-01T20:44:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "coding_exercises_filtered"
Coding exercises generated by gpt, then filtered. This has a lot of duplicates - would not recommend using as is.
|
[
"# Dataset Card for \"coding_exercises_filtered\"\n\nCoding exercises generated by gpt, then filtered. This has a lot of duplicates - would not recommend using as is."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"coding_exercises_filtered\"\n\nCoding exercises generated by gpt, then filtered. This has a lot of duplicates - would not recommend using as is."
] |
[
6,
45
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"coding_exercises_filtered\"\n\nCoding exercises generated by gpt, then filtered. This has a lot of duplicates - would not recommend using as is."
] |
f0fa01fa14916450558912a2bf2f7d56242776d2
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gddgdg/guanaco-llama2-1k
|
[
"region:us"
] |
2023-08-29T16:38:49+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T16:38:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
4fba4134181fe92c8e9c62dee12c36faa68c916f
|
# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2
## Dataset Summary
Subset of the original [OpenAssistant Conversations Dataset (OASST)](https://huggingface.co/datasets/OpenAssistant/oasst1).
* Filtered by `lang=es`.
* Formatted according to the Llama-2 pattern: "\<s> [INST] user prompt [/INST] output model \</s>"
* Select the best ranked output (Some instructions had multiple outputs ranked by humans).
* Select only the first level of the tree conversation.
## Dataset Structure
The dataset has 3909 rows of tuples (instructions and outputs).
|
dariolopez/Llama-2-oasst1-es
|
[
"size_categories:1K<n<10K",
"language:es",
"license:apache-2.0",
"region:us"
] |
2023-08-29T16:39:30+00:00
|
{"language": ["es"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4524060, "num_examples": 3909}], "download_size": 2528456, "dataset_size": 4524060}}
|
2023-08-29T16:45:43+00:00
|
[] |
[
"es"
] |
TAGS
#size_categories-1K<n<10K #language-Spanish #license-apache-2.0 #region-us
|
# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2
## Dataset Summary
Subset of the original OpenAssistant Conversations Dataset (OASST).
* Filtered by 'lang=es'.
* Formatted according to the Llama-2 pattern: "\<s> [INST] user prompt [/INST] output model \</s>"
* Select the best ranked output (Some instructions had multiple outputs ranked by humans).
* Select only the first level of the tree conversation.
## Dataset Structure
The dataset has 3909 rows of tuples (instructions and outputs).
|
[
"# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2",
"## Dataset Summary\n\nSubset of the original OpenAssistant Conversations Dataset (OASST).\n\n* Filtered by 'lang=es'.\n* Formatted according to the Llama-2 pattern: \"\\<s> [INST] user prompt [/INST] output model \\</s>\"\n* Select the best ranked output (Some instructions had multiple outputs ranked by humans).\n* Select only the first level of the tree conversation.",
"## Dataset Structure\n\nThe dataset has 3909 rows of tuples (instructions and outputs)."
] |
[
"TAGS\n#size_categories-1K<n<10K #language-Spanish #license-apache-2.0 #region-us \n",
"# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2",
"## Dataset Summary\n\nSubset of the original OpenAssistant Conversations Dataset (OASST).\n\n* Filtered by 'lang=es'.\n* Formatted according to the Llama-2 pattern: \"\\<s> [INST] user prompt [/INST] output model \\</s>\"\n* Select the best ranked output (Some instructions had multiple outputs ranked by humans).\n* Select only the first level of the tree conversation.",
"## Dataset Structure\n\nThe dataset has 3909 rows of tuples (instructions and outputs)."
] |
[
31,
22,
96,
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #language-Spanish #license-apache-2.0 #region-us \n# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2## Dataset Summary\n\nSubset of the original OpenAssistant Conversations Dataset (OASST).\n\n* Filtered by 'lang=es'.\n* Formatted according to the Llama-2 pattern: \"\\<s> [INST] user prompt [/INST] output model \\</s>\"\n* Select the best ranked output (Some instructions had multiple outputs ranked by humans).\n* Select only the first level of the tree conversation.## Dataset Structure\n\nThe dataset has 3909 rows of tuples (instructions and outputs)."
