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1d9dd591042f453b32c3a78f21cf586fbaf6cd50
|
# Dataset Card for "cqadupstack-gaming"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-gaming
|
[
"region:us"
] |
2023-10-09T11:39:39+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 105494, "num_examples": 1595}, {"name": "corpus", "num_bytes": 20666596, "num_examples": 45301}], "download_size": 12946080, "dataset_size": 20772090}}
|
2023-10-09T11:39:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-gaming"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-gaming\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-gaming\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-gaming\"\n\nMore Information needed"
] |
6af912484b05750722b0fb9920683af29ae692ef
|
# Dataset Card for "cqadupstack-mathematica"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-mathematica
|
[
"region:us"
] |
2023-10-09T11:39:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 52792, "num_examples": 804}, {"name": "corpus", "num_bytes": 18735825, "num_examples": 16705}], "download_size": 10393860, "dataset_size": 18788617}}
|
2023-10-09T11:39:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-mathematica"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-mathematica\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-mathematica\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-mathematica\"\n\nMore Information needed"
] |
e2f8167fa98c900c5afb0ae52a8ec6979b62e526
|
# Dataset Card for "cqadupstack-programmers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-programmers
|
[
"region:us"
] |
2023-10-09T11:39:57+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 63785, "num_examples": 876}, {"name": "corpus", "num_bytes": 32727262, "num_examples": 32176}], "download_size": 19360000, "dataset_size": 32791047}}
|
2023-10-09T11:40:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-programmers"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-programmers\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-programmers\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-programmers\"\n\nMore Information needed"
] |
08af78097fe7d9341c4d61735c2e80e8a32f1090
|
# Dataset Card for "cqadupstack-tex"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-tex
|
[
"region:us"
] |
2023-10-09T11:40:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 186934, "num_examples": 2906}, {"name": "corpus", "num_bytes": 86600423, "num_examples": 68184}], "download_size": 43424126, "dataset_size": 86787357}}
|
2023-10-09T11:40:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-tex"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-tex\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-tex\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-tex\"\n\nMore Information needed"
] |
19a1f59786b13fa662e2aa62cab3f1736bcbfc2d
|
# Dataset Card for "cqadupstack-webmasters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-webmasters
|
[
"region:us"
] |
2023-10-09T11:41:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 34792, "num_examples": 506}, {"name": "corpus", "num_bytes": 11659413, "num_examples": 17405}], "download_size": 6885106, "dataset_size": 11694205}}
|
2023-10-09T11:41:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-webmasters"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-webmasters\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-webmasters\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-webmasters\"\n\nMore Information needed"
] |
454cd762c99afbb3573bca03c2a267eececfec59
|
# Dataset Card for "disease-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ftuncc/disease-ds-mini
|
[
"region:us"
] |
2023-10-09T11:41:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "Disease", "dtype": "string"}, {"name": "Fever", "dtype": "string"}, {"name": "Cough", "dtype": "string"}, {"name": "Fatigue", "dtype": "string"}, {"name": "Difficulty Breathing", "dtype": "string"}, {"name": "Age", "dtype": "int64"}, {"name": "Gender", "dtype": "string"}, {"name": "Blood Pressure", "dtype": "string"}, {"name": "Cholesterol Level", "dtype": "string"}, {"name": "Outcome Variable", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28090.85386819484, "num_examples": 314}, {"name": "validation", "num_bytes": 3131.1461318051574, "num_examples": 35}], "download_size": 12876, "dataset_size": 31222.0}}
|
2023-10-09T11:41:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "disease-ds-mini"
More Information needed
|
[
"# Dataset Card for \"disease-ds-mini\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"disease-ds-mini\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"disease-ds-mini\"\n\nMore Information needed"
] |
3edf77d2dd03d1ac16184e23af83e3095c24438e
|
# Dataset Card for "cqadupstack-english"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-english
|
[
"region:us"
] |
2023-10-09T11:41:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 103588, "num_examples": 1570}, {"name": "corpus", "num_bytes": 18199570, "num_examples": 40221}], "download_size": 11382247, "dataset_size": 18303158}}
|
2023-10-09T11:41:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-english"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-english\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-english\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-english\"\n\nMore Information needed"
] |
417a1f7b3e7a6829fe45223d2a2d5e83c0cb4383
|
# Dataset Card for "cqadupstack-gis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-gis
|
[
"region:us"
] |
2023-10-09T11:41:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 61244, "num_examples": 885}, {"name": "corpus", "num_bytes": 36704924, "num_examples": 37637}], "download_size": 20083359, "dataset_size": 36766168}}
|
2023-10-09T11:41:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-gis"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-gis\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-gis\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-gis\"\n\nMore Information needed"
] |
6fc51d8a4663b1d881ebc5f793cb0833a4294bb3
|
# Dataset Card for "cqadupstack-physics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-physics
|
[
"region:us"
] |
2023-10-09T11:41:33+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 73255, "num_examples": 1039}, {"name": "corpus", "num_bytes": 29949928, "num_examples": 38316}], "download_size": 17827262, "dataset_size": 30023183}}
|
2023-10-09T11:41:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-physics"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-physics\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-physics\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-physics\"\n\nMore Information needed"
] |
9044cbe43f17b5ac61f83c7dbc0d51f6b8f61397
|
# Dataset Card for "cqadupstack-physics-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-physics-qrels
|
[
"region:us"
] |
2023-10-09T11:41:40+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "query-id", "dtype": "string"}, {"name": "corpus-id", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 50809, "num_examples": 1933}], "download_size": 25022, "dataset_size": 50809}}
|
2023-10-09T11:41:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-physics-qrels"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-physics-qrels\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-physics-qrels\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-physics-qrels\"\n\nMore Information needed"
] |
4544f68129a25a6c3ccceacb5d4039ac6e2af72e
|
# Dataset Card for "cqadupstack-stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-stats
|
[
"region:us"
] |
2023-10-09T11:41:44+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 47795, "num_examples": 652}, {"name": "corpus", "num_bytes": 42923933, "num_examples": 42269}], "download_size": 24679799, "dataset_size": 42971728}}
|
2023-10-09T11:41:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-stats"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-stats\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-stats\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-stats\"\n\nMore Information needed"
] |
4275901780afbf8cdd4f43f1ea91dda3b99bb6a5
|
# Dataset Card for "cqadupstack-stats-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-stats-qrels
|
[
"region:us"
] |
2023-10-09T11:41:49+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "query-id", "dtype": "string"}, {"name": "corpus-id", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 23665, "num_examples": 913}], "download_size": 13316, "dataset_size": 23665}}
|
2023-10-09T11:41:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-stats-qrels"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-stats-qrels\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-stats-qrels\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-stats-qrels\"\n\nMore Information needed"
] |
71986ad1cef369476e1b48f1482ffb6ea7a136fd
|
# Dataset Card for "cqadupstack-unix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-unix
|
[
"region:us"
] |
2023-10-09T11:41:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 72357, "num_examples": 1072}, {"name": "corpus", "num_bytes": 46102756, "num_examples": 47382}], "download_size": 24571026, "dataset_size": 46175113}}
|
2023-10-09T11:42:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-unix"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-unix\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-unix\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-unix\"\n\nMore Information needed"
] |
f33e541880dc8b6838cf379c3615633507fcab9d
|
# Dataset Card for "cqadupstack-unix-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-unix-qrels
|
[
"region:us"
] |
2023-10-09T11:42:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "query-id", "dtype": "string"}, {"name": "corpus-id", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 44636, "num_examples": 1693}], "download_size": 23577, "dataset_size": 44636}}
|
2023-10-09T11:42:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-unix-qrels"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-unix-qrels\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-unix-qrels\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-unix-qrels\"\n\nMore Information needed"
] |
3d564e224082020f2fb679ad71c135b215cbd95f
|
# Dataset Card for "cqadupstack-wordpress"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-wordpress
|
[
"region:us"
] |
2023-10-09T11:42:04+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 35736, "num_examples": 541}, {"name": "corpus", "num_bytes": 53026140, "num_examples": 48605}], "download_size": 26551471, "dataset_size": 53061876}}
|
2023-10-09T11:42:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-wordpress"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-wordpress\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-wordpress\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-wordpress\"\n\nMore Information needed"
] |
d52b1d0ae2df2598c18624dedc269ca72ee5adb0
|
# Dataset Card for "cqadupstack-wordpress-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/cqadupstack-wordpress-qrels
|
[
"region:us"
] |
2023-10-09T11:42:09+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "query-id", "dtype": "string"}, {"name": "corpus-id", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 19885, "num_examples": 744}], "download_size": 11490, "dataset_size": 19885}}
|
2023-10-09T11:42:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cqadupstack-wordpress-qrels"
More Information needed
|
[
"# Dataset Card for \"cqadupstack-wordpress-qrels\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cqadupstack-wordpress-qrels\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cqadupstack-wordpress-qrels\"\n\nMore Information needed"
] |
36ca72803a4382060f70dc0d5de35a698a64a987
|
# Dataset Card for Evaluation run of akjindal53244/Mistral-7B-v0.1-Open-Platypus
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus
- **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 [akjindal53244/Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus) 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_akjindal53244__Mistral-7B-v0.1-Open-Platypus",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T03:30:37.870273](https://huggingface.co/datasets/open-llm-leaderboard/details_akjindal53244__Mistral-7B-v0.1-Open-Platypus/blob/main/results_2023-10-25T03-30-37.870273.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": 0.16128355704697986,
"em_stderr": 0.0037665373341562473,
"f1": 0.21934249161073788,
"f1_stderr": 0.003766121643482467,
"acc": 0.47474797642135197,
"acc_stderr": 0.011060564905702893
},
"harness|drop|3": {
"em": 0.16128355704697986,
"em_stderr": 0.0037665373341562473,
"f1": 0.21934249161073788,
"f1_stderr": 0.003766121643482467
},
"harness|gsm8k|5": {
"acc": 0.1728582259287339,
"acc_stderr": 0.010415432246200586
},
"harness|winogrande|5": {
"acc": 0.77663772691397,
"acc_stderr": 0.011705697565205201
}
}
```
### 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_akjindal53244__Mistral-7B-v0.1-Open-Platypus
|
[
"region:us"
] |
2023-10-09T11:53:05+00:00
|
{"pretty_name": "Evaluation run of akjindal53244/Mistral-7B-v0.1-Open-Platypus", "dataset_summary": "Dataset automatically created during the evaluation run of model [akjindal53244/Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus) 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_akjindal53244__Mistral-7B-v0.1-Open-Platypus\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T03:30:37.870273](https://huggingface.co/datasets/open-llm-leaderboard/details_akjindal53244__Mistral-7B-v0.1-Open-Platypus/blob/main/results_2023-10-25T03-30-37.870273.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\": 0.16128355704697986,\n \"em_stderr\": 0.0037665373341562473,\n \"f1\": 0.21934249161073788,\n \"f1_stderr\": 0.003766121643482467,\n \"acc\": 0.47474797642135197,\n \"acc_stderr\": 0.011060564905702893\n },\n \"harness|drop|3\": {\n \"em\": 0.16128355704697986,\n \"em_stderr\": 0.0037665373341562473,\n \"f1\": 0.21934249161073788,\n \"f1_stderr\": 0.003766121643482467\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1728582259287339,\n \"acc_stderr\": 0.010415432246200586\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.77663772691397,\n \"acc_stderr\": 0.011705697565205201\n }\n}\n```", "repo_url": "https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus", "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_10_09T12_52_41.880840", "path": ["**/details_harness|arc:challenge|25_2023-10-09T12-52-41.880840.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-09T12-52-41.880840.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T03_30_37.870273", "path": ["**/details_harness|drop|3_2023-10-25T03-30-37.870273.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T03-30-37.870273.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T03_30_37.870273", "path": ["**/details_harness|gsm8k|5_2023-10-25T03-30-37.870273.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T03-30-37.870273.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_09T12_52_41.880840", "path": ["**/details_harness|hellaswag|10_2023-10-09T12-52-41.880840.parquet"]}, {"split": "latest", "path": 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|
2023-10-25T02:30:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of akjindal53244/Mistral-7B-v0.1-Open-Platypus
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model akjindal53244/Mistral-7B-v0.1-Open-Platypus 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 2023-10-25T03:30:37.870273(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 akjindal53244/Mistral-7B-v0.1-Open-Platypus",
"## 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 akjindal53244/Mistral-7B-v0.1-Open-Platypus 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 2023-10-25T03:30:37.870273(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 akjindal53244/Mistral-7B-v0.1-Open-Platypus 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 2023-10-25T03:30:37.870273(note that their might be results for other tasks in the repos if successive 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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"## 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 akjindal53244/Mistral-7B-v0.1-Open-Platypus## 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 akjindal53244/Mistral-7B-v0.1-Open-Platypus 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 2023-10-25T03:30:37.870273(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
f1aaf66b0237228c2e0904e7980d71c34854e2d3
|
# Dataset Card for Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B
- **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 [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B) 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_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.0-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T17:59:18.672226](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.0-7B/blob/main/results_2023-10-28T17-59-18.672226.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": 0.007655201342281879,
"em_stderr": 0.0008925843316825968,
"f1": 0.06762374161073832,
"f1_stderr": 0.0015672145775403328,
"acc": 0.48594340621826704,
"acc_stderr": 0.011102174081480334
},
"harness|drop|3": {
"em": 0.007655201342281879,
"em_stderr": 0.0008925843316825968,
"f1": 0.06762374161073832,
"f1_stderr": 0.0015672145775403328
},
"harness|gsm8k|5": {
"acc": 0.18498862774829417,
"acc_stderr": 0.010695390472237908
},
"harness|winogrande|5": {
"acc": 0.7868981846882399,
"acc_stderr": 0.01150895769072276
}
}
```
### 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_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.0-7B
|
[
"region:us"
] |
2023-10-09T12:04:20+00:00
|