] |
6ee301e3c2aa5e83d04cbac9b473432d4105facd
|
# Dataset of Yukina Kiritani
This is the dataset of Yukina Kiritani, containing 101 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 101 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 237 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 101 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 101 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 101 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 101 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 101 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 237 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 237 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 237 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/yukina_kiritani_imocho
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T16:43:20+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:21+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Yukina Kiritani
==========================
This is the dataset of Yukina Kiritani, containing 101 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
c1bcf4854c26042784fa3c972fa9b37da276e431
|
# Dataset Card for Evaluation run of Phind/Phind-CodeLlama-34B-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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 agregated 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_Phind__Phind-CodeLlama-34B-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T16:59:17.432507](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2/blob/main/results_2023-10-23T16-59-17.432507.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": {
"em": 0.32571308724832215,
"em_stderr": 0.0047993190397442416,
"f1": 0.3870176174496661,
"f1_stderr": 0.004690520641787959,
"acc": 0.47511298949899267,
"acc_stderr": 0.01213509959347268
},
"harness|drop|3": {
"em": 0.32571308724832215,
"em_stderr": 0.0047993190397442416,
"f1": 0.3870176174496661,
"f1_stderr": 0.004690520641787959
},
"harness|gsm8k|5": {
"acc": 0.23199393479909022,
"acc_stderr": 0.01162687317509241
},
"harness|winogrande|5": {
"acc": 0.7182320441988951,
"acc_stderr": 0.012643326011852953
}
}
```
### 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_Phind__Phind-CodeLlama-34B-v2
|
[
"region:us"
] |
2023-08-29T16:46:17+00:00
|
{"pretty_name": "Evaluation run of Phind/Phind-CodeLlama-34B-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 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 agregated 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_Phind__Phind-CodeLlama-34B-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T16:59:17.432507](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2/blob/main/results_2023-10-23T16-59-17.432507.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 \"em\": 0.32571308724832215,\n \"em_stderr\": 0.0047993190397442416,\n \"f1\": 0.3870176174496661,\n \"f1_stderr\": 0.004690520641787959,\n \"acc\": 0.47511298949899267,\n \"acc_stderr\": 0.01213509959347268\n },\n \"harness|drop|3\": {\n \"em\": 0.32571308724832215,\n \"em_stderr\": 0.0047993190397442416,\n \"f1\": 0.3870176174496661,\n \"f1_stderr\": 0.004690520641787959\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.23199393479909022,\n \"acc_stderr\": 0.01162687317509241\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7182320441988951,\n \"acc_stderr\": 0.012643326011852953\n }\n}\n```", "repo_url": "https://huggingface.co/Phind/Phind-CodeLlama-34B-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|arc:challenge|25_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T16_59_17.432507", "path": ["**/details_harness|drop|3_2023-10-23T16-59-17.432507.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T16-59-17.432507.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T16_59_17.432507", "path": ["**/details_harness|gsm8k|5_2023-10-23T16-59-17.432507.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T16-59-17.432507.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hellaswag|10_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-management|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-marketing|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T17:45:53.549865.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T17:45:53.549865.parquet", 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T17:45:53.549865.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T17:45:53.549865.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_23T16_59_17.432507", "path": ["**/details_harness|winogrande|5_2023-10-23T16-59-17.432507.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-23T16-59-17.432507.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T17_45_53.549865", "path": ["results_2023-08-29T17:45:53.549865.parquet"]}, {"split": "2023_10_23T16_59_17.432507", "path": ["results_2023-10-23T16-59-17.432507.parquet"]}, {"split": "latest", "path": ["results_2023-10-23T16-59-17.432507.parquet"]}]}]}
|
2023-10-23T15:59:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Phind/Phind-CodeLlama-34B-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 Phind/Phind-CodeLlama-34B-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 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 agregated 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 2023-10-23T16:59:17.432507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 Phind/Phind-CodeLlama-34B-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Phind/Phind-CodeLlama-34B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-23T16:59:17.432507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 Phind/Phind-CodeLlama-34B-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Phind/Phind-CodeLlama-34B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-23T16:59:17.432507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
23,
31,
171,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
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9,
8,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Phind/Phind-CodeLlama-34B-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Phind/Phind-CodeLlama-34B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-23T16:59:17.432507(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
63c34ee3aada8c1c4d8e4ad6cda0a85a27fbd412
|
# Dataset Card for "RLCD-SFT-conversations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TaylorAI/RLCD-SFT-conversations
|
[
"region:us"
] |
2023-08-29T16:55:06+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "0", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 244406994, "num_examples": 167999}], "download_size": 137318929, "dataset_size": 244406994}}
|
2023-08-29T16:57:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "RLCD-SFT-conversations"
More Information needed
|
[
"# Dataset Card for \"RLCD-SFT-conversations\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"RLCD-SFT-conversations\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"RLCD-SFT-conversations\"\n\nMore Information needed"
] |
db6afe25a7b704c3691879039304e94e29f875c3
|
<div align="center">
<b> VOICE CONVERSATION BACKUP</b><br />
[Original Repo](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)
|
mrmocciai/mrmocci
|
[
"language:en",
"license:mit",
"code",
"region:us"
] |
2023-08-29T16:55:13+00:00
|
{"language": ["en"], "license": "mit", "tags": ["code"]}
|
2023-10-06T13:20:47+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-mit #code #region-us
|
<div align="center">
<b> VOICE CONVERSATION BACKUP</b><br />
Original Repo
|
[] |
[
"TAGS\n#language-English #license-mit #code #region-us \n"
] |
[
17
] |
[
"passage: TAGS\n#language-English #license-mit #code #region-us \n"
] |
9ad406a874ff469ed34fcd1e4e92de754804ca16
|
# Dataset Card for "instruct_v3_subset_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
aditijha/instruct_v3_subset_2
|
[
"region:us"
] |
2023-08-29T16:58:38+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3930962.2554168818, "num_examples": 1000}], "download_size": 2048066, "dataset_size": 3930962.2554168818}}
|
2023-08-29T16:58:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "instruct_v3_subset_2"
More Information needed
|
[
"# Dataset Card for \"instruct_v3_subset_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"instruct_v3_subset_2\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"instruct_v3_subset_2\"\n\nMore Information needed"
] |
effc9bbeed8bb28e2fa8bcfe181deeb8bdc111ec
|
# Bangumi Image Base of Kono Subarashii Sekai Ni Shukufuku Wo!
This is the image base of bangumi Kono Subarashii Sekai ni Shukufuku wo!, we detected 52 characters, 4562 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 8 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 9 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 5 | [Download](2/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 3 | 24 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 139 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 128 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 457 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 13 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 13 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 7 | [Download](9/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 10 | 1320 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 10 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 11 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 10 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 17 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 469 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 31 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 16 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 30 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 17 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 37 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 9 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 7 | [Download](24/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 25 | 12 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 49 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 13 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 15 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 13 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 50 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 27 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 43 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 28 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 46 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 43 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 15 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 778 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 34 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 24 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 13 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 6 | [Download](43/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 44 | 45 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 17 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 5 | [Download](46/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 47 | 6 | [Download](47/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 48 | 6 | [Download](48/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 49 | 8 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 9 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 403 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/konosuba
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-08-29T17:00:41+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T19:04:57+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Kono Subarashii Sekai Ni Shukufuku Wo!
============================================================
This is the image base of bangumi Kono Subarashii Sekai ni Shukufuku wo!, we detected 52 characters, 4562 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
32e1c5bd2dceb6659b11cd8e696a08c9cf0e9314
|
# Dataset Card for Evaluation run of NousResearch/CodeLlama-13b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NousResearch/CodeLlama-13b-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 [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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 agregated 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_NousResearch__CodeLlama-13b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T22:40:09.407812](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__CodeLlama-13b-hf/blob/main/results_2023-10-18T22-40-09.407812.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": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192065,
"f1": 0.05248531879194655,
"f1_stderr": 0.0012515405190332619,
"acc": 0.3964846847094825,
"acc_stderr": 0.011095593973496732
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192065,
"f1": 0.05248531879194655,
"f1_stderr": 0.0012515405190332619
},
"harness|gsm8k|5": {
"acc": 0.12130401819560273,
"acc_stderr": 0.008992888497275572
},
"harness|winogrande|5": {
"acc": 0.6716653512233622,
"acc_stderr": 0.01319829944971789
}
}
```
### 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_NousResearch__CodeLlama-13b-hf
|
[
"region:us"
] |
2023-08-29T17:14:16+00:00
|
{"pretty_name": "Evaluation run of NousResearch/CodeLlama-13b-hf", "dataset_summary": "Dataset automatically created during the evaluation run of model [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 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 agregated 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_NousResearch__CodeLlama-13b-hf\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T22:40:09.407812](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__CodeLlama-13b-hf/blob/main/results_2023-10-18T22-40-09.407812.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 \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652192065,\n \"f1\": 0.05248531879194655,\n \"f1_stderr\": 0.0012515405190332619,\n \"acc\": 0.3964846847094825,\n \"acc_stderr\": 0.011095593973496732\n },\n \"harness|drop|3\": {\n \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652192065,\n \"f1\": 0.05248531879194655,\n \"f1_stderr\": 0.0012515405190332619\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12130401819560273,\n \"acc_stderr\": 0.008992888497275572\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6716653512233622,\n \"acc_stderr\": 0.01319829944971789\n }\n}\n```", "repo_url": "https://huggingface.co/NousResearch/CodeLlama-13b-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_08_29T18_13_52.290314", "path": ["**/details_harness|arc:challenge|25_2023-08-29T18:13:52.290314.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T18:13:52.290314.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T22_40_09.407812", "path": ["**/details_harness|drop|3_2023-10-18T22-40-09.407812.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T22-40-09.407812.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T22_40_09.407812", "path": ["**/details_harness|gsm8k|5_2023-10-18T22-40-09.407812.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T22-40-09.407812.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T18_13_52.290314", "path": ["**/details_harness|hellaswag|10_2023-08-29T18:13:52.290314.parquet"]}, {"split": "latest", "path": 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|
2023-10-18T21:40:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of NousResearch/CodeLlama-13b-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 NousResearch/CodeLlama-13b-hf on the Open LLM Leaderboard.