{"pretty_name": "Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B) 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_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.0-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T17:59:18.672226](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.0-7B/blob/main/results_2023-10-28T17-59-18.672226.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\": 0.007655201342281879,\n \"em_stderr\": 0.0008925843316825968,\n \"f1\": 0.06762374161073832,\n \"f1_stderr\": 0.0015672145775403328,\n \"acc\": 0.48594340621826704,\n \"acc_stderr\": 0.011102174081480334\n },\n \"harness|drop|3\": {\n \"em\": 0.007655201342281879,\n \"em_stderr\": 0.0008925843316825968,\n \"f1\": 0.06762374161073832,\n \"f1_stderr\": 0.0015672145775403328\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18498862774829417,\n \"acc_stderr\": 0.010695390472237908\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.01150895769072276\n }\n}\n```", "repo_url": "https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B", "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": 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|
2023-10-28T16:59:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B 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 2023-10-28T17:59:18.672226(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B",
"## 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 PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B 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 2023-10-28T17:59:18.672226(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B",
"## 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 PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B 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 2023-10-28T17:59:18.672226(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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,
32,
31,
180,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B## 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 PeanutJar/Mistral-v0.1-PeanutButter-v0.0.0-7B 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 2023-10-28T17:59:18.672226(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
3b15a150e842a07f30046013edf4f941bea5d553
|
Eng = This is the training data of the character voice models from "Fallout 4" for so-vits-svc-4.1.21
Rus = Это тренировочные данные моделей голосов персонажей из "Fallout 4" для so-vits-svc-4.1.21
|
Rootreck/so-vits-svc-4.0-Fallout_4
|
[
"language:en",
"language:ru",
"region:us"
] |
2023-10-09T12:08:23+00:00
|
{"language": ["en", "ru"]}
|
2023-11-29T16:46:27+00:00
|
[] |
[
"en",
"ru"
] |
TAGS
#language-English #language-Russian #region-us
|
Eng = This is the training data of the character voice models from "Fallout 4" for so-vits-svc-4.1.21
Rus = Это тренировочные данные моделей голосов персонажей из "Fallout 4" для so-vits-svc-4.1.21
|
[] |
[
"TAGS\n#language-English #language-Russian #region-us \n"
] |
[
15
] |
[
"passage: TAGS\n#language-English #language-Russian #region-us \n"
] |
f443e440ce1bf813e158af4791134158fc07459b
|
# Dataset Card for "batangueno-accent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jrs-a/batangueno-accent
|
[
"region:us"
] |
2023-10-09T12:11:04+00:00
|
{"dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "input_length", "dtype": "string"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 244706143.0, "num_examples": 471}], "download_size": 225571755, "dataset_size": 244706143.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T16:00:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "batangueno-accent"
More Information needed
|
[
"# Dataset Card for \"batangueno-accent\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"batangueno-accent\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"batangueno-accent\"\n\nMore Information needed"
] |
4050c161b336fb089400261ca553b3ad11a3e4db
|
# Dataset Card for "cartoon-faces"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Skiittoo/cartoon-faces
|
[
"region:us"
] |
2023-10-09T12:13:53+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": 646360781.0, "num_examples": 10000}], "download_size": 647319030, "dataset_size": 646360781.0}}
|
2023-10-09T12:14:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cartoon-faces"
More Information needed
|
[
"# Dataset Card for \"cartoon-faces\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cartoon-faces\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cartoon-faces\"\n\nMore Information needed"
] |
43fdec50203438345ebc3bcd02f76f9847260403
|
# Dataset Card for "images_rotation_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vmalitskyi/images_rotation_dataset
|
[
"region:us"
] |
2023-10-09T12:14:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "name", "dtype": "string"}, {"name": "sender_id", "dtype": "int64"}, {"name": "label", "dtype": "int64"}, {"name": "kids", "dtype": "int64"}, {"name": "class", "dtype": "int64"}, {"name": "fold", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 32685477988, "num_examples": 21612}], "download_size": 10754548456, "dataset_size": 32685477988}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:02:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "images_rotation_dataset"
More Information needed
|
[
"# Dataset Card for \"images_rotation_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"images_rotation_dataset\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"images_rotation_dataset\"\n\nMore Information needed"
] |
ead7cd7d12db15bc41f227233e2160c9f4dfba44
|
# Dataset Card for "solar-incorrect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Martin97Bozic/solar-incorrect
|
[
"region:us"
] |
2023-10-09T12:22:07+00:00
|
{"dataset_info": {"features": [{"name": "id_doc", "dtype": "string"}, {"name": "doc_title", "dtype": "string"}, {"name": "is_manually_validated", "dtype": "bool"}, {"name": "src_tokens", "sequence": "string"}, {"name": "src_ling_annotations", "struct": [{"name": "lemma", "sequence": "string"}, {"name": "ana", "sequence": "string"}, {"name": "msd", "sequence": "string"}, {"name": "ne_tag", "sequence": "string"}, {"name": "space_after", "sequence": "bool"}]}, {"name": "tgt_tokens", "sequence": "string"}, {"name": "tgt_ling_annotations", "struct": [{"name": "lemma", "sequence": "string"}, {"name": "ana", "sequence": "string"}, {"name": "msd", "sequence": "string"}, {"name": "ne_tag", "sequence": "string"}, {"name": "space_after", "sequence": "bool"}]}, {"name": "corrections", "list": [{"name": "idx_src", "sequence": "int32"}, {"name": "idx_tgt", "sequence": "int32"}, {"name": "corr_types", "sequence": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6534388, "num_examples": 512}], "download_size": 878259, "dataset_size": 6534388}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T12:26:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "solar-incorrect"
More Information needed
|
[
"# Dataset Card for \"solar-incorrect\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"solar-incorrect\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"solar-incorrect\"\n\nMore Information needed"
] |
1bc24abe086a24943d2ba0e761d0cc80a33b1e26
|
# Dataset Card for "my_newest_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
harinarayan/my_newest_dataset
|
[
"region:us"
] |
2023-10-09T12:22:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1558328.0, "num_examples": 36}], "download_size": 1436147, "dataset_size": 1558328.0}}
|
2023-10-09T12:22:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "my_newest_dataset"
More Information needed
|
[
"# Dataset Card for \"my_newest_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"my_newest_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"my_newest_dataset\"\n\nMore Information needed"
] |
04bd3a6853701f8464a4b831236f0e4a5f87f74e
|
# Dataset Card for "openwebtext-100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mychen76/openwebtext-100k
|
[
"region:us"
] |
2023-10-09T12:32:49+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 497257202, "num_examples": 100000}], "download_size": 302557845, "dataset_size": 497257202}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T12:37:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "openwebtext-100k"
More Information needed
|
[
"# Dataset Card for \"openwebtext-100k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"openwebtext-100k\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"openwebtext-100k\"\n\nMore Information needed"
] |
da9a1cafb74acb3da269f599430c7e5d4825f160
|
# Dataset Card for "spotlight-matthijs-snacks-enrichment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
renumics/spotlight-matthijs-snacks-enrichment
|
[
"region:us"
] |
2023-10-09T12:39:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "image.embedding", "sequence": "float32", "length": 2}], "splits": [{"name": "train", "num_bytes": 38704, "num_examples": 4838}, {"name": "test", "num_bytes": 7616, "num_examples": 952}, {"name": "validation", "num_bytes": 7640, "num_examples": 955}], "download_size": 77321, "dataset_size": 53960}}
|
2023-10-13T08:28:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "spotlight-matthijs-snacks-enrichment"
More Information needed
|
[
"# Dataset Card for \"spotlight-matthijs-snacks-enrichment\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"spotlight-matthijs-snacks-enrichment\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"spotlight-matthijs-snacks-enrichment\"\n\nMore Information needed"
] |
aee21e4d9340f5a2afb1b6b3e7b616463b59e1ef
|
## Dataset Creation
The AG-Pair dataset is built from the original dataset AG's News that contains 120k training samples from four topics.
Given a pair of news as input, the model has to predict whether they are belonging to the same topic (Same) or not (Different).
To generate this dataset, samples in AG are iterated in random order and have an equal chance to combine a sample in the same topic or the other three topics.
Thus the numbers of training samples in two classes are both 60k.
Moreover, each news in AG's News occurs exactly twice in the AG-Pair dataset to keep the same word frequency.
## Additional Information
### Dataset Curators
[Chong Li]([email protected])
### Citation Information
```
@inproceedings{li-etal-2023-FunctionalSpecialization,
author = {Chong Li and
Shaonan Wang and
Yunhao Zhang and
Jiajun Zhang and
Chengqing Zong},
title = "Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
```
|
chongli17/AG-Pair
|
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:unknown",
"region:us"
] |
2023-10-09T12:39:56+00:00
|
{"language": ["en"], "license": "unknown", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]}
|
2023-10-09T12:53:52+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-unknown #region-us
|
## Dataset Creation
The AG-Pair dataset is built from the original dataset AG's News that contains 120k training samples from four topics.
Given a pair of news as input, the model has to predict whether they are belonging to the same topic (Same) or not (Different).
To generate this dataset, samples in AG are iterated in random order and have an equal chance to combine a sample in the same topic or the other three topics.
Thus the numbers of training samples in two classes are both 60k.
Moreover, each news in AG's News occurs exactly twice in the AG-Pair dataset to keep the same word frequency.
## Additional Information
### Dataset Curators
Chong Li
|
[
"## Dataset Creation\n\nThe AG-Pair dataset is built from the original dataset AG's News that contains 120k training samples from four topics.\nGiven a pair of news as input, the model has to predict whether they are belonging to the same topic (Same) or not (Different).\n\nTo generate this dataset, samples in AG are iterated in random order and have an equal chance to combine a sample in the same topic or the other three topics.\nThus the numbers of training samples in two classes are both 60k.\nMoreover, each news in AG's News occurs exactly twice in the AG-Pair dataset to keep the same word frequency.",
"## Additional Information",
"### Dataset Curators\n\nChong Li"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-unknown #region-us \n",
"## Dataset Creation\n\nThe AG-Pair dataset is built from the original dataset AG's News that contains 120k training samples from four topics.\nGiven a pair of news as input, the model has to predict whether they are belonging to the same topic (Same) or not (Different).\n\nTo generate this dataset, samples in AG are iterated in random order and have an equal chance to combine a sample in the same topic or the other three topics.\nThus the numbers of training samples in two classes are both 60k.\nMoreover, each news in AG's News occurs exactly twice in the AG-Pair dataset to keep the same word frequency.",
"## Additional Information",
"### Dataset Curators\n\nChong Li"
] |
[
40,
154,
5,
9
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-unknown #region-us \n## Dataset Creation\n\nThe AG-Pair dataset is built from the original dataset AG's News that contains 120k training samples from four topics.\nGiven a pair of news as input, the model has to predict whether they are belonging to the same topic (Same) or not (Different).\n\nTo generate this dataset, samples in AG are iterated in random order and have an equal chance to combine a sample in the same topic or the other three topics.\nThus the numbers of training samples in two classes are both 60k.\nMoreover, each news in AG's News occurs exactly twice in the AG-Pair dataset to keep the same word frequency.## Additional Information### Dataset Curators\n\nChong Li"
] |
0a20f1ddf452fae40dd01471fd06e51e6fc15ee7
|
# Dataset Card for "my_small_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
harinarayan/my_small_dataset
|
[
"region:us"
] |
2023-10-09T12:50:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 445121.0, "num_examples": 8}], "download_size": 417058, "dataset_size": 445121.0}}
|
2023-10-09T12:50:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "my_small_dataset"
More Information needed
|
[
"# Dataset Card for \"my_small_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"my_small_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"my_small_dataset\"\n\nMore Information needed"
] |
ee1c8770d4070f96ce79c160fb0e88675e63f6fc
|
# Dataset Card for "news_recommendations_base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/news_recommendations_base
|
[
"region:us"
] |
2023-10-09T12:53:05+00:00
|
{"dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "sub-category", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "times", "dtype": "timestamp[ns]"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1561817, "num_examples": 3981}], "download_size": 742112, "dataset_size": 1561817}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T12:53:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "news_recommendations_base"
More Information needed
|
[
"# Dataset Card for \"news_recommendations_base\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"news_recommendations_base\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"news_recommendations_base\"\n\nMore Information needed"
] |
cf1ac06300eec193a22b0e72fc20798ecb5f477b
|
# Dataset Card for "my_tiny_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
harinarayan/my_tiny_dataset
|
[
"region:us"
] |
2023-10-09T12:54:28+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 445121.0, "num_examples": 8}], "download_size": 0, "dataset_size": 445121.0}}
|
2023-10-09T12:59:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "my_tiny_dataset"
More Information needed
|
[
"# Dataset Card for \"my_tiny_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"my_tiny_dataset\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"my_tiny_dataset\"\n\nMore Information needed"
] |
b869fdd1c98e3bfe74905c0ecd2013bb2b0d5ced
|
# Dataset Card for "18cadc88"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-muse256-muse512-wuerst-sdv15/18cadc88
|
[
"region:us"
] |
2023-10-09T13:02:50+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 191, "num_examples": 10}], "download_size": 1352, "dataset_size": 191}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:02:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "18cadc88"
More Information needed
|
[
"# Dataset Card for \"18cadc88\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"18cadc88\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"18cadc88\"\n\nMore Information needed"
] |
cd54f0c4e4bd5473ea9ab51f330414c99557e493
|
# tiny-instruct-v1
This dataset is collated from multiple other open-source datasets (de-duplicated). This has a total of ~6M rows each with an instruction and response (single-turn converstion).
#### Code Datasets:
1. [CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K)
2. [CodeExercise-Python-27k](https://huggingface.co/datasets/codefuse-ai/CodeExercise-Python-27k)
3. [Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1)
4. [tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes)
5. [Evol-instruction-66k](https://huggingface.co/datasets/codefuse-ai/Evol-instruction-66k)
6. [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
7. [programming_books_llama](https://huggingface.co/datasets/open-phi/programming_books_llama)
8. [WizardLM_evol_instruct_70k](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_70k)
#### Math Datasets:
1. [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
2. [arxiv-math-instruct-50k](https://huggingface.co/datasets/ArtifactAI/arxiv-math-instruct-50k)
3. [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
#### General Datasets:
1. [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)
2. [claude_evol_instruct_210k](https://huggingface.co/datasets/Norquinal/claude_evol_instruct_210k)
|
04RR/tiny-instruct
|
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-10-09T13:06:24+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "tiny-instruct"}
|
2023-10-15T15:55:38+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us
|
# tiny-instruct-v1
This dataset is collated from multiple other open-source datasets (de-duplicated). This has a total of ~6M rows each with an instruction and response (single-turn converstion).