The dataset is composed of 64 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 agregated 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 2023-10-18T22:40:09.407812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 NousResearch/CodeLlama-13b-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 NousResearch/CodeLlama-13b-hf on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-18T22:40:09.407812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 NousResearch/CodeLlama-13b-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 NousResearch/CodeLlama-13b-hf on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-18T22:40:09.407812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 NousResearch/CodeLlama-13b-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 NousResearch/CodeLlama-13b-hf on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-10-18T22:40:09.407812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
88cd1cda1cdccc44050bb389b74d7ed4e9675219
|
# Dataset Card for sample
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("vikas-mehta-cohere-health/sample")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("vikas-mehta-cohere-health/sample")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| sentiment | Sentiment | LabelQuestion | True | N/A | ['positive', 'neutral', 'negative'] |
| mixed-emotion | Mixed-emotion | MultiLabelQuestion | True | N/A | ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'] |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"text": "i didnt feel humiliated"
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "ca1b15e8-a86c-4cdf-8783-45d3ee4912f4",
"values": {
"mixed-emotion": {
"value": [
"fear",
"surprise"
]
},
"sentiment": {
"value": "positive"
}
}
}
],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"mixed-emotion": [
{
"status": "submitted",
"user_id": "ca1b15e8-a86c-4cdf-8783-45d3ee4912f4",
"value": [
"fear",
"surprise"
]
}
],
"mixed-emotion-suggestion": null,
"mixed-emotion-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"sentiment": [
{
"status": "submitted",
"user_id": "ca1b15e8-a86c-4cdf-8783-45d3ee4912f4",
"value": "positive"
}
],
"sentiment-suggestion": null,
"sentiment-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"text": "i didnt feel humiliated"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **sentiment** is of type `LabelQuestion` with the following allowed values ['positive', 'neutral', 'negative'].
* **mixed-emotion** is of type `MultiLabelQuestion` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **sentiment-suggestion** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative'].
* (optional) **mixed-emotion-suggestion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
#### 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]
|
vikas-mehta-cohere-health/sample
|
[
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] |
2023-08-29T17:14:56+00:00
|
{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}
|
2023-08-29T17:14:58+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #rlfh #argilla #human-feedback #region-us
|
Dataset Card for sample
=======================
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'.
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla.
* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'.
* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:
### Load with 'datasets'
To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:
### Supported Tasks and Leaderboards
This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.
There are no leaderboards associated with this dataset.
### Languages
Dataset Structure
-----------------
### Data in Argilla
The dataset is created in Argilla with: fields, questions, suggestions, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
While the same record in HuggingFace 'datasets' looks as follows:
### Data Fields
Among the dataset fields, we differentiate between the following:
* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
+ text is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.
+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].
+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
+ (optional) sentiment-suggestion is of type 'label\_selection' with the following allowed values ['positive', 'neutral', 'negative'].
+ (optional) mixed-emotion-suggestion is of type 'multi\_label\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
Additionally, we also have one more field which is optional and is the following:
* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is 'train'.
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation guidelines
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
#### Annotation process
#### Who are the annotators?
### Personal and 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 Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
27,
162,
40,
53,
68,
11,
208,
40,
464,
27,
7,
4,
10,
10,
5,
36,
5,
9,
18,
7,
8,
14,
6,
6,
5
] |
[
"passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------",
"passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file."