#### Code Datasets:
1. CodeAlpaca_20K
2. CodeExercise-Python-27k
3. Evol-Instruct-Code-80k-v1
4. tiny-codes
5. Evol-instruction-66k
6. sciphi-python-textbook
7. programming_books_llama
8. WizardLM_evol_instruct_70k
#### Math Datasets:
1. MetaMathQA
2. arxiv-math-instruct-50k
3. MathInstruct
#### General Datasets:
1. OpenOrca
2. claude_evol_instruct_210k
|
[
"# tiny-instruct-v1\nThis dataset is collated from multiple other open-source datasets (de-duplicated). This has a total of ~6M rows each with an instruction and response (single-turn converstion).",
"#### Code Datasets:\n1. CodeAlpaca_20K\n2. CodeExercise-Python-27k\n3. Evol-Instruct-Code-80k-v1\n4. tiny-codes\n5. Evol-instruction-66k\n6. sciphi-python-textbook\n7. programming_books_llama\n8. WizardLM_evol_instruct_70k",
"#### Math Datasets:\n1. MetaMathQA\n2. arxiv-math-instruct-50k\n3. MathInstruct",
"#### General Datasets:\n1. OpenOrca\n2. claude_evol_instruct_210k"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n",
"# tiny-instruct-v1\nThis dataset is collated from multiple other open-source datasets (de-duplicated). This has a total of ~6M rows each with an instruction and response (single-turn converstion).",
"#### Code Datasets:\n1. CodeAlpaca_20K\n2. CodeExercise-Python-27k\n3. Evol-Instruct-Code-80k-v1\n4. tiny-codes\n5. Evol-instruction-66k\n6. sciphi-python-textbook\n7. programming_books_llama\n8. WizardLM_evol_instruct_70k",
"#### Math Datasets:\n1. MetaMathQA\n2. arxiv-math-instruct-50k\n3. MathInstruct",
"#### General Datasets:\n1. OpenOrca\n2. claude_evol_instruct_210k"
] |
[
41,
59,
83,
24,
23
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n# tiny-instruct-v1\nThis dataset is collated from multiple other open-source datasets (de-duplicated). This has a total of ~6M rows each with an instruction and response (single-turn converstion).#### Code Datasets:\n1. CodeAlpaca_20K\n2. CodeExercise-Python-27k\n3. Evol-Instruct-Code-80k-v1\n4. tiny-codes\n5. Evol-instruction-66k\n6. sciphi-python-textbook\n7. programming_books_llama\n8. WizardLM_evol_instruct_70k#### Math Datasets:\n1. MetaMathQA\n2. arxiv-math-instruct-50k\n3. MathInstruct#### General Datasets:\n1. OpenOrca\n2. claude_evol_instruct_210k"
] |
c5d4b72fabc7cb4d39842039439a99e928082f7c
|
# Dataset Card for "ef1e42ce"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-muse256-muse512-wuerst-sdv15/ef1e42ce
|
[
"region:us"
] |
2023-10-09T13:07:22+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 202, "num_examples": 10}], "download_size": 1381, "dataset_size": 202}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:07:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ef1e42ce"
More Information needed
|
[
"# Dataset Card for \"ef1e42ce\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ef1e42ce\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ef1e42ce\"\n\nMore Information needed"
] |
50094a15592e7474386f030eaf02dbf164873c9f
|
# Dataset Card for "9208a1cc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/9208a1cc
|
[
"region:us"
] |
2023-10-09T13:11:16+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 174, "num_examples": 10}], "download_size": 1342, "dataset_size": 174}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:11:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "9208a1cc"
More Information needed
|
[
"# Dataset Card for \"9208a1cc\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"9208a1cc\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"9208a1cc\"\n\nMore Information needed"
] |
31ffef9e84ff093a1612772b6d8db645187d9e29
|
# Dataset Card for "news_recommendations_base_vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/news_recommendations_base_vectorized
|
[
"region:us"
] |
2023-10-09T13:16:55+00:00
|
{"dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "sub-category", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "times", "dtype": "timestamp[ns]"}, {"name": "url", "dtype": "string"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 7692557, "num_examples": 3981}], "download_size": 9317253, "dataset_size": 7692557}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:16:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "news_recommendations_base_vectorized"
More Information needed
|
[
"# Dataset Card for \"news_recommendations_base_vectorized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"news_recommendations_base_vectorized\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"news_recommendations_base_vectorized\"\n\nMore Information needed"
] |
c9c65913e6fda6301063e3874b193e35e66141be
|
# Dataset Card for "testt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
W1lson/testt
|
[
"region:us"
] |
2023-10-09T13:22:42+00:00
|
{"dataset_info": {"features": [{"name": "Category", "dtype": "string"}, {"name": "Description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4499, "num_examples": 100}], "download_size": 3168, "dataset_size": 4499}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:55:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "testt"
More Information needed
|
[
"# Dataset Card for \"testt\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"testt\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"testt\"\n\nMore Information needed"
] |
d6a31dbd55a7724a969dd9e1f15282fcbd3b62ff
|
# KOR-OpenOrca-Platypus
- OpenOrca-Ko + KOpen-platypus
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
## KOpen-platpyus
Repo: [KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)
- 고품질 한국어 데이터셋
1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정
2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존
3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴
4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)
5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정
6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역
7. 고유명사는 최대한 유지함
> Post-processing 작업 내용
## OpenOrca-Ko
Repo: [OpenOrca-Ko](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO)
1. NIV // 1571개
2. FLAN // 9434개
3. T0 // 6351개
4. CoT // 2117개
5. KoCoT // 2159개
> Dataset 구성
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
kyujinpy/KOR-OpenOrca-Platypus
|
[
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<50K",
"language:ko",
"license:cc-by-nc-4.0",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] |
2023-10-09T13:23:30+00:00
|
{"language": ["ko"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<50K"], "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], "pretty_name": "OpenOrca", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 78588418, "num_examples": 46558}], "download_size": 39656100, "dataset_size": 78588418}}
|
2023-10-24T05:54:44+00:00
|
[
"2306.02707",
"2301.13688"
] |
[
"ko"
] |
TAGS
#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<50K #language-Korean #license-cc-by-nc-4.0 #arxiv-2306.02707 #arxiv-2301.13688 #region-us
|
# KOR-OpenOrca-Platypus
- OpenOrca-Ko + KOpen-platypus
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다
## KOpen-platpyus
Repo: KOpen-platypus
- 고품질 한국어 데이터셋
1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정
2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존
3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴
4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)
5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정
6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역
7. 고유명사는 최대한 유지함
> Post-processing 작업 내용
## OpenOrca-Ko
Repo: OpenOrca-Ko
1. NIV // 1571개
2. FLAN // 9434개
3. T0 // 6351개
4. CoT // 2117개
5. KoCoT // 2159개
> Dataset 구성
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- Dataset Summary
- Dataset Attribution
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Dataset Use
- Use Cases
- Usage Caveats
- Getting Started
<p><h1> The OpenOrca Dataset! </h1></p>
!OpenOrca Logo
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our second model, highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
OpenOrca-Preview1-13B
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented FLAN Collection data.
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
URL:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
URL URL
Want to visualize our full dataset? Check out our Nomic Atlas Map.
<img src="URL alt="Atlas Nomic Dataset Map" width="400" height="400" />
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021.
These are referenced by the official FLAN Collection repo as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
|
[
"# KOR-OpenOrca-Platypus\n- OpenOrca-Ko + KOpen-platypus\n- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다",
"## KOpen-platpyus\nRepo: KOpen-platypus\n\n- 고품질 한국어 데이터셋 \n1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정\n2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존\n3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴\n4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)\n5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정\n6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역\n7. 고유명사는 최대한 유지함\n> Post-processing 작업 내용",
"## OpenOrca-Ko\nRepo: OpenOrca-Ko\n\n1. NIV // 1571개\n2. FLAN // 9434개\n3. T0 // 6351개\n4. CoT // 2117개\n5. KoCoT // 2159개\n> Dataset 구성",
"## Translation\nUsing DeepL Pro API. Thanks.\n\n---\n>Below is original dataset card",
"## Table of Contents\n- Dataset Summary\n- Dataset Attribution\n- Supported Tasks and Leaderboards\n- Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Started\n\n\n<p><h1> The OpenOrca Dataset! </h1></p>\n\n!OpenOrca Logo\n\n<a name=\"dataset-announcement\"></a>\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!",
"# Official Models",
"## OpenOrca-Platypus2-13B\n\nOur latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!\nReleased in partnership with Platypus.",
"## LlongOrca 7B & 13B\n\n* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.\n* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.",
"## OpenOrcaxOpenChat-Preview2-13B\n\nOur second model, highlighting that we've surpassed the performance reported in the Orca paper.\nWas #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.\nReleased in partnership with OpenChat.",
"## OpenOrca-Preview1-13B\n\nOpenOrca-Preview1-13B\nThis model was trained in less than a day, for <$200, with <10% of our data.\nAt release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.\n\n<a name=\"dataset-summary\"></a>",
"# Dataset Summary\n\nThe OpenOrca dataset is a collection of augmented FLAN Collection data.\nCurrently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.\nIt is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.\nThe data is primarily used for training and evaluation in the field of natural language processing.\n\n<a name=\"dataset-attribution\"></a>",
"# Dataset Attribution\n\nWe would like to give special recognition to the following contributors for their significant efforts and dedication:\n \n\n Teknium \n WingLian/Caseus\n Eric Hartford\n NanoBit\n Pankaj\n Winddude\n Rohan\n\n URL:\n Autometa\n Entropi\n AtlasUnified\n NeverendingToast\n NanoBit\n WingLian/Caseus\n\nAlso of course, as always, TheBloke, for being the backbone of the whole community.\n\nMany thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! \n\nWe are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:\nURL URL\n\nWant to visualize our full dataset? Check out our Nomic Atlas Map.\n <img src=\"URL alt=\"Atlas Nomic Dataset Map\" width=\"400\" height=\"400\" />\n\n\n<a name=\"supported-tasks-and-leaderboards\"></a>",
"# Supported Tasks and Leaderboards\n\nThis dataset supports a range of tasks including language modeling, text generation, and text augmentation.\nIt has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.\nFurther information on leaderboards will be updated as they become available.\n\n<a name=\"languages\"></a>",
"# Languages\n\nThe language of the data is primarily English.\n\n<a name=\"dataset-structure\"></a>",
"# Dataset Structure\n\n<a name=\"data-instances\"></a>",
"## Data Instances\n\nA data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.\nThe response is then entered into the response field.\n\n<a name=\"data-fields\"></a>",
"## Data Fields\n\nThe fields are:\n1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.\n2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint\n3) 'question', representing a question entry as provided by the FLAN Collection\n4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.\n\n<a name=\"data-splits\"></a>",
"## Data Splits\n\nThe data is unsplit.\n\n<a name=\"dataset-creation\"></a>",
"# Dataset Creation\n\n<a name=\"curation-rationale\"></a>",
"## Curation Rationale\n\nThe dataset was created to provide a source of augmented text data for researchers and developers.\nThe datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.\nThis \"reasoning trace\" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.\n\n<a name=\"source-data\"></a>",
"## Source Data\n\nThe data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:\n\n1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.\n We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.\n2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021.\n These are referenced by the official FLAN Collection repo as the preferred data source.\n However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.\n\nCombined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.\n\n<a name=\"dataset-use\"></a>",
"# Dataset Use\n\n<a name=\"use-cases\"></a>",
"## Use Cases\n\nThe dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.\n\n<a name=\"usage-caveats\"></a>",
"## Usage Caveats\n\nGiven that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.\nFurther, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.\n\n<a name=\"getting-started\"></a>",
"## Getting Started\n\nThis dataset is organized such that it can be naively loaded via Hugging Face datasets library.\nWe recommend using streaming due to the large size of the files.\nRegular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face."
] |
[
"TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<50K #language-Korean #license-cc-by-nc-4.0 #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n",
"# KOR-OpenOrca-Platypus\n- OpenOrca-Ko + KOpen-platypus\n- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다",
"## KOpen-platpyus\nRepo: KOpen-platypus\n\n- 고품질 한국어 데이터셋 \n1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정\n2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존\n3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴\n4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)\n5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정\n6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역\n7. 고유명사는 최대한 유지함\n> Post-processing 작업 내용",
"## OpenOrca-Ko\nRepo: OpenOrca-Ko\n\n1. NIV // 1571개\n2. FLAN // 9434개\n3. T0 // 6351개\n4. CoT // 2117개\n5. KoCoT // 2159개\n> Dataset 구성",
"## Translation\nUsing DeepL Pro API. Thanks.\n\n---\n>Below is original dataset card",
"## Table of Contents\n- Dataset Summary\n- Dataset Attribution\n- Supported Tasks and Leaderboards\n- Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Started\n\n\n<p><h1> The OpenOrca Dataset! </h1></p>\n\n!OpenOrca Logo\n\n<a name=\"dataset-announcement\"></a>\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!",
"# Official Models",
"## OpenOrca-Platypus2-13B\n\nOur latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!\nReleased in partnership with Platypus.",
"## LlongOrca 7B & 13B\n\n* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.\n* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.",
"## OpenOrcaxOpenChat-Preview2-13B\n\nOur second model, highlighting that we've surpassed the performance reported in the Orca paper.\nWas #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.\nReleased in partnership with OpenChat.",
"## OpenOrca-Preview1-13B\n\nOpenOrca-Preview1-13B\nThis model was trained in less than a day, for <$200, with <10% of our data.\nAt release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.\n\n<a name=\"dataset-summary\"></a>",
"# Dataset Summary\n\nThe OpenOrca dataset is a collection of augmented FLAN Collection data.\nCurrently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.\nIt is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.\nThe data is primarily used for training and evaluation in the field of natural language processing.\n\n<a name=\"dataset-attribution\"></a>",
"# Dataset Attribution\n\nWe would like to give special recognition to the following contributors for their significant efforts and dedication:\n \n\n Teknium \n WingLian/Caseus\n Eric Hartford\n NanoBit\n Pankaj\n Winddude\n Rohan\n\n URL:\n Autometa\n Entropi\n AtlasUnified\n NeverendingToast\n NanoBit\n WingLian/Caseus\n\nAlso of course, as always, TheBloke, for being the backbone of the whole community.\n\nMany thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! \n\nWe are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:\nURL URL\n\nWant to visualize our full dataset? Check out our Nomic Atlas Map.\n <img src=\"URL alt=\"Atlas Nomic Dataset Map\" width=\"400\" height=\"400\" />\n\n\n<a name=\"supported-tasks-and-leaderboards\"></a>",
"# Supported Tasks and Leaderboards\n\nThis dataset supports a range of tasks including language modeling, text generation, and text augmentation.\nIt has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.\nFurther information on leaderboards will be updated as they become available.\n\n<a name=\"languages\"></a>",
"# Languages\n\nThe language of the data is primarily English.\n\n<a name=\"dataset-structure\"></a>",
"# Dataset Structure\n\n<a name=\"data-instances\"></a>",
"## Data Instances\n\nA data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.\nThe response is then entered into the response field.\n\n<a name=\"data-fields\"></a>",
"## Data Fields\n\nThe fields are:\n1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.\n2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint\n3) 'question', representing a question entry as provided by the FLAN Collection\n4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.\n\n<a name=\"data-splits\"></a>",
"## Data Splits\n\nThe data is unsplit.\n\n<a name=\"dataset-creation\"></a>",
"# Dataset Creation\n\n<a name=\"curation-rationale\"></a>",
"## Curation Rationale\n\nThe dataset was created to provide a source of augmented text data for researchers and developers.\nThe datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.\nThis \"reasoning trace\" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.\n\n<a name=\"source-data\"></a>",
"## Source Data\n\nThe data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:\n\n1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.\n We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.\n2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021.\n These are referenced by the official FLAN Collection repo as the preferred data source.\n However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.\n\nCombined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.\n\n<a name=\"dataset-use\"></a>",
"# Dataset Use\n\n<a name=\"use-cases\"></a>",
"## Use Cases\n\nThe dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.\n\n<a name=\"usage-caveats\"></a>",
"## Usage Caveats\n\nGiven that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.\nFurther, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.\n\n<a name=\"getting-started\"></a>",
"## Getting Started\n\nThis dataset is organized such that it can be naively loaded via Hugging Face datasets library.\nWe recommend using streaming due to the large size of the files.\nRegular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face."