] |
aebb2f7a9f60e206d2d674cfa02ed5da01c9e436
|
# Dataset Card for Evaluation run of NobodyExistsOnTheInternet/PuffedLIMA13bQLORA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NobodyExistsOnTheInternet/PuffedLIMA13bQLORA
- **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 [NobodyExistsOnTheInternet/PuffedLIMA13bQLORA](https://huggingface.co/NobodyExistsOnTheInternet/PuffedLIMA13bQLORA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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 agregated 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_NobodyExistsOnTheInternet__PuffedLIMA13bQLORA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T19:41:53.265233](https://huggingface.co/datasets/open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedLIMA13bQLORA/blob/main/results_2023-09-22T19-41-53.265233.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": {
"em": 0.024853187919463088,
"em_stderr": 0.0015942840667017492,
"f1": 0.0820931208053691,
"f1_stderr": 0.0019961777964585216,
"acc": 0.4196788722651694,
"acc_stderr": 0.009952538718324454
},
"harness|drop|3": {
"em": 0.024853187919463088,
"em_stderr": 0.0015942840667017492,
"f1": 0.0820931208053691,
"f1_stderr": 0.0019961777964585216
},
"harness|gsm8k|5": {
"acc": 0.08718726307808947,
"acc_stderr": 0.0077706914167835605
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865348
}
}
```
### 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_NobodyExistsOnTheInternet__PuffedLIMA13bQLORA
|
[
"region:us"
] |
2023-08-29T17:14:58+00:00
|
{"pretty_name": "Evaluation run of NobodyExistsOnTheInternet/PuffedLIMA13bQLORA", "dataset_summary": "Dataset automatically created during the evaluation run of model [NobodyExistsOnTheInternet/PuffedLIMA13bQLORA](https://huggingface.co/NobodyExistsOnTheInternet/PuffedLIMA13bQLORA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 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 agregated 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_NobodyExistsOnTheInternet__PuffedLIMA13bQLORA\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-22T19:41:53.265233](https://huggingface.co/datasets/open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedLIMA13bQLORA/blob/main/results_2023-09-22T19-41-53.265233.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 \"em\": 0.024853187919463088,\n \"em_stderr\": 0.0015942840667017492,\n \"f1\": 0.0820931208053691,\n \"f1_stderr\": 0.0019961777964585216,\n \"acc\": 0.4196788722651694,\n \"acc_stderr\": 0.009952538718324454\n },\n \"harness|drop|3\": {\n \"em\": 0.024853187919463088,\n \"em_stderr\": 0.0015942840667017492,\n \"f1\": 0.0820931208053691,\n \"f1_stderr\": 0.0019961777964585216\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08718726307808947,\n \"acc_stderr\": 0.0077706914167835605\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865348\n }\n}\n```", "repo_url": "https://huggingface.co/NobodyExistsOnTheInternet/PuffedLIMA13bQLORA", "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_08_29T18_14_34.642776", "path": ["**/details_harness|arc:challenge|25_2023-08-29T18:14:34.642776.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T18:14:34.642776.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_22T19_41_53.265233", "path": ["**/details_harness|drop|3_2023-09-22T19-41-53.265233.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-22T19-41-53.265233.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_22T19_41_53.265233", "path": ["**/details_harness|gsm8k|5_2023-09-22T19-41-53.265233.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-22T19-41-53.265233.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T18_14_34.642776", "path": ["**/details_harness|hellaswag|10_2023-08-29T18:14:34.642776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T18:14:34.642776.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_29T18_14_34.642776", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T18:14:34.642776.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T18:14:34.642776.parquet", 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|
2023-09-22T18:42:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of NobodyExistsOnTheInternet/PuffedLIMA13bQLORA
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model NobodyExistsOnTheInternet/PuffedLIMA13bQLORA on the Open LLM Leaderboard.
The dataset is composed of 64 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 agregated 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 2023-09-22T19:41:53.265233(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 NobodyExistsOnTheInternet/PuffedLIMA13bQLORA",
"## 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 NobodyExistsOnTheInternet/PuffedLIMA13bQLORA on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-09-22T19:41:53.265233(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 NobodyExistsOnTheInternet/PuffedLIMA13bQLORA",
"## 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 NobodyExistsOnTheInternet/PuffedLIMA13bQLORA on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-09-22T19:41:53.265233(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 NobodyExistsOnTheInternet/PuffedLIMA13bQLORA## 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 NobodyExistsOnTheInternet/PuffedLIMA13bQLORA on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 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 agregated 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 2023-09-22T19:41:53.265233(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
905fdf981119aebcd195f83f96761acc46e378e1
|
# Dataset Card for "result_with_w2v2_baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
linhtran92/result_with_w2v2_baseline
|
[
"region:us"
] |
2023-08-29T17:19:26+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "id", "dtype": "string"}, {"name": "w2v2_baseline_transcription", "dtype": "string"}, {"name": "w2v2_baseline_norm", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 174371743.625, "num_examples": 1299}], "download_size": 164231284, "dataset_size": 174371743.625}}
|
2023-08-29T17:19:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "result_with_w2v2_baseline"
More Information needed
|
[
"# Dataset Card for \"result_with_w2v2_baseline\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"result_with_w2v2_baseline\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"result_with_w2v2_baseline\"\n\nMore Information needed"
] |
42a845e662941337ee3e87e98cd70ab69dcbcdb9
|
# Dataset of Mitsuki Kanzaki
This is the dataset of Mitsuki Kanzaki, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 412 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 412 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 412 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 412 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/mitsuki_kanzaki_imocho
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T17:33:40+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:23+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Mitsuki Kanzaki
==========================
This is the dataset of Mitsuki Kanzaki, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
1fcc7e9b508edeee1b685b778b46d2744ae1d1d6
|
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
|
Q-bert/LLaVa-Llama-7b-tokenized
|
[
"license:mit",
"region:us"
] |
2023-08-29T17:59:25+00:00
|
{"license": "mit"}
|
2023-08-29T18:01:26+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
19da0ae38c94bcb5d00a1dfaacdd242d76df07cc
|
# Dataset Card for "autotree_automl_house_16H_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_house_16H_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-29T18:00:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 267120000, "num_examples": 10000}, {"name": "validation", "num_bytes": 267120000, "num_examples": 10000}], "download_size": 229828254, "dataset_size": 534240000}}
|
2023-08-30T20:12:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_house_16H_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_house_16H_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_house_16H_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_house_16H_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
520f27bc7a33bc5f1263e5398fc0d654e967acca
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
csupiisc/guanaco-llama2-1k
|
[
"region:us"
] |
2023-08-29T18:01:55+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6656, "num_examples": 8}], "download_size": 6982, "dataset_size": 6656}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T18:01:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
0b3dabbb3a58c5f29e3fecf19c0f5716608c4560
|
# Dataset Card for "guanaco-llama2-configsup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
csupiisc/guanaco-llama2-configsup
|
[
"region:us"
] |
2023-08-29T18:02:40+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 833500, "num_examples": 1000}], "download_size": 7838, "dataset_size": 833500}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T05:33:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-configsup"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-configsup\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-configsup\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-configsup\"\n\nMore Information needed"
] |
6cf53e2a8e7b26d0ea85c8570e4b9b2232b7da75
|
# Dataset Card for Evaluation run of nicholasKluge/Aira-124M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nicholasKluge/Aira-124M
- **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 [nicholasKluge/Aira-124M](https://huggingface.co/nicholasKluge/Aira-124M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_nicholasKluge__Aira-124M",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T19:04:35.532451](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-124M/blob/main/results_2023-08-29T19%3A04%3A35.532451.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": 0.25265346552614076,
"acc_stderr": 0.03117857003137413,
"acc_norm": 0.253799928797708,
"acc_norm_stderr": 0.03119563902907945,
"mc1": 0.2460220318237454,
"mc1_stderr": 0.01507721920066259,
"mc2": 0.41020465472810524,
"mc2_stderr": 0.015012374839842264
},
"harness|arc:challenge|25": {
"acc": 0.19880546075085323,
"acc_stderr": 0.01166285019817554,
"acc_norm": 0.24573378839590443,
"acc_norm_stderr": 0.012581033453730107
},
"harness|hellaswag|10": {
"acc": 0.2921728739294961,
"acc_stderr": 0.004538319464111971,
"acc_norm": 0.312885879306911,
"acc_norm_stderr": 0.004627207073171273
},
"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": 0.2074074074074074,
"acc_stderr": 0.03502553170678316,
"acc_norm": 0.2074074074074074,
"acc_norm_stderr": 0.03502553170678316
},
"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": 0.2,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.2,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21132075471698114,
"acc_stderr": 0.025125766484827845,
"acc_norm": 0.21132075471698114,
"acc_norm_stderr": 0.025125766484827845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.19,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.19,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_stderr": 0.03942772444036623,
"acc_norm": 0.19,
"acc_norm_stderr": 0.03942772444036623
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.0326926380614177
},
"harness|hendrycksTest-college_physics|5": {
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},
"harness|hendrycksTest-computer_security|5": {
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},
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm_stderr": 0.03379976689896309
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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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},
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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-high_school_world_history|5": {
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},
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},
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"harness|hendrycksTest-logical_fallacies|5": {
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},
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},
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},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.3,
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm_stderr": 0.023267528432100174