] |
[
169,
53,
168,
54,
20,
199,
4,
48,
98,
67,
95,
122,
233,
86,
25,
19,
67,
153,
24,
18,
146,
235,
16,
46,
70,
66
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[
"passage: TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<50K #language-Korean #license-cc-by-nc-4.0 #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n# KOR-OpenOrca-Platypus\n- OpenOrca-Ko + KOpen-platypus\n- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다## KOpen-platpyus\nRepo: KOpen-platypus\n\n- 고품질 한국어 데이터셋 \n1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정\n2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존\n3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴\n4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)\n5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정\n6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역\n7. 고유명사는 최대한 유지함\n> Post-processing 작업 내용## OpenOrca-Ko\nRepo: OpenOrca-Ko\n\n1. NIV // 1571개\n2. FLAN // 9434개\n3. T0 // 6351개\n4. CoT // 2117개\n5. KoCoT // 2159개\n> Dataset 구성## Translation\nUsing DeepL Pro API. Thanks.\n\n---\n>Below is original dataset card",
"passage: ## Table of Contents\n- Dataset Summary\n- Dataset Attribution\n- Supported Tasks and Leaderboards\n- Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Started\n\n\n<p><h1> The OpenOrca Dataset! </h1></p>\n\n!OpenOrca Logo\n\n<a name=\"dataset-announcement\"></a>\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!# Official Models## OpenOrca-Platypus2-13B\n\nOur latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!\nReleased in partnership with Platypus.## LlongOrca 7B & 13B\n\n* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.\n* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.## OpenOrcaxOpenChat-Preview2-13B\n\nOur second model, highlighting that we've surpassed the performance reported in the Orca paper.\nWas #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.\nReleased in partnership with OpenChat.## OpenOrca-Preview1-13B\n\nOpenOrca-Preview1-13B\nThis model was trained in less than a day, for <$200, with <10% of our data.\nAt release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.\n\n<a name=\"dataset-summary\"></a># Dataset Summary\n\nThe OpenOrca dataset is a collection of augmented FLAN Collection data.\nCurrently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.\nIt is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.\nThe data is primarily used for training and evaluation in the field of natural language processing.\n\n<a name=\"dataset-attribution\"></a>",
"passage: # Dataset Attribution\n\nWe would like to give special recognition to the following contributors for their significant efforts and dedication:\n \n\n Teknium \n WingLian/Caseus\n Eric Hartford\n NanoBit\n Pankaj\n Winddude\n Rohan\n\n URL:\n Autometa\n Entropi\n AtlasUnified\n NeverendingToast\n NanoBit\n WingLian/Caseus\n\nAlso of course, as always, TheBloke, for being the backbone of the whole community.\n\nMany thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! \n\nWe are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:\nURL URL\n\nWant to visualize our full dataset? Check out our Nomic Atlas Map.\n <img src=\"URL alt=\"Atlas Nomic Dataset Map\" width=\"400\" height=\"400\" />\n\n\n<a name=\"supported-tasks-and-leaderboards\"></a># Supported Tasks and Leaderboards\n\nThis dataset supports a range of tasks including language modeling, text generation, and text augmentation.\nIt has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.\nFurther information on leaderboards will be updated as they become available.\n\n<a name=\"languages\"></a># Languages\n\nThe language of the data is primarily English.\n\n<a name=\"dataset-structure\"></a># Dataset Structure\n\n<a name=\"data-instances\"></a>## Data Instances\n\nA data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.\nThe response is then entered into the response field.\n\n<a name=\"data-fields\"></a>## Data Fields\n\nThe fields are:\n1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.\n2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint\n3) 'question', representing a question entry as provided by the FLAN Collection\n4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.\n\n<a name=\"data-splits\"></a>## Data Splits\n\nThe data is unsplit.\n\n<a name=\"dataset-creation\"></a># Dataset Creation\n\n<a name=\"curation-rationale\"></a>"
] |
dfaca7c9fe222f586cfac278e55aded8f84bcbf6
|
# Dataset Card for "testt2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
W1lson/testt2
|
[
"region:us"
] |
2023-10-09T13:56:03+00:00
|
{"dataset_info": {"features": [{"name": "Category", "dtype": "string"}, {"name": "Description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 383, "num_examples": 5}], "download_size": 1879, "dataset_size": 383}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T13:58:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "testt2"
More Information needed
|
[
"# Dataset Card for \"testt2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"testt2\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"testt2\"\n\nMore Information needed"
] |
23aac1acd8382560ba02ce05222c873f4c7b7470
|
# Dataset Card for "BossaNova"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
RorooroR/BossaNova
|
[
"region:us"
] |
2023-10-09T14:05:16+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 1147109518.125, "num_examples": 27791}], "download_size": 1143310714, "dataset_size": 1147109518.125}}
|
2023-10-09T16:13:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "BossaNova"
More Information needed
|
[
"# Dataset Card for \"BossaNova\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"BossaNova\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"BossaNova\"\n\nMore Information needed"
] |
b172c6fd5c75cdfc74307b6c5986c739815a16ff
|
# 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]
|
selinerdem/test-german-orca
|
[
"region:us"
] |
2023-10-09T14:17:26+00:00
|
{}
|
2023-10-09T14:18:27+00:00
|
[] |
[] |
TAGS
#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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"### 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 Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
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[
"passage: TAGS\n#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"
] |
9ea7aaf4a9923954c60034214efb4d169917d6c8
|
# Dataset Card for "patent_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hoang14/patent_final
|
[
"region:us"
] |
2023-10-09T14:21:16+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "prompt_len", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 305751123.29471916, "num_examples": 100488}], "download_size": 130651397, "dataset_size": 305751123.29471916}}
|
2023-10-09T14:31:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "patent_final"
More Information needed
|
[
"# Dataset Card for \"patent_final\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"patent_final\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"patent_final\"\n\nMore Information needed"
] |
3b6bd8308ecd55492298eecd2a072e37890b68eb
|
# Dataset Card for "spotlight-cifar100-enrichment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
renumics/spotlight-cifar100-enrichment
|
[
"region:us"
] |
2023-10-09T14:22:15+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "prediction", "dtype": {"class_label": {"names": {"0": "apple", "1": "aquarium_fish", "2": "baby", "3": "bear", "4": "beaver", "5": "bed", "6": "bee", "7": "beetle", "8": "bicycle", "9": "bottle", "10": "bowl", "11": "boy", "12": "bridge", "13": "bus", "14": "butterfly", "15": "camel", "16": "can", "17": "castle", "18": "caterpillar", "19": "cattle", "20": "chair", "21": "chimpanzee", "22": "clock", "23": "cloud", "24": "cockroach", "25": "couch", "26": "cra", "27": "crocodile", "28": "cup", "29": "dinosaur", "30": "dolphin", "31": "elephant", "32": "flatfish", "33": "forest", "34": "fox", "35": "girl", "36": "hamster", "37": "house", "38": "kangaroo", "39": "keyboard", "40": "lamp", "41": "lawn_mower", "42": "leopard", "43": "lion", "44": "lizard", "45": "lobster", "46": "man", "47": "maple_tree", "48": "motorcycle", "49": "mountain", "50": "mouse", "51": "mushroom", "52": "oak_tree", "53": "orange", "54": "orchid", "55": "otter", "56": "palm_tree", "57": "pear", "58": "pickup_truck", "59": "pine_tree", "60": "plain", "61": "plate", "62": "poppy", "63": "porcupine", "64": "possum", "65": "rabbit", "66": "raccoon", "67": "ray", "68": "road", "69": "rocket", "70": "rose", "71": "sea", "72": "seal", "73": "shark", "74": "shrew", "75": "skunk", "76": "skyscraper", "77": "snail", "78": "snake", "79": "spider", "80": "squirrel", "81": "streetcar", "82": "sunflower", "83": "sweet_pepper", "84": "table", "85": "tank", "86": "telephone", "87": "television", "88": "tiger", "89": "tractor", "90": "train", "91": "trout", "92": "tulip", "93": "turtle", "94": "wardrobe", "95": "whale", "96": "willow_tree", "97": "wolf", "98": "woman", "99": "worm"}}}}, {"name": "prediction_error", "dtype": "bool"}, {"name": "probability", "dtype": "float32"}, {"name": "entropy", "dtype": "float32"}, {"name": "embedding_reduced", "sequence": "float32", "length": 2}, {"name": "embedding", "sequence": "float32", "length": 768}], "splits": [{"name": "train", "num_bytes": 154806250, "num_examples": 50000}, {"name": "test", "num_bytes": 30961250, "num_examples": 10000}], "download_size": 223227009, "dataset_size": 185767500}}
|
2023-10-19T14:07:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "spotlight-cifar100-enrichment"
More Information needed
|
[
"# Dataset Card for \"spotlight-cifar100-enrichment\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"spotlight-cifar100-enrichment\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"spotlight-cifar100-enrichment\"\n\nMore Information needed"
] |
6f401a94ae0336b694ec7b6c37b398df5b0c236c
|
# Dataset Card for Evaluation run of budecosystem/boomer-1b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/budecosystem/boomer-1b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [budecosystem/boomer-1b](https://huggingface.co/budecosystem/boomer-1b) 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_budecosystem__boomer-1b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T14:53:25.007106](https://huggingface.co/datasets/open-llm-leaderboard/details_budecosystem__boomer-1b/blob/main/results_2023-10-24T14-53-25.007106.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": 0.0002773614457335763,
"f1": 0.052141359060402785,
"f1_stderr": 0.0013172260484977333,
"acc": 0.2571140151259026,
"acc_stderr": 0.008333536236283095
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335763,
"f1": 0.052141359060402785,
"f1_stderr": 0.0013172260484977333
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.002615326510775672
},
"harness|winogrande|5": {
"acc": 0.505130228887135,
"acc_stderr": 0.014051745961790516
}
}
```
### 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_budecosystem__boomer-1b
|
[
"region:us"
] |
2023-10-09T14:38:00+00:00
|
{"pretty_name": "Evaluation run of budecosystem/boomer-1b", "dataset_summary": "Dataset automatically created during the evaluation run of model [budecosystem/boomer-1b](https://huggingface.co/budecosystem/boomer-1b) 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_budecosystem__boomer-1b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T14:53:25.007106](https://huggingface.co/datasets/open-llm-leaderboard/details_budecosystem__boomer-1b/blob/main/results_2023-10-24T14-53-25.007106.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\": 0.0002773614457335763,\n \"f1\": 0.052141359060402785,\n \"f1_stderr\": 0.0013172260484977333,\n \"acc\": 0.2571140151259026,\n \"acc_stderr\": 0.008333536236283095\n },\n \"harness|drop|3\": {\n \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.0002773614457335763,\n \"f1\": 0.052141359060402785,\n \"f1_stderr\": 0.0013172260484977333\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \"acc_stderr\": 0.002615326510775672\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790516\n }\n}\n```", "repo_url": "https://huggingface.co/budecosystem/boomer-1b", "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_10_09T15_37_37.200624", "path": ["**/details_harness|arc:challenge|25_2023-10-09T15-37-37.200624.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-09T15-37-37.200624.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T14_53_25.007106", "path": ["**/details_harness|drop|3_2023-10-24T14-53-25.007106.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T14-53-25.007106.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T14_53_25.007106", "path": ["**/details_harness|gsm8k|5_2023-10-24T14-53-25.007106.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T14-53-25.007106.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_09T15_37_37.200624", "path": ["**/details_harness|hellaswag|10_2023-10-09T15-37-37.200624.parquet"]}, {"split": "latest", "path": 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TAGS
#region-us
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# Dataset Card for Evaluation run of budecosystem/boomer-1b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model budecosystem/boomer-1b 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 2023-10-24T14:53:25.007106(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 budecosystem/boomer-1b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model budecosystem/boomer-1b 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 2023-10-24T14:53:25.007106(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 budecosystem/boomer-1b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model budecosystem/boomer-1b 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 2023-10-24T14:53:25.007106(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 budecosystem/boomer-1b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model budecosystem/boomer-1b 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 2023-10-24T14:53:25.007106(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
2969ee9a40bcef59bc2ffed75ce452e92bd1b305
|
# Dataset Card for Evaluation run of caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16
- **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 [caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16](https://huggingface.co/caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16) 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_caisarl76__Mistral-7B-OpenOrca-Guanaco-accu16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T08:32:33.327127](https://huggingface.co/datasets/open-llm-leaderboard/details_caisarl76__Mistral-7B-OpenOrca-Guanaco-accu16/blob/main/results_2023-10-26T08-32-33.327127.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": 0.0045092281879194635,
"em_stderr": 0.0006861346899094969,
"f1": 0.08383808724832231,
"f1_stderr": 0.0017696414807013908,
"acc": 0.46277883857625746,
"acc_stderr": 0.011001753966995261
},
"harness|drop|3": {
"em": 0.0045092281879194635,
"em_stderr": 0.0006861346899094969,
"f1": 0.08383808724832231,
"f1_stderr": 0.0017696414807013908
},
"harness|gsm8k|5": {
"acc": 0.1599696739954511,
"acc_stderr": 0.010097377827752538
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237986
}
}
```
### 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_caisarl76__Mistral-7B-OpenOrca-Guanaco-accu16
|
[
"region:us"
] |
2023-10-09T14:53:34+00:00
|
{"pretty_name": "Evaluation run of caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16", "dataset_summary": "Dataset automatically created during the evaluation run of model [caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16](https://huggingface.co/caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16) 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_caisarl76__Mistral-7B-OpenOrca-Guanaco-accu16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-26T08:32:33.327127](https://huggingface.co/datasets/open-llm-leaderboard/details_caisarl76__Mistral-7B-OpenOrca-Guanaco-accu16/blob/main/results_2023-10-26T08-32-33.327127.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\": 0.0045092281879194635,\n \"em_stderr\": 0.0006861346899094969,\n \"f1\": 0.08383808724832231,\n \"f1_stderr\": 0.0017696414807013908,\n \"acc\": 0.46277883857625746,\n \"acc_stderr\": 0.011001753966995261\n },\n \"harness|drop|3\": {\n \"em\": 0.0045092281879194635,\n \"em_stderr\": 0.0006861346899094969,\n \"f1\": 0.08383808724832231,\n \"f1_stderr\": 0.0017696414807013908\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1599696739954511,\n \"acc_stderr\": 0.010097377827752538\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237986\n }\n}\n```", "repo_url": "https://huggingface.co/caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16", "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_10_09T15_53_10.944584", "path": ["**/details_harness|arc:challenge|25_2023-10-09T15-53-10.944584.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-09T15-53-10.944584.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_26T08_32_33.327127", "path": ["**/details_harness|drop|3_2023-10-26T08-32-33.327127.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-26T08-32-33.327127.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_26T08_32_33.327127", "path": ["**/details_harness|gsm8k|5_2023-10-26T08-32-33.327127.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-26T08-32-33.327127.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_09T15_53_10.944584", "path": ["**/details_harness|hellaswag|10_2023-10-09T15-53-10.944584.parquet"]}, {"split": "latest", "path": 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|
2023-10-26T07:32:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16 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 2023-10-26T08:32:33.327127(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16",
"## 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 caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16 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 2023-10-26T08:32:33.327127(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16 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 2023-10-26T08:32:33.327127(note that their might be results for other tasks in 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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"### Data Instances",
"### Data Fields",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16## 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 caisarl76/Mistral-7B-OpenOrca-Guanaco-accu16 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 2023-10-26T08:32:33.327127(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
707653c7e5f96ca9bf2f1f63506f4d362f7bfd09
|
# Dataset Card for Evaluation run of nicholasKluge/Aira-1B5
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nicholasKluge/Aira-1B5
- **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-1B5](https://huggingface.co/nicholasKluge/Aira-1B5) 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-1B5",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-09T15:54:46.926141](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-1B5/blob/main/results_2023-10-09T15-54-46.926141.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": 0.2743564373642291,
"acc_stderr": 0.03211959266297477,
"acc_norm": 0.27587655832273894,
"acc_norm_stderr": 0.0321270755179637,
"mc1": 0.2386780905752754,
"mc1_stderr": 0.014922629695456418,
"mc2": 0.4115839931034755,
"mc2_stderr": 0.015541548311642976
},
"harness|arc:challenge|25": {
"acc": 0.2687713310580205,
"acc_stderr": 0.01295506596371069,
"acc_norm": 0.28924914675767915,
"acc_norm_stderr": 0.013250012579393443
},
"harness|hellaswag|10": {
"acc": 0.36188010356502687,
"acc_stderr": 0.004795622757327151,
"acc_norm": 0.43108942441744674,
"acc_norm_stderr": 0.004942164585991465
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.035914440841969694,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.035914440841969694
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.29605263157894735,
"acc_stderr": 0.037150621549989056,
"acc_norm": 0.29605263157894735,
"acc_norm_stderr": 0.037150621549989056
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3018867924528302,
"acc_stderr": 0.028254200344438665,
"acc_norm": 0.3018867924528302,