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.2424581005586592,
"acc_norm_stderr": 0.014333522059217889
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.25163398692810457,
"acc_stderr": 0.024848018263875195,
"acc_norm": 0.25163398692810457,
"acc_norm_stderr": 0.024848018263875195
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.18006430868167203,
"acc_norm_stderr": 0.021823422857744953
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.25308641975308643,
"acc_norm_stderr": 0.02419180860071301
},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.2730496453900709,
"acc_norm_stderr": 0.026577860943307857
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.25554106910039115,
"acc_stderr": 0.011139857833598506,
"acc_norm": 0.25554106910039115,
"acc_norm_stderr": 0.011139857833598506
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.44485294117647056,
"acc_stderr": 0.030187532060329376,
"acc_norm": 0.44485294117647056,
"acc_norm_stderr": 0.030187532060329376
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25,
"acc_stderr": 0.01751781884501444,
"acc_norm": 0.25,
"acc_norm_stderr": 0.01751781884501444
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.18181818181818182,
"acc_stderr": 0.036942843353378,
"acc_norm": 0.18181818181818182,
"acc_norm_stderr": 0.036942843353378
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.4,
"acc_stderr": 0.031362502409358936,
"acc_norm": 0.4,
"acc_norm_stderr": 0.031362502409358936
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.24378109452736318,
"acc_stderr": 0.030360490154014652,
"acc_norm": 0.24378109452736318,
"acc_norm_stderr": 0.030360490154014652
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-virology|5": {
"acc": 0.22289156626506024,
"acc_stderr": 0.03240004825594689,
"acc_norm": 0.22289156626506024,
"acc_norm_stderr": 0.03240004825594689
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.034462962170884265,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.034462962170884265
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2460220318237454,
"mc1_stderr": 0.01507721920066259,
"mc2": 0.41020465472810524,
"mc2_stderr": 0.015012374839842264
}
}
```
### 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_nicholasKluge__Aira-124M
|
[
"region:us"
] |
2023-08-29T18:04:50+00:00
|
{"pretty_name": "Evaluation run of nicholasKluge/Aira-124M", "dataset_summary": "Dataset automatically created during the evaluation run of model [nicholasKluge/Aira-124M](https://huggingface.co/nicholasKluge/Aira-124M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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_nicholasKluge__Aira-124M\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T19:04:35.532451](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-124M/blob/main/results_2023-08-29T19%3A04%3A35.532451.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\": 0.25265346552614076,\n \"acc_stderr\": 0.03117857003137413,\n \"acc_norm\": 0.253799928797708,\n \"acc_norm_stderr\": 0.03119563902907945,\n \"mc1\": 0.2460220318237454,\n \"mc1_stderr\": 0.01507721920066259,\n \"mc2\": 0.41020465472810524,\n \"mc2_stderr\": 0.015012374839842264\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.19880546075085323,\n \"acc_stderr\": 0.01166285019817554,\n \"acc_norm\": 0.24573378839590443,\n \"acc_norm_stderr\": 0.012581033453730107\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2921728739294961,\n \"acc_stderr\": 0.004538319464111971,\n \"acc_norm\": 0.312885879306911,\n \"acc_norm_stderr\": 0.004627207073171273\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\": 0.2074074074074074,\n \"acc_stderr\": 0.03502553170678316,\n \"acc_norm\": 0.2074074074074074,\n \"acc_norm_stderr\": 0.03502553170678316\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\": 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827845,\n \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827845\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\": 0.19,\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\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\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\": 0.25,\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\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.04096985139843671,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.04096985139843671\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24867724867724866,\n \"acc_stderr\": 0.022261817692400168,\n \"acc_norm\": 0.24867724867724866,\n \"acc_norm_stderr\": 0.022261817692400168\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.16666666666666666,\n \"acc_stderr\": 0.03333333333333337,\n \"acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.03333333333333337\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.13,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\": 0.13,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.21935483870967742,\n \"acc_stderr\": 0.023540799358723285,\n \"acc_norm\": 0.21935483870967742,\n \"acc_norm_stderr\": 0.023540799358723285\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.19704433497536947,\n \"acc_stderr\": 0.02798672466673622,\n \"acc_norm\": 0.19704433497536947,\n \"acc_norm_stderr\": 0.02798672466673622\n },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\": {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.03192271569548299,\n \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.03192271569548299\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\": 0.30256410256410254,\n \"acc_stderr\": 0.023290888053772725,\n \"acc_norm\": 0.30256410256410254,\n \"acc_norm_stderr\": 0.023290888053772725\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.22962962962962963,\n \"acc_stderr\": 0.02564410863926763,\n \"acc_norm\": 0.22962962962962963,\n \"acc_norm_stderr\": 0.02564410863926763\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868966,\n \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868966\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.3467889908256881,\n \"acc_stderr\": 0.020406097104093027,\n \"acc_norm\": 0.3467889908256881,\n \"acc_norm_stderr\": 0.020406097104093027\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\": 0.27941176470588236,\n \"acc_stderr\": 0.031493281045079556,\n \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.031493281045079556\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.28270042194092826,\n \"acc_stderr\": 0.02931281415395594,\n \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.02931281415395594\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.1210762331838565,\n \"acc_stderr\": 0.021894174113185737,\n \"acc_norm\": 0.1210762331838565,\n \"acc_norm_stderr\": 0.021894174113185737\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\": 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.03487825168497892,\n \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.03487825168497892\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2767857142857143,\n \"acc_stderr\": 0.04246624336697624,\n \"acc_norm\": 0.2767857142857143,\n \"acc_norm_stderr\": 0.04246624336697624\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822585,\n \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822585\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23504273504273504,\n \"acc_stderr\": 0.027778835904935427,\n \"acc_norm\": 0.23504273504273504,\n \"acc_norm_stderr\": 0.027778835904935427\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\": 0.24521072796934865,\n \"acc_stderr\": 0.01538435228454394,\n \"acc_norm\": 0.24521072796934865,\n \"acc_norm_stderr\": 0.01538435228454394\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\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875195,\n \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875195\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.18006430868167203,\n \"acc_stderr\": 0.021823422857744953,\n \"acc_norm\": 0.18006430868167203,\n \"acc_norm_stderr\": 0.021823422857744953\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.25308641975308643,\n \"acc_stderr\": 0.02419180860071301,\n \"acc_norm\": 0.25308641975308643,\n \"acc_norm_stderr\": 0.02419180860071301\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2730496453900709,\n \"acc_stderr\": 0.026577860943307857,\n \"acc_norm\": 0.2730496453900709,\n \"acc_norm_stderr\": 0.026577860943307857\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.25554106910039115,\n \"acc_stderr\": 0.011139857833598506,\n \"acc_norm\": 0.25554106910039115,\n \"acc_norm_stderr\": 0.011139857833598506\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.030187532060329376,\n \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.030187532060329376\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\": 0.18181818181818182,\n \"acc_stderr\": 0.036942843353378,\n \"acc_norm\": 0.18181818181818182,\n \"acc_norm_stderr\": 0.036942843353378\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\": 0.24378109452736318,\n \"acc_stderr\": 0.030360490154014652,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.030360490154014652\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.22289156626506024,\n \"acc_stderr\": 0.03240004825594689,\n \"acc_norm\": 0.22289156626506024,\n \"acc_norm_stderr\": 0.03240004825594689\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.034462962170884265,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.034462962170884265\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2460220318237454,\n \"mc1_stderr\": 0.01507721920066259,\n \"mc2\": 0.41020465472810524,\n \"mc2_stderr\": 0.015012374839842264\n }\n}\n```", "repo_url": "https://huggingface.co/nicholasKluge/Aira-124M", "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_08_29T19_04_35.532451", "path": ["**/details_harness|arc:challenge|25_2023-08-29T19:04:35.532451.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T19:04:35.532451.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": 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|
2023-08-29T18:05:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of nicholasKluge/Aira-124M
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model nicholasKluge/Aira-124M on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T19:04:35.532451(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 nicholasKluge/Aira-124M",
"## 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 nicholasKluge/Aira-124M on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T19:04:35.532451(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 nicholasKluge/Aira-124M",
"## 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 nicholasKluge/Aira-124M on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T19:04:35.532451(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 nicholasKluge/Aira-124M## 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 nicholasKluge/Aira-124M on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated 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 2023-08-29T19:04:35.532451(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
cb75dfe41f86e01a282f08acfa2da0cc0929250a
|
purvesh/MCD_ABSA
|
[
"region:us"
] |
2023-08-29T18:06:14+00:00
|
{}
|
2023-08-29T18:15:21+00:00
|
[] |
[] |
TAGS
#region-us
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
||
fa513fc0af512f5132a8f3e4a88b092a1664947a
|
# Dataset Card for "result_with_w2v2_baseline_snfintuned_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
linhtran92/result_with_w2v2_baseline_snfintuned_20
|
[
"region:us"
] |
2023-08-29T18:16:48+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "id", "dtype": "string"}, {"name": "w2v2_baseline_transcription", "dtype": "string"}, {"name": "w2v2_baseline_norm", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 174374874.625, "num_examples": 1299}], "download_size": 164232283, "dataset_size": 174374874.625}}
|
2023-08-29T18:17:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "result_with_w2v2_baseline_snfintuned_20"
More Information needed
|
[
"# Dataset Card for \"result_with_w2v2_baseline_snfintuned_20\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"result_with_w2v2_baseline_snfintuned_20\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"result_with_w2v2_baseline_snfintuned_20\"\n\nMore Information needed"
] |
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