"acc_norm_stderr": 0.028254200344438665
},
"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": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.32947976878612717,
"acc_stderr": 0.03583901754736411,
"acc_norm": 0.32947976878612717,
"acc_norm_stderr": 0.03583901754736411
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082633,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082633
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.20851063829787234,
"acc_stderr": 0.026556982117838728,
"acc_norm": 0.20851063829787234,
"acc_norm_stderr": 0.026556982117838728
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813344,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813344
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.26455026455026454,
"acc_stderr": 0.022717467897708604,
"acc_norm": 0.26455026455026454,
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"harness|truthfulqa:mc|0": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_nicholasKluge__Aira-1B5
|
[
"region:us"
] |
2023-10-09T14:55:01+00:00
|
{"pretty_name": "Evaluation run of nicholasKluge/Aira-1B5", "dataset_summary": "Dataset automatically created during the evaluation run of model [nicholasKluge/Aira-1B5](https://huggingface.co/nicholasKluge/Aira-1B5) 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-1B5\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-09T15:54:46.926141](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-1B5/blob/main/results_2023-10-09T15-54-46.926141.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\": 0.2743564373642291,\n \"acc_stderr\": 0.03211959266297477,\n \"acc_norm\": 0.27587655832273894,\n \"acc_norm_stderr\": 0.0321270755179637,\n \"mc1\": 0.2386780905752754,\n \"mc1_stderr\": 0.014922629695456418,\n \"mc2\": 0.4115839931034755,\n \"mc2_stderr\": 0.015541548311642976\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.2687713310580205,\n \"acc_stderr\": 0.01295506596371069,\n \"acc_norm\": 0.28924914675767915,\n \"acc_norm_stderr\": 0.013250012579393443\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.36188010356502687,\n \"acc_stderr\": 0.004795622757327151,\n \"acc_norm\": 0.43108942441744674,\n \"acc_norm_stderr\": 0.004942164585991465\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.035914440841969694,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.035914440841969694\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.29605263157894735,\n \"acc_stderr\": 0.037150621549989056,\n \"acc_norm\": 0.29605263157894735,\n \"acc_norm_stderr\": 0.037150621549989056\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.3018867924528302,\n \"acc_stderr\": 0.028254200344438665,\n \"acc_norm\": 0.3018867924528302,\n \"acc_norm_stderr\": 0.028254200344438665\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\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_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-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.32947976878612717,\n \"acc_stderr\": 0.03583901754736411,\n \"acc_norm\": 0.32947976878612717,\n \"acc_norm_stderr\": 0.03583901754736411\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082633,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082633\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.20851063829787234,\n \"acc_stderr\": 0.026556982117838728,\n \"acc_norm\": 0.20851063829787234,\n \"acc_norm_stderr\": 0.026556982117838728\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708604,\n \"acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708604\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n \"acc_stderr\": 0.0243625996930311,\n \"acc_norm\": 0.24193548387096775,\n \"acc_norm_stderr\": 0.0243625996930311\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\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.23030303030303031,\n \"acc_stderr\": 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|
2023-10-09T14:55:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of nicholasKluge/Aira-1B5
## 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-1B5 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 2023-10-09T15:54:46.926141(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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-1B5",
"## 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-1B5 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 2023-10-09T15:54:46.926141(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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-1B5",
"## 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-1B5 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 2023-10-09T15:54:46.926141(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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-1B5## 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-1B5 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 2023-10-09T15:54:46.926141(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
b8ca60be82ef25ed32ff74d25dd38cdcf6ca0c76
|
# Dataset Card for Evaluation run of caisarl76/Mistral-7B-guanaco1k-ep2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2
- **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 [caisarl76/Mistral-7B-guanaco1k-ep2](https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2) 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_caisarl76__Mistral-7B-guanaco1k-ep2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T04:08:20.324415](https://huggingface.co/datasets/open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2/blob/main/results_2023-10-25T04-08-20.324415.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": 0.002307046979865772,
"em_stderr": 0.0004913221265094507,
"f1": 0.06542994966442944,
"f1_stderr": 0.001488633695023099,
"acc": 0.4501858873976542,
"acc_stderr": 0.010287740882080417
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094507,
"f1": 0.06542994966442944,
"f1_stderr": 0.001488633695023099
},
"harness|gsm8k|5": {
"acc": 0.1197877179681577,
"acc_stderr": 0.008944213403553055
},
"harness|winogrande|5": {
"acc": 0.7805840568271507,
"acc_stderr": 0.01163126836060778
}
}
```
### 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_caisarl76__Mistral-7B-guanaco1k-ep2
|
[
"region:us"
] |
2023-10-09T14:58:17+00:00
|
{"pretty_name": "Evaluation run of caisarl76/Mistral-7B-guanaco1k-ep2", "dataset_summary": "Dataset automatically created during the evaluation run of model [caisarl76/Mistral-7B-guanaco1k-ep2](https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2) 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_caisarl76__Mistral-7B-guanaco1k-ep2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T04:08:20.324415](https://huggingface.co/datasets/open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2/blob/main/results_2023-10-25T04-08-20.324415.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\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094507,\n \"f1\": 0.06542994966442944,\n \"f1_stderr\": 0.001488633695023099,\n \"acc\": 0.4501858873976542,\n \"acc_stderr\": 0.010287740882080417\n },\n \"harness|drop|3\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094507,\n \"f1\": 0.06542994966442944,\n \"f1_stderr\": 0.001488633695023099\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1197877179681577,\n \"acc_stderr\": 0.008944213403553055\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7805840568271507,\n \"acc_stderr\": 0.01163126836060778\n }\n}\n```", "repo_url": "https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2", "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_10_09T15_57_53.203212", "path": ["**/details_harness|arc:challenge|25_2023-10-09T15-57-53.203212.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-09T15-57-53.203212.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T04_08_20.324415", "path": ["**/details_harness|drop|3_2023-10-25T04-08-20.324415.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T04-08-20.324415.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T04_08_20.324415", "path": ["**/details_harness|gsm8k|5_2023-10-25T04-08-20.324415.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T04-08-20.324415.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_09T15_57_53.203212", "path": ["**/details_harness|hellaswag|10_2023-10-09T15-57-53.203212.parquet"]}, {"split": "latest", "path": 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|
2023-10-25T03:08:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of caisarl76/Mistral-7B-guanaco1k-ep2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model caisarl76/Mistral-7B-guanaco1k-ep2 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 2023-10-25T04:08:20.324415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 caisarl76/Mistral-7B-guanaco1k-ep2",
"## 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 caisarl76/Mistral-7B-guanaco1k-ep2 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 2023-10-25T04:08:20.324415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 caisarl76/Mistral-7B-guanaco1k-ep2",
"## 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 caisarl76/Mistral-7B-guanaco1k-ep2 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 2023-10-25T04:08:20.324415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
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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 caisarl76/Mistral-7B-guanaco1k-ep2## 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 caisarl76/Mistral-7B-guanaco1k-ep2 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 2023-10-25T04:08:20.324415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
5e7de8d6c0d6dd490bd27f5f848b58f41852394c
|
Agricorp Dataset
The AutoCeres dataset comprises a collection of images captured from various sources and cultivation locations. It encompasses the following crops:
Corn
Soybean
Rice
Onion
Each crop category is associated with a set of images, and for further analysis and segmentation tasks, masks corresponding to these crops are also included. This dataset serves as a valuable resource for the development and training of computer vision algorithms in the agricultural domain.
|
Autoceres/Agricorp
|
[
"region:us"
] |
2023-10-09T15:02:14+00:00
|
{}
|
2023-10-09T15:31:40+00:00
|
[] |
[] |
TAGS
#region-us
|
Agricorp Dataset
The AutoCeres dataset comprises a collection of images captured from various sources and cultivation locations. It encompasses the following crops:
Corn
Soybean
Rice
Onion
Each crop category is associated with a set of images, and for further analysis and segmentation tasks, masks corresponding to these crops are also included. This dataset serves as a valuable resource for the development and training of computer vision algorithms in the agricultural domain.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
df6667b5a494fecdc1d20cad66bdf66f79c2d155
|
# Magic the gathering dataset
This dataset contains text of all magic the gathering cards.
Example usage:
```python
from datasets import load_dataset
dataset = load_dataset('augustoperes/mtg_text')
dataset
# outputs:
# DatasetDict({
# train: Dataset({
# features: ['card_name', 'type_line', 'oracle_text'],
# num_rows: 20063
# })
# validation: Dataset({
# features: ['card_name', 'type_line', 'oracle_text'],
# num_rows: 5016
# })
# })
```
Elements of the dataset are, for example:
```python
train_dataset = dataset['train']
train_dataset[0]
# Outputs
# {'card_name': 'Recurring Insight',
# 'type_line': 'Sorcery',
# 'oracle_text': "Draw cards equal to the number of cards in target opponent's hand.\nRebound (If you cast this spell from your hand, exile it as it resolves. At the beginning of your next upkeep, you may cast this card from exile without paying its mana cost.)"}
```
# Example usage with Pytorch
You can easily tokenize, convert and pad this dataset to be usable in pytorch with:
```python
from transformers import AutoTokenizer
import torch
from torch.nn.utils.rnn import pad_sequence
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
def tokenize(sample):
sample["card_name"] = tokenizer(sample["card_name"])["input_ids"]
sample["type_line"] = tokenizer(sample["type_line"])["input_ids"]
sample["oracle_text"] = tokenizer(sample["oracle_text"])["input_ids"]
return sample
tokenized_dataset = train_dataset.map(tokenize)
def collate_fn(sequences):
# Pad the sequences to the maximum length in the batch
card_names = [torch.tensor(sequence['card_name']) for sequence in sequences]
type_line = [torch.tensor(sequence['type_line']) for sequence in sequences]
oracle_text = [torch.tensor(sequence['oracle_text']) for sequence in sequences]
padded_card_name = pad_sequence(card_names, batch_first=True, padding_value=0)
padded_type_line = pad_sequence(type_line, batch_first=True, padding_value=0)
padded_oracle_text = pad_sequence(oracle_text, batch_first=True, padding_value=0)
return {'card_name': padded_card_name, 'type_line': padded_type_line, 'padded_oracle_text': padded_oracle_text}
loader = torch.utils.data.DataLoader(tokenized_dataset, collate_fn=collate_fn, batch_size=4)
for e in loader:
print(e)
break
# Will output:
# {'card_name': tensor([[ 101, 10694, 12369, 102, 0],
# [ 101, 3704, 9881, 102, 0],
# [ 101, 22639, 20066, 7347, 102],
# [ 101, 25697, 1997, 6019, 102]]),
# 'type_line': tensor([[ 101, 2061, 19170, 2854, 102, 0, 0],
# [ 101, 6492, 1517, 4743, 102, 0, 0],
# [ 101, 6492, 1517, 22639, 102, 0, 0],
# [ 101, 4372, 14856, 21181, 1517, 15240, 102]]),
# 'padded_oracle_text': [ommited for readability])}
```
|
augustoperes/mtg_text
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
2023-10-09T15:02:55+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"]}
|
2023-10-18T13:34:55+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #region-us
|
# Magic the gathering dataset
This dataset contains text of all magic the gathering cards.
Example usage:
Elements of the dataset are, for example:
# Example usage with Pytorch
You can easily tokenize, convert and pad this dataset to be usable in pytorch with:
|
[
"# Magic the gathering dataset\n\nThis dataset contains text of all magic the gathering cards.\nExample usage:\n\n\n\nElements of the dataset are, for example:",
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] |
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"# Example usage with Pytorch\n\nYou can easily tokenize, convert and pad this dataset to be usable in pytorch with:"
] |
[
33,
38,
31
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[
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #region-us \n# Magic the gathering dataset\n\nThis dataset contains text of all magic the gathering cards.\nExample usage:\n\n\n\nElements of the dataset are, for example:# Example usage with Pytorch\n\nYou can easily tokenize, convert and pad this dataset to be usable in pytorch with:"
] |
a120d708c7694db25dc69153bc6d7456a47fadc3
|
# Dataset Card for "apt-micro-dataset-llm-v2-714k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arpitsh018/apt-micro-dataset-llm-v2-714k
|
[
"region:us"
] |
2023-10-09T15:17:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1753434111.3731575, "num_examples": 714801}, {"name": "validation", "num_bytes": 490607.6268424799, "num_examples": 200}], "download_size": 911152910, "dataset_size": 1753924719.0}}
|
2023-10-09T15:18:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "apt-micro-dataset-llm-v2-714k"
More Information needed
|
[
"# Dataset Card for \"apt-micro-dataset-llm-v2-714k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"apt-micro-dataset-llm-v2-714k\"\n\nMore Information needed"
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[
6,
26
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"apt-micro-dataset-llm-v2-714k\"\n\nMore Information needed"
] |
bf32dfc809231e61f1b57a0a8878a5cb2bfcf2ba
|
# 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)
|
JennyZZZ/guanaco-llama2-1k
|
[
"region:us"
] |
2023-10-09T15:22:36+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"}], "splits": [{"name": "train", "num_bytes": 15401731, "num_examples": 9846}, {"name": "test", "num_bytes": 815439, "num_examples": 518}], "download_size": 0, "dataset_size": 16217170}}
|
2023-10-11T15:42:14+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"
] |
54b80ba04bebdfd6d51bba57de32196b7d510b49
|
# 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]
|
neenax/explanation_feedback
|
[
"size_categories:n<1K",
"region:us"
] |
2023-10-09T15:24:20+00:00
|
{"size_categories": ["n<1K"]}
|
2023-10-09T15:53:09+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #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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"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Dataset Curators",
"### Licensing Information",
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] |
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] |
a71b712a96bebd471be607abf8910fe7a1dfeb93
|
# Dataset Card for "eng_sur_val_full_DA_tokenized_rt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/eng_sur_val_full_DA_tokenized_rt5
|
[
"region:us"
] |
2023-10-09T15:26:25+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 26838380, "num_examples": 22390}], "download_size": 6042406, "dataset_size": 26838380}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T15:26:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eng_sur_val_full_DA_tokenized_rt5"
More Information needed
|
[
"# Dataset Card for \"eng_sur_val_full_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eng_sur_val_full_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eng_sur_val_full_DA_tokenized_rt5\"\n\nMore Information needed"
] |
7c8c0448eb82f1de703b81a9aee99a75b0437d52
|
# Dataset Card for "eng_sur_val_DA_tokenized_rt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/eng_sur_val_DA_tokenized_rt5
|
[
"region:us"
] |
2023-10-09T15:31:40+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 6022485, "num_examples": 5000}], "download_size": 1353838, "dataset_size": 6022485}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T15:31:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eng_sur_val_DA_tokenized_rt5"
More Information needed
|
[
"# Dataset Card for \"eng_sur_val_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eng_sur_val_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eng_sur_val_DA_tokenized_rt5\"\n\nMore Information needed"
] |
b9597e1e74dbf43caa7930ce1deb0fec074ce423
|
# Dataset Card for "eng_sur_DA_tokenized_rt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/eng_sur_DA_tokenized_rt5
|
[
"region:us"
] |
2023-10-09T15:35:59+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 104310930, "num_examples": 155590}], "download_size": 23898508, "dataset_size": 104310930}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T15:36:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eng_sur_DA_tokenized_rt5"
More Information needed
|
[
"# Dataset Card for \"eng_sur_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eng_sur_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eng_sur_DA_tokenized_rt5\"\n\nMore Information needed"
] |
57980d5041a0ad4a583e6dedb00ab853e1aa4178
|
# Dataset Card for "eng_DA_tokenized_rt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/eng_DA_tokenized_rt5
|
[
"region:us"
] |
2023-10-09T15:38:09+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 155868746, "num_examples": 138200}], "download_size": 50879179, "dataset_size": 155868746}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T15:38:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eng_DA_tokenized_rt5"
More Information needed
|
[
"# Dataset Card for \"eng_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eng_DA_tokenized_rt5\"\n\nMore Information needed"
] |
[
6,
20
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"eng_DA_tokenized_rt5\"\n\nMore Information needed"
] |
a02862cf2af4cb46a3384e27e583b99a73650d16
|
# Dataset Card for "bill_text_us"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [BillML](https://github.com/dreamproit/BillML)
- **Repository:** [BillML](https://github.com/dreamproit/BillML)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Dataset for US Congressional bills (bill_text_us).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English
## Dataset Structure
### Data Instances
#### default
### Data Fields
- id: id of the bill in format(congress number + bill type + bill number + bill version).
- congress: number of the congress.
- bill_type: type of the bill.
- bill_number: number of the bill.
- bill_version: version of the bill.
- title: official title of the bill.
- sections: list of bill sections with section_id, text and header.
- sections_length: number with lenght of the sections list.
- text: bill text.
- text_length: number of characters in the text.
### Data Splits
train
## Dataset Creation
### Curation Rationale
Bills (proposed laws) are specialized, structured documents with great public significance.
Often, the language of a bill may not directly explain the potential impact of the legislation.
This dataset collects the text of bills and some metadata.
As a result, this dataset collects bill text; it also provides text as a list of sections with the text and header.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
[govinfo.gov](https://www.govinfo.gov/)
#### Initial Data Collection and Normalization
The data consists of the US congress bills that were collected from the [govinfo.gov](https://www.govinfo.gov/) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 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
[dreamproit.com](https://dreamproit.com/)
### Licensing Information
Bill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@aih](https://github.com/aih) [@BorodaUA](https://github.com/BorodaUA), [@alexbojko](https://github.com/alexbojko) for adding this dataset.
|
dreamproit/bill_text_us
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"legal",
"bills",
"region:us"
] |
2023-10-09T16:02:16+00:00
|
{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text-classification"], "pretty_name": "bill_text_us", "tags": ["legal", "bills"]}
|
2023-10-16T11:06:51+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-text-classification #size_categories-100K<n<1M #language-English #legal #bills #region-us
|
# Dataset Card for "bill_text_us"
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: BillML
- Repository: BillML
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
Dataset for US Congressional bills (bill_text_us).
### Supported Tasks and Leaderboards
### Languages
English
## Dataset Structure
### Data Instances
#### default
### Data Fields
- id: id of the bill in format(congress number + bill type + bill number + bill version).
- congress: number of the congress.
- bill_type: type of the bill.
- bill_number: number of the bill.
- bill_version: version of the bill.
- title: official title of the bill.
- sections: list of bill sections with section_id, text and header.
- sections_length: number with lenght of the sections list.
- text: bill text.
- text_length: number of characters in the text.
### Data Splits
train
## Dataset Creation
### Curation Rationale
Bills (proposed laws) are specialized, structured documents with great public significance.
Often, the language of a bill may not directly explain the potential impact of the legislation.
This dataset collects the text of bills and some metadata.
As a result, this dataset collects bill text; it also provides text as a list of sections with the text and header.
### Source Data
URL
#### Initial Data Collection and Normalization
The data consists of the US congress bills that were collected from the URL service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
URL
### Licensing Information
Bill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under CC0.
### Contributions
Thanks to @aih @BorodaUA, @alexbojko for adding this dataset.
|
[
"# Dataset Card for \"bill_text_us\"",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: BillML\n- Repository: BillML\n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nDataset for US Congressional bills (bill_text_us).",
"### Supported Tasks and Leaderboards",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances",
"#### default",
"### Data Fields\n\n- id: id of the bill in format(congress number + bill type + bill number + bill version).\n- congress: number of the congress.\n- bill_type: type of the bill.\n- bill_number: number of the bill.\n- bill_version: version of the bill.\n- title: official title of the bill.\n- sections: list of bill sections with section_id, text and header.\n- sections_length: number with lenght of the sections list.\n- text: bill text.\n- text_length: number of characters in the text.",
"### Data Splits\n\ntrain",
"## Dataset Creation",
"### Curation Rationale\n\nBills (proposed laws) are specialized, structured documents with great public significance.\nOften, the language of a bill may not directly explain the potential impact of the legislation.\nThis dataset collects the text of bills and some metadata.\nAs a result, this dataset collects bill text; it also provides text as a list of sections with the text and header.",
"### Source Data\n\nURL",
"#### Initial Data Collection and Normalization\n\nThe data consists of the US congress bills that were collected from the URL service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators\n\nURL",
"### Licensing Information\n\nBill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under CC0.",
"### Contributions\n\nThanks to @aih @BorodaUA, @alexbojko for adding this dataset."
] |
[
"TAGS\n#task_categories-text-generation #task_categories-text-classification #size_categories-100K<n<1M #language-English #legal #bills #region-us \n",
"# Dataset Card for \"bill_text_us\"",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: BillML\n- Repository: BillML\n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nDataset for US Congressional bills (bill_text_us).",
"### Supported Tasks and Leaderboards",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances",
"#### default",
"### Data Fields\n\n- id: id of the bill in format(congress number + bill type + bill number + bill version).\n- congress: number of the congress.\n- bill_type: type of the bill.\n- bill_number: number of the bill.\n- bill_version: version of the bill.\n- title: official title of the bill.\n- sections: list of bill sections with section_id, text and header.\n- sections_length: number with lenght of the sections list.\n- text: bill text.\n- text_length: number of characters in the text.",
"### Data Splits\n\ntrain",
"## Dataset Creation",
"### Curation Rationale\n\nBills (proposed laws) are specialized, structured documents with great public significance.\nOften, the language of a bill may not directly explain the potential impact of the legislation.\nThis dataset collects the text of bills and some metadata.\nAs a result, this dataset collects bill text; it also provides text as a list of sections with the text and header.",
"### Source Data\n\nURL",
"#### Initial Data Collection and Normalization\n\nThe data consists of the US congress bills that were collected from the URL service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators\n\nURL",
"### Licensing Information\n\nBill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under CC0.",
"### Contributions\n\nThanks to @aih @BorodaUA, @alexbojko for adding this dataset."
] |
[
49,
13,
125,
28,
22,
10,
5,
6,
6,
3,
133,
6,
5,
91,
5,
48,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
7,
59,
26
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-text-classification #size_categories-100K<n<1M #language-English #legal #bills #region-us \n# Dataset Card for \"bill_text_us\"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: BillML\n- Repository: BillML\n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nDataset for US Congressional bills (bill_text_us).### Supported Tasks and Leaderboards### Languages\n\nEnglish## Dataset Structure### Data Instances#### default### Data Fields\n\n- id: id of the bill in format(congress number + bill type + bill number + bill version).\n- congress: number of the congress.\n- bill_type: type of the bill.\n- bill_number: number of the bill.\n- bill_version: version of the bill.\n- title: official title of the bill.\n- sections: list of bill sections with section_id, text and header.\n- sections_length: number with lenght of the sections list.\n- text: bill text.\n- text_length: number of characters in the text.### Data Splits\n\ntrain## Dataset Creation### Curation Rationale\n\nBills (proposed laws) are specialized, structured documents with great public significance.\nOften, the language of a bill may not directly explain the potential impact of the legislation.\nThis dataset collects the text of bills and some metadata.\nAs a result, this dataset collects bill text; it also provides text as a list of sections with the text and header.### Source Data\n\nURL"
] |
f4f7826ac194a1c53b53807c58c51ac5cbabf3fc
|
# The Autocast Initiative
This dataset archives podcasts in real-time. Podcasts that indentify with the principle of autocasting as their method for sharing audiofiles with an audience of subsribers.
All contributers are volonteers.
## The Principles Autocasting
* The content is primarily not created.
* Neither the files or the RSS feed is not manipulated after publish, other than to correct mistakes.
* * The "episode description" is the exception to the above. Use this field however you please.
* No method is to be considered "too low-effort" when it comes to generating audiofiles.
* For content protected by monetization is encouraged to commit scrambled content and provide means for unscrambling as they see fit.
* * Further monetization is encouraged.
* Get paid if you can.
## How to contribute
Create a folder for your autocast as so:
```
/archive/[Name of your feed]/
```
Do not substitute special characters (if possible)
In this folder, include your episodes as well as snapshots of your RSS feed at the time of publish (if possible)
```
/archive/[Name of your feed]/[001].mp3 // or whichever format you use
/archive/[Name of your feed]/[001].xml
/archive/[Name of your feed]/[002].mp3 // ...
/archive/[Name of your feed]/[002].xml
...
/archive/[Name of your feed]/[00n].mp3 // ...
/archive/[Name of your feed]/[00n].xml
...
```
If you intend to publish more than 1000 episodes in a single feed, figure it out (responsibly)
|
peter-h-o-r-v/autocast-initiative
|
[
"license:artistic-2.0",
"art",
"sound",
"podcast",
"podcasting",
"region:us"
] |
2023-10-09T16:45:03+00:00
|
{"license": "artistic-2.0", "pretty_name": "The Autocast Initiative", "tags": ["art", "sound", "podcast", "podcasting"]}
|
2023-10-09T17:31:55+00:00
|
[] |
[] |
TAGS
#license-artistic-2.0 #art #sound #podcast #podcasting #region-us
|
# The Autocast Initiative
This dataset archives podcasts in real-time. Podcasts that indentify with the principle of autocasting as their method for sharing audiofiles with an audience of subsribers.
All contributers are volonteers.
## The Principles Autocasting
* The content is primarily not created.
* Neither the files or the RSS feed is not manipulated after publish, other than to correct mistakes.
* * The "episode description" is the exception to the above. Use this field however you please.
* No method is to be considered "too low-effort" when it comes to generating audiofiles.
* For content protected by monetization is encouraged to commit scrambled content and provide means for unscrambling as they see fit.
* * Further monetization is encouraged.
* Get paid if you can.
## How to contribute
Create a folder for your autocast as so:
Do not substitute special characters (if possible)
In this folder, include your episodes as well as snapshots of your RSS feed at the time of publish (if possible)
If you intend to publish more than 1000 episodes in a single feed, figure it out (responsibly)
|
[
"# The Autocast Initiative\nThis dataset archives podcasts in real-time. Podcasts that indentify with the principle of autocasting as their method for sharing audiofiles with an audience of subsribers.\n\nAll contributers are volonteers.",
"## The Principles Autocasting\n\n* The content is primarily not created.\n\n* Neither the files or the RSS feed is not manipulated after publish, other than to correct mistakes.\n* * The \"episode description\" is the exception to the above. Use this field however you please.\n\n* No method is to be considered \"too low-effort\" when it comes to generating audiofiles.\n\n* For content protected by monetization is encouraged to commit scrambled content and provide means for unscrambling as they see fit.\n* * Further monetization is encouraged.\n \n* Get paid if you can.",
"## How to contribute\n\nCreate a folder for your autocast as so: \n\n\nDo not substitute special characters (if possible)\n\nIn this folder, include your episodes as well as snapshots of your RSS feed at the time of publish (if possible)\n\n\nIf you intend to publish more than 1000 episodes in a single feed, figure it out (responsibly)"
] |
[
"TAGS\n#license-artistic-2.0 #art #sound #podcast #podcasting #region-us \n",
"# The Autocast Initiative\nThis dataset archives podcasts in real-time. Podcasts that indentify with the principle of autocasting as their method for sharing audiofiles with an audience of subsribers.\n\nAll contributers are volonteers.",
"## The Principles Autocasting\n\n* The content is primarily not created.\n\n* Neither the files or the RSS feed is not manipulated after publish, other than to correct mistakes.\n* * The \"episode description\" is the exception to the above. Use this field however you please.\n\n* No method is to be considered \"too low-effort\" when it comes to generating audiofiles.\n\n* For content protected by monetization is encouraged to commit scrambled content and provide means for unscrambling as they see fit.\n* * Further monetization is encouraged.\n \n* Get paid if you can.",
"## How to contribute\n\nCreate a folder for your autocast as so: \n\n\nDo not substitute special characters (if possible)\n\nIn this folder, include your episodes as well as snapshots of your RSS feed at the time of publish (if possible)\n\n\nIf you intend to publish more than 1000 episodes in a single feed, figure it out (responsibly)"
] |
[
25,
56,
134,
74
] |
[
"passage: TAGS\n#license-artistic-2.0 #art #sound #podcast #podcasting #region-us \n# The Autocast Initiative\nThis dataset archives podcasts in real-time. Podcasts that indentify with the principle of autocasting as their method for sharing audiofiles with an audience of subsribers.\n\nAll contributers are volonteers.## The Principles Autocasting\n\n* The content is primarily not created.\n\n* Neither the files or the RSS feed is not manipulated after publish, other than to correct mistakes.\n* * The \"episode description\" is the exception to the above. Use this field however you please.\n\n* No method is to be considered \"too low-effort\" when it comes to generating audiofiles.\n\n* For content protected by monetization is encouraged to commit scrambled content and provide means for unscrambling as they see fit.\n* * Further monetization is encouraged.\n \n* Get paid if you can.## How to contribute\n\nCreate a folder for your autocast as so: \n\n\nDo not substitute special characters (if possible)\n\nIn this folder, include your episodes as well as snapshots of your RSS feed at the time of publish (if possible)\n\n\nIf you intend to publish more than 1000 episodes in a single feed, figure it out (responsibly)"
] |
32b68e2169774e114d648a0344d8b35b820c1397
|
# Dataset Card for "patent_v2_merged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenthanhdo/patent_v2_merged
|
[
"region:us"
] |
2023-10-09T16:53:28+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 118735189, "num_examples": 100488}], "download_size": 66085340, "dataset_size": 118735189}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T16:53:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "patent_v2_merged"
More Information needed
|
[
"# Dataset Card for \"patent_v2_merged\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"patent_v2_merged\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"patent_v2_merged\"\n\nMore Information needed"
] |
e204c103f101d9c44a3a9767288e46c5b586bfdf
|
# Dataset Card for "lmsys-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amphora/lmsys-filtered
|
[
"region:us"
] |
2023-10-09T16:55:20+00:00
|
{"dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "dtype": "string"}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "dtype": "string"}, {"name": "redacted", "dtype": "bool"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 317822351, "num_examples": 62968}], "download_size": 122101594, "dataset_size": 317822351}}
|
2023-10-09T16:57:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "lmsys-filtered"
More Information needed
|
[
"# Dataset Card for \"lmsys-filtered\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"lmsys-filtered\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"lmsys-filtered\"\n\nMore Information needed"
] |
7d25a37a69983a2ba17b6489cdf89511b31d683a
|
<div style='background: #ffeef1; border: 1px solid #fd91a4; padding:1em; border-radius:3px; margin-bottom:2em;'>
<h3 style='margin:0'>NSFW</h3>
<p style='margin:0'>This dataset is not suitable for use by minors. The dataset contains X-rated/NFSW content.</p>
</div>
# E621 Rising V3: Curated Image Dataset
* **279,296** images (53GB) downloaded from `e621.net` (90% of samples), `gelbooru.com`, `danbooru.com`, and `rule34.xxx`
* **6,820** [tags](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data/blob/main/tag-counts.by-name.json)
* Used to train [E621 Rising v3](https://huggingface.co/hearmeneigh/e621-rising-v3) SDXL model
This dataset was created with [Dataset Rising](https://github.com/hearmeneigh/dataset-rising) toolchain and a [custom configuration](https://github.com/hearmeneigh/e621-rising-configs).
You can use these tools to train your own version!
## Image Processing
* Only `jpg` and `png` images were considered
* Image width and height have been clamped to `(0, 1024]px`; larger images have been resized to meet the limit
* Alpha channels have been removed
* All images have been converted to `jpg` format
* All images have been converted to TrueColor `RGB`
* All images have been verified to load with `Pillow`
* Metadata from E621 is [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data)
## Tags
Comprehensive list of 6,820 tags and counts:
* [By name](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data/blob/main/tag-counts.by-name.json)
* [By count](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data/blob/main/tag-counts.by-count.json)
### Additional Tags
* `rating_explicit`
* `rating_questionable`
* `rating_safe`
* `rising_masterpiece`
* `rising_unpopular`
* `favorites_below_X` (25, 50, 100, 250, 500, 1000)
* `favorites_above_X` (250, 500, 1000, 2000, 3000, 4000)
* `score_below_X` (0, 25, 50, 100, 250, 500)
* `score_above_X` (100, 250, 500, 1000, 1500, 2000)
|
hearmeneigh/e621-rising-v3-curated
|
[
"size_categories:100K<n<1M",
"furry",
"anthro",
"nsfw",
"e621",
"booru",
"imagebooru",
"imageboard",
"gelbooru",
"danbooru",
"rule34",
"not-for-all-audiences",
"region:us"
] |
2023-10-09T17:03:16+00:00
|
{"size_categories": ["100K<n<1M"], "pretty_name": "E621 Rising V3 Image Dataset", "dataset_info": {"features": [{"name": "source_id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "tags", "sequence": "string"}, {"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "selector", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53726659168.0, "num_examples": 279296}], "download_size": 53423627875, "dataset_size": 53726659168.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["furry", "anthro", "nsfw", "e621", "booru", "imagebooru", "imageboard", "gelbooru", "danbooru", "rule34", "not-for-all-audiences"]}
|
2023-10-24T18:36:28+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #furry #anthro #nsfw #e621 #booru #imagebooru #imageboard #gelbooru #danbooru #rule34 #not-for-all-audiences #region-us
|
<div style='background: #ffeef1; border: 1px solid #fd91a4; padding:1em; border-radius:3px; margin-bottom:2em;'>
<h3 style='margin:0'>NSFW</h3>
<p style='margin:0'>This dataset is not suitable for use by minors. The dataset contains X-rated/NFSW content.</p>
</div>
# E621 Rising V3: Curated Image Dataset
* 279,296 images (53GB) downloaded from 'URL' (90% of samples), 'URL', 'URL', and 'URL'
* 6,820 tags
* Used to train E621 Rising v3 SDXL model
This dataset was created with Dataset Rising toolchain and a custom configuration.
You can use these tools to train your own version!
## Image Processing
* Only 'jpg' and 'png' images were considered
* Image width and height have been clamped to '(0, 1024]px'; larger images have been resized to meet the limit
* Alpha channels have been removed
* All images have been converted to 'jpg' format
* All images have been converted to TrueColor 'RGB'
* All images have been verified to load with 'Pillow'
* Metadata from E621 is available here
## Tags
Comprehensive list of 6,820 tags and counts:
* By name
* By count
### Additional Tags
* 'rating_explicit'
* 'rating_questionable'
* 'rating_safe'
* 'rising_masterpiece'
* 'rising_unpopular'
* 'favorites_below_X' (25, 50, 100, 250, 500, 1000)
* 'favorites_above_X' (250, 500, 1000, 2000, 3000, 4000)
* 'score_below_X' (0, 25, 50, 100, 250, 500)
* 'score_above_X' (100, 250, 500, 1000, 1500, 2000)
|
[
"# E621 Rising V3: Curated Image Dataset\n\n* 279,296 images (53GB) downloaded from 'URL' (90% of samples), 'URL', 'URL', and 'URL'\n* 6,820 tags\n* Used to train E621 Rising v3 SDXL model\n\nThis dataset was created with Dataset Rising toolchain and a custom configuration.\nYou can use these tools to train your own version!",
"## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 1024]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here",
"## Tags\nComprehensive list of 6,820 tags and counts:\n\n* By name\n* By count",
"### Additional Tags\n* 'rating_explicit'\n* 'rating_questionable'\n* 'rating_safe'\n* 'rising_masterpiece'\n* 'rising_unpopular'\n* 'favorites_below_X' (25, 50, 100, 250, 500, 1000)\n* 'favorites_above_X' (250, 500, 1000, 2000, 3000, 4000)\n* 'score_below_X' (0, 25, 50, 100, 250, 500)\n* 'score_above_X' (100, 250, 500, 1000, 1500, 2000)"
] |
[
"TAGS\n#size_categories-100K<n<1M #furry #anthro #nsfw #e621 #booru #imagebooru #imageboard #gelbooru #danbooru #rule34 #not-for-all-audiences #region-us \n",
"# E621 Rising V3: Curated Image Dataset\n\n* 279,296 images (53GB) downloaded from 'URL' (90% of samples), 'URL', 'URL', and 'URL'\n* 6,820 tags\n* Used to train E621 Rising v3 SDXL model\n\nThis dataset was created with Dataset Rising toolchain and a custom configuration.\nYou can use these tools to train your own version!",
"## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 1024]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here",
"## Tags\nComprehensive list of 6,820 tags and counts:\n\n* By name\n* By count",
"### Additional Tags\n* 'rating_explicit'\n* 'rating_questionable'\n* 'rating_safe'\n* 'rising_masterpiece'\n* 'rising_unpopular'\n* 'favorites_below_X' (25, 50, 100, 250, 500, 1000)\n* 'favorites_above_X' (250, 500, 1000, 2000, 3000, 4000)\n* 'score_below_X' (0, 25, 50, 100, 250, 500)\n* 'score_above_X' (100, 250, 500, 1000, 1500, 2000)"
] |
[
63,
96,
105,
21,
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[
"passage: TAGS\n#size_categories-100K<n<1M #furry #anthro #nsfw #e621 #booru #imagebooru #imageboard #gelbooru #danbooru #rule34 #not-for-all-audiences #region-us \n# E621 Rising V3: Curated Image Dataset\n\n* 279,296 images (53GB) downloaded from 'URL' (90% of samples), 'URL', 'URL', and 'URL'\n* 6,820 tags\n* Used to train E621 Rising v3 SDXL model\n\nThis dataset was created with Dataset Rising toolchain and a custom configuration.\nYou can use these tools to train your own version!## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 1024]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here## Tags\nComprehensive list of 6,820 tags and counts:\n\n* By name\n* By count### Additional Tags\n* 'rating_explicit'\n* 'rating_questionable'\n* 'rating_safe'\n* 'rising_masterpiece'\n* 'rising_unpopular'\n* 'favorites_below_X' (25, 50, 100, 250, 500, 1000)\n* 'favorites_above_X' (250, 500, 1000, 2000, 3000, 4000)\n* 'score_below_X' (0, 25, 50, 100, 250, 500)\n* 'score_above_X' (100, 250, 500, 1000, 1500, 2000)"
] |
31d316286ac6cec5024d781dc697515c1e565ad7
|
# Dataset Card for "rule_gen_splunk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/rule_gen_splunk
|
[
"region:us"
] |
2023-10-09T17:42:26+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "null"}, {"name": "rule", "dtype": "null"}, {"name": "software", "dtype": "null"}, {"name": "configuration", "dtype": "null"}, {"name": "description", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 0}], "download_size": 1376, "dataset_size": 0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T17:43:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rule_gen_splunk"
More Information needed
|
[
"# Dataset Card for \"rule_gen_splunk\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rule_gen_splunk\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rule_gen_splunk\"\n\nMore Information needed"
] |
3f977dc26b4cbad9af2a698658eb28928837fe0f
|
# Dataset Card for Evaluation run of v2ray/LLaMA-2-Jannie-70B-QLoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA
- **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 [v2ray/LLaMA-2-Jannie-70B-QLoRA](https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA) 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 1 run(s). Each run can be found as a specific 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_v2ray__LLaMA-2-Jannie-70B-QLoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-09T18:55:45.725131](https://huggingface.co/datasets/open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA/blob/main/results_2023-10-09T18-55-45.725131.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": 0.5506501677852349,
"em_stderr": 0.0050941277409732805,
"f1": 0.5974674916107394,
"f1_stderr": 0.004813528422862131,
"acc": 0.5735917227001633,
"acc_stderr": 0.011696543872157381
},
"harness|drop|3": {
"em": 0.5506501677852349,
"em_stderr": 0.0050941277409732805,
"f1": 0.5974674916107394,
"f1_stderr": 0.004813528422862131
},
"harness|gsm8k|5": {
"acc": 0.31766489764973466,
"acc_stderr": 0.012824066621488854
},
"harness|winogrande|5": {
"acc": 0.829518547750592,
"acc_stderr": 0.010569021122825909
}
}
```
### 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_v2ray__LLaMA-2-Jannie-70B-QLoRA
|
[
"region:us"
] |
2023-10-09T17:55:49+00:00
|
{"pretty_name": "Evaluation run of v2ray/LLaMA-2-Jannie-70B-QLoRA", "dataset_summary": "Dataset automatically created during the evaluation run of model [v2ray/LLaMA-2-Jannie-70B-QLoRA](https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA) 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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to 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_v2ray__LLaMA-2-Jannie-70B-QLoRA\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-09T18:55:45.725131](https://huggingface.co/datasets/open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA/blob/main/results_2023-10-09T18-55-45.725131.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\": 0.5506501677852349,\n \"em_stderr\": 0.0050941277409732805,\n \"f1\": 0.5974674916107394,\n \"f1_stderr\": 0.004813528422862131,\n \"acc\": 0.5735917227001633,\n \"acc_stderr\": 0.011696543872157381\n },\n \"harness|drop|3\": {\n \"em\": 0.5506501677852349,\n \"em_stderr\": 0.0050941277409732805,\n \"f1\": 0.5974674916107394,\n \"f1_stderr\": 0.004813528422862131\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.31766489764973466,\n \"acc_stderr\": 0.012824066621488854\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825909\n }\n}\n```", "repo_url": "https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA", "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_10_09T18_55_45.725131", "path": ["**/details_harness|drop|3_2023-10-09T18-55-45.725131.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-09T18-55-45.725131.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_09T18_55_45.725131", "path": ["**/details_harness|gsm8k|5_2023-10-09T18-55-45.725131.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-09T18-55-45.725131.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_09T18_55_45.725131", "path": ["**/details_harness|winogrande|5_2023-10-09T18-55-45.725131.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-09T18-55-45.725131.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_09T18_55_45.725131", "path": ["results_2023-10-09T18-55-45.725131.parquet"]}, {"split": "latest", "path": ["results_2023-10-09T18-55-45.725131.parquet"]}]}]}
|
2023-10-09T17:55:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of v2ray/LLaMA-2-Jannie-70B-QLoRA
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model v2ray/LLaMA-2-Jannie-70B-QLoRA 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 1 run(s). Each run can be found as a specific 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 2023-10-09T18:55:45.725131(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 v2ray/LLaMA-2-Jannie-70B-QLoRA",
"## 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 v2ray/LLaMA-2-Jannie-70B-QLoRA 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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to 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 2023-10-09T18:55:45.725131(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 v2ray/LLaMA-2-Jannie-70B-QLoRA",
"## 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 v2ray/LLaMA-2-Jannie-70B-QLoRA 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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to 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 2023-10-09T18:55:45.725131(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find 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",
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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 v2ray/LLaMA-2-Jannie-70B-QLoRA## 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 v2ray/LLaMA-2-Jannie-70B-QLoRA 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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to 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 2023-10-09T18:55:45.725131(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
3af27f5171fcca171859832ec1890bd15abc9e97
|
# Dataset Card for "dpt-testing-version-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChirathD/dpt-testing-version-1
|
[
"region:us"
] |
2023-10-09T18:00:43+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3135193.0, "num_examples": 5}], "download_size": 3136751, "dataset_size": 3135193.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T18:00:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dpt-testing-version-1"
More Information needed
|
[
"# Dataset Card for \"dpt-testing-version-1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dpt-testing-version-1\"\n\nMore Information needed"
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[
6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"dpt-testing-version-1\"\n\nMore Information needed"
] |
80de752c1df4355e3a6bb13a80dbfb3822e217d1
|
# Dataset Card for Evaluation run of bhenrym14/mistral-7b-platypus-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bhenrym14/mistral-7b-platypus-fp16
- **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 [bhenrym14/mistral-7b-platypus-fp16](https://huggingface.co/bhenrym14/mistral-7b-platypus-fp16) 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_bhenrym14__mistral-7b-platypus-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T09:15:23.830857](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__mistral-7b-platypus-fp16/blob/main/results_2023-10-29T09-15-23.830857.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": 0.4168414429530201,
"em_stderr": 0.005049151744527279,
"f1": 0.4591768036912757,
"f1_stderr": 0.0048851694906548275,
"acc": 0.479468014382712,
"acc_stderr": 0.010986687977801515
},
"harness|drop|3": {
"em": 0.4168414429530201,
"em_stderr": 0.005049151744527279,
"f1": 0.4591768036912757,
"f1_stderr": 0.0048851694906548275
},
"harness|gsm8k|5": {
"acc": 0.17361637604245642,
"acc_stderr": 0.010433463221257632
},
"harness|winogrande|5": {
"acc": 0.7853196527229677,
"acc_stderr": 0.011539912734345398
}
}
```
### 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?
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### Personal and Sensitive Information
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## Considerations for Using the Data
### Social Impact of Dataset
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### Discussion of Biases
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### Other Known Limitations
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## Additional Information
### Dataset Curators
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### Contributions
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|
open-llm-leaderboard/details_bhenrym14__mistral-7b-platypus-fp16
|
[
"region:us"
] |
2023-10-09T18:22:37+00:00
|
{"pretty_name": "Evaluation run of bhenrym14/mistral-7b-platypus-fp16", "dataset_summary": "Dataset automatically created during the evaluation run of model [bhenrym14/mistral-7b-platypus-fp16](https://huggingface.co/bhenrym14/mistral-7b-platypus-fp16) 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_bhenrym14__mistral-7b-platypus-fp16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T09:15:23.830857](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__mistral-7b-platypus-fp16/blob/main/results_2023-10-29T09-15-23.830857.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\": 0.4168414429530201,\n \"em_stderr\": 0.005049151744527279,\n \"f1\": 0.4591768036912757,\n \"f1_stderr\": 0.0048851694906548275,\n \"acc\": 0.479468014382712,\n \"acc_stderr\": 0.010986687977801515\n },\n \"harness|drop|3\": {\n \"em\": 0.4168414429530201,\n \"em_stderr\": 0.005049151744527279,\n \"f1\": 0.4591768036912757,\n \"f1_stderr\": 0.0048851694906548275\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17361637604245642,\n \"acc_stderr\": 0.010433463221257632\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7853196527229677,\n \"acc_stderr\": 0.011539912734345398\n }\n}\n```", "repo_url": "https://huggingface.co/bhenrym14/mistral-7b-platypus-fp16", "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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2023-10-29T09:15:36+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of bhenrym14/mistral-7b-platypus-fp16
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model bhenrym14/mistral-7b-platypus-fp16 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 2023-10-29T09:15:23.830857(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 bhenrym14/mistral-7b-platypus-fp16",
"## 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 bhenrym14/mistral-7b-platypus-fp16 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 2023-10-29T09:15:23.830857(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
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"#### 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"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of bhenrym14/mistral-7b-platypus-fp16",
"## 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 bhenrym14/mistral-7b-platypus-fp16 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 2023-10-29T09:15:23.830857(note that their might be results for other tasks in the repos if successive 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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"## 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 bhenrym14/mistral-7b-platypus-fp16## 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 bhenrym14/mistral-7b-platypus-fp16 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 2023-10-29T09:15:23.830857(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
b2c505180d70554431812940f9a5a6d7dacc1564
|
# Dataset Card for "oa_lima_strat_qcm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ArmelRandy/oa_lima_strat_qcm
|
[
"region:us"
] |
2023-10-09T19:43:32+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24587836.604066804, "num_examples": 18828}, {"name": "test", "num_bytes": 1294165.3959331955, "num_examples": 991}], "download_size": 16177809, "dataset_size": 25882002.0}}
|
2023-10-09T19:43:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "oa_lima_strat_qcm"
More Information needed
|
[
"# Dataset Card for \"oa_lima_strat_qcm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"oa_lima_strat_qcm\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"oa_lima_strat_qcm\"\n\nMore Information needed"
] |
a12dd1c4b82c2ed2e123f72e274de85363f5f126
|
# Dataset Card for "cot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/cot
|
[
"region:us"
] |
2023-10-09T20:04:29+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 115613738, "num_examples": 100000}], "download_size": 52113324, "dataset_size": 115613738}}
|
2023-10-09T20:04:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cot"
More Information needed
|
[
"# Dataset Card for \"cot\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cot\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cot\"\n\nMore Information needed"
] |
cdee24c808ba2692486975cc1f1222f3fa370290
|
# Dataset Card for "dolly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/dolly
|
[
"region:us"
] |
2023-10-09T20:04:34+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 13007120, "num_examples": 15011}], "download_size": 7493126, "dataset_size": 13007120}}
|
2023-10-09T20:04:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dolly"
More Information needed
|
[
"# Dataset Card for \"dolly\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dolly\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dolly\"\n\nMore Information needed"
] |
a6334568fd9b203ea1210bdf73c5d80c943db6e2
|
# Dataset Card for "oasst1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/oasst1
|
[
"region:us"
] |
2023-10-09T20:04:37+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 51422776, "num_examples": 33919}], "download_size": 20867411, "dataset_size": 51422776}}
|
2023-10-09T20:04:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "oasst1"
More Information needed
|
[
"# Dataset Card for \"oasst1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"oasst1\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"oasst1\"\n\nMore Information needed"
] |
682fd6a7730226d419cc5b223761219fda4c02d6
|
# Dataset Card for "sharegpt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/sharegpt
|
[
"region:us"
] |
2023-10-09T20:04:41+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 815707764, "num_examples": 168864}], "download_size": 347091152, "dataset_size": 815707764}}
|
2023-10-09T20:04:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sharegpt"
More Information needed
|
[
"# Dataset Card for \"sharegpt\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sharegpt\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sharegpt\"\n\nMore Information needed"
] |
7a7860f5f59cf3c15c50a14400798113b1e6ce89
|
# Dataset Card for "stanford_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/stanford_alpaca
|
[
"region:us"
] |
2023-10-09T20:04:58+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 23769688, "num_examples": 52002}], "download_size": 12254044, "dataset_size": 23769688}}
|
2023-10-09T20:05:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "stanford_alpaca"
More Information needed
|
[
"# Dataset Card for \"stanford_alpaca\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"stanford_alpaca\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"stanford_alpaca\"\n\nMore Information needed"
] |
269679cb6236d8f3e7c354bf47a090d1d07f8096
|
# Dataset Card for "self_instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/self_instruct
|
[
"region:us"
] |
2023-10-09T20:05:00+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 27516583, "num_examples": 82439}], "download_size": 11204230, "dataset_size": 27516583}}
|
2023-10-09T20:05:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "self_instruct"
More Information needed
|
[
"# Dataset Card for \"self_instruct\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"self_instruct\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"self_instruct\"\n\nMore Information needed"
] |
b38c1455a31bbd454a5b549b9c76b813da94d80e
|
# Dataset Card for "code_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/code_alpaca
|
[
"region:us"
] |
2023-10-09T20:05:07+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 7830075, "num_examples": 20022}], "download_size": 3538209, "dataset_size": 7830075}}
|
2023-10-09T20:05:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "code_alpaca"
More Information needed
|
[
"# Dataset Card for \"code_alpaca\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"code_alpaca\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"code_alpaca\"\n\nMore Information needed"
] |
1a8a613643062cf5616d1f57266f694285032977
|
# Dataset Card for "english_AAAI_Math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
YaHi/english_AAAI_Math
|
[
"region:us"
] |
2023-10-09T20:06:26+00:00
|
{"dataset_info": {"features": [{"name": "dataset_version", "dtype": "timestamp[s]"}, {"name": "queId", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "qtype", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "knowledge_point_routes", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2228695, "num_examples": 5927}], "download_size": 854269, "dataset_size": 2228695}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T20:06:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "english_AAAI_Math"
More Information needed
|
[
"# Dataset Card for \"english_AAAI_Math\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"english_AAAI_Math\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"english_AAAI_Math\"\n\nMore Information needed"
] |
fb962bcf02a2c206e805239b5c58457caca0c340
|
# Dataset Card for "chinese_AAAI_Math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
YaHi/chinese_AAAI_Math
|
[
"region:us"
] |
2023-10-09T20:06:27+00:00
|
{"dataset_info": {"features": [{"name": "dataset_version", "dtype": "timestamp[s]"}, {"name": "queId", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "qtype", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "knowledge_point_routes", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2911523, "num_examples": 7436}], "download_size": 1485592, "dataset_size": 2911523}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T20:06:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "chinese_AAAI_Math"
More Information needed
|
[
"# Dataset Card for \"chinese_AAAI_Math\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"chinese_AAAI_Math\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"chinese_AAAI_Math\"\n\nMore Information needed"
] |
1221e711c13a725b52d87a2762110048e121f8e8
|
# AI ArXiv Dataset
The AI ArXiv dataset contains a selection of papers on the topics of AI and LLMs.
You can find a heavily upgraded [v2 dataset here](https://huggingface.co/datasets/aurelio-ai/ai-arxiv2). The v2 dataset improves both data quality and dataset size.
|
jamescalam/ai-arxiv
|
[
"region:us"
] |
2023-10-09T20:07:32+00:00
|
{}
|
2024-01-29T11:16:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# AI ArXiv Dataset
The AI ArXiv dataset contains a selection of papers on the topics of AI and LLMs.
You can find a heavily upgraded v2 dataset here. The v2 dataset improves both data quality and dataset size.
|
[
"# AI ArXiv Dataset\n\nThe AI ArXiv dataset contains a selection of papers on the topics of AI and LLMs.\n\nYou can find a heavily upgraded v2 dataset here. The v2 dataset improves both data quality and dataset size."
] |
[
"TAGS\n#region-us \n",
"# AI ArXiv Dataset\n\nThe AI ArXiv dataset contains a selection of papers on the topics of AI and LLMs.\n\nYou can find a heavily upgraded v2 dataset here. The v2 dataset improves both data quality and dataset size."
] |
[
6,
62
] |
[
"passage: TAGS\n#region-us \n# AI ArXiv Dataset\n\nThe AI ArXiv dataset contains a selection of papers on the topics of AI and LLMs.\n\nYou can find a heavily upgraded v2 dataset here. The v2 dataset improves both data quality and dataset size."
] |
9b057f3558e8003ab48bf674b403d3feb7233f43
|
# Stanford Sentiment Treebank - Binary
|
yangwang825/sst2-textbugger
|
[
"region:us"
] |
2023-10-09T20:08:44+00:00
|
{}
|
2023-10-09T21:09:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Stanford Sentiment Treebank - Binary
|
[
"# Stanford Sentiment Treebank - Binary"
] |
[
"TAGS\n#region-us \n",
"# Stanford Sentiment Treebank - Binary"
] |
[
6,
9
] |
[
"passage: TAGS\n#region-us \n# Stanford Sentiment Treebank - Binary"
] |
1ddc8273e9b87e2b3da2d80a17b803f87eff06f0
|
# Stanford Sentiment Treebank - Binary
|
yangwang825/sst2-pwws
|
[
"region:us"
] |
2023-10-09T20:10:05+00:00
|
{}
|
2023-10-09T21:08:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Stanford Sentiment Treebank - Binary
|
[
"# Stanford Sentiment Treebank - Binary"
] |
[
"TAGS\n#region-us \n",
"# Stanford Sentiment Treebank - Binary"
] |
[
6,
9
] |
[
"passage: TAGS\n#region-us \n# Stanford Sentiment Treebank - Binary"
] |
f5abf5b7c99f74a3bf6b5461f1bbcbef6361cfdb
|
# Stanford Sentiment Treebank - Binary
|
yangwang825/sst2-textfooler
|
[
"region:us"
] |
2023-10-09T20:11:56+00:00
|
{}
|
2023-10-09T21:09:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Stanford Sentiment Treebank - Binary
|
[
"# Stanford Sentiment Treebank - Binary"
] |
[
"TAGS\n#region-us \n",
"# Stanford Sentiment Treebank - Binary"
] |
[
6,
9
] |
[
"passage: TAGS\n#region-us \n# Stanford Sentiment Treebank - Binary"
] |
97e12a8520bbe5c7206590f613119ab0a2dbe9b9
|
This dataset consists of 80 episodes of driving data collected using an autopilot agent in CARLA simulator for training imitation learning models for autonomous driving tasks.
Each frame is structured as follows:
```
frame_data = {
'frame': the frame index,
'hlc': an integer representing the high-level command,
'light': an integer representing current traffic light status,
'controls': an array of [throttle, steer, brake],
'measurements': current speed in km/h,
'rgb': rgb camera image,
'segmentation': ground truth segmentation image,
}
```
This dataset is used in [this project](https://github.com/TheRoboticsClub/gsoc2023-Meiqi_Zhao) and the trained models are available [here](https://huggingface.co/nightmare-nectarine/segmentation-based-imitation-learning-in-CARLA). Check out the [example code](https://github.com/TheRoboticsClub/gsoc2023-Meiqi_Zhao/blob/main/src/ModifiedDeepestLSTMTinyPilotNet/utils/load_dataset.py) for loading the dataset.
|
nightmare-nectarine/segmentation-carla-driving
|
[
"size_categories:10B<n<100B",
"language:en",
"license:mit",
"Autonomous Driving",
"CARLA Simulator",
"ImitationLearning",
"region:us"
] |
2023-10-09T20:15:59+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10B<n<100B"], "pretty_name": "S", "tags": ["Autonomous Driving", "CARLA Simulator", "ImitationLearning"]}
|
2023-10-12T00:36:11+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-10B<n<100B #language-English #license-mit #Autonomous Driving #CARLA Simulator #ImitationLearning #region-us
|
This dataset consists of 80 episodes of driving data collected using an autopilot agent in CARLA simulator for training imitation learning models for autonomous driving tasks.
Each frame is structured as follows:
This dataset is used in this project and the trained models are available here. Check out the example code for loading the dataset.
|
[] |
[
"TAGS\n#size_categories-10B<n<100B #language-English #license-mit #Autonomous Driving #CARLA Simulator #ImitationLearning #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#size_categories-10B<n<100B #language-English #license-mit #Autonomous Driving #CARLA Simulator #ImitationLearning #region-us \n"
] |
e2b4639e7d6ae3cce9110f5ba9cdcfc8213f73c0
|
# Dataset Card for "tulu_v2_cot_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/tulu_v2_cot_subset
|
[
"region:us"
] |
2023-10-09T20:23:55+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 57705790, "num_examples": 50000}], "download_size": 25971959, "dataset_size": 57705790}}
|
2023-10-09T20:23:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tulu_v2_cot_subset"
More Information needed
|
[
"# Dataset Card for \"tulu_v2_cot_subset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tulu_v2_cot_subset\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tulu_v2_cot_subset\"\n\nMore Information needed"
] |
fbdcee6544a06d002889f1899b7af45ae29809c7
|
# Dataset Card for "tulu_v2_flan_v2_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/tulu_v2_flan_v2_subset
|
[
"region:us"
] |
2023-10-09T20:23:58+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 111227584, "num_examples": 50000}], "download_size": 64903414, "dataset_size": 111227584}}
|
2023-10-09T20:24:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tulu_v2_flan_v2_subset"
More Information needed
|
[
"# Dataset Card for \"tulu_v2_flan_v2_subset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tulu_v2_flan_v2_subset\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tulu_v2_flan_v2_subset\"\n\nMore Information needed"
] |
8fe171079e20afb00bfa95e16f267c2b896c8903
|
# Dataset Card for "tulu_v2_oasst1_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/tulu_v2_oasst1_subset
|
[
"region:us"
] |
2023-10-09T20:24:24+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 12306024, "num_examples": 7708}], "download_size": 7059985, "dataset_size": 12306024}}
|
2023-10-09T20:24:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tulu_v2_oasst1_subset"
More Information needed
|
[
"# Dataset Card for \"tulu_v2_oasst1_subset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tulu_v2_oasst1_subset\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tulu_v2_oasst1_subset\"\n\nMore Information needed"
] |
5bb0453d9761ab236df867c24ede974b0e6d699f
|
# Dataset Card for "tulu_v2_gpt4_alpaca_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/tulu_v2_gpt4_alpaca_subset
|
[
"region:us"
] |
2023-10-09T20:24:31+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 16994301, "num_examples": 20000}], "download_size": 9302507, "dataset_size": 16994301}}
|
2023-10-09T20:24:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tulu_v2_gpt4_alpaca_subset"
More Information needed
|
[
"# Dataset Card for \"tulu_v2_gpt4_alpaca_subset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tulu_v2_gpt4_alpaca_subset\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tulu_v2_gpt4_alpaca_subset\"\n\nMore Information needed"
] |
65d42f6ff64a7913e746b259d8b62cacac460930
|
# Dataset Card for "tulu_v2_code_alpaca_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ostapeno/tulu_v2_code_alpaca_subset
|
[
"region:us"
] |
2023-10-09T20:24:34+00:00
|
{"dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 7823498, "num_examples": 20022}], "download_size": 3528838, "dataset_size": 7823498}}
|
2023-10-09T20:24:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tulu_v2_code_alpaca_subset"
More Information needed
|
[
"# Dataset Card for \"tulu_v2_code_alpaca_subset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tulu_v2_code_alpaca_subset\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tulu_v2_code_alpaca_subset\"\n\nMore Information needed"
] |
f998e0237f6bcf1f851f5e080fb3a44ff3f9de4d
|
# Dataset Card for "2ddeba07"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/2ddeba07
|
[
"region:us"
] |
2023-10-09T20:37:38+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 200, "num_examples": 10}], "download_size": 1374, "dataset_size": 200}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T20:37:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "2ddeba07"
More Information needed
|
[
"# Dataset Card for \"2ddeba07\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"2ddeba07\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"2ddeba07\"\n\nMore Information needed"
] |
d5bcda82afbda46a2ff31a7fee9658e9d69ad48a
|
# Dataset Card for Evaluation run of xiaol/RWKV-v4-raven-14B-one-state
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xiaol/RWKV-v4-raven-14B-one-state
- **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 [xiaol/RWKV-v4-raven-14B-one-state](https://huggingface.co/xiaol/RWKV-v4-raven-14B-one-state) 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_xiaol__RWKV-v4-raven-14B-one-state",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-09T21:38:42.028709](https://huggingface.co/datasets/open-llm-leaderboard/details_xiaol__RWKV-v4-raven-14B-one-state/blob/main/results_2023-10-09T21-38-42.028709.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": 0.33924685524661535,
"acc_stderr": 0.03400094010286168,
"acc_norm": 0.3432206955736541,
"acc_norm_stderr": 0.03399555734342263,
"mc1": 0.2484700122399021,
"mc1_stderr": 0.015127427096520681,
"mc2": 0.37298301233557335,
"mc2_stderr": 0.014007983938605419
},
"harness|arc:challenge|25": {
"acc": 0.41467576791808874,
"acc_stderr": 0.014397070564409172,
"acc_norm": 0.45733788395904434,
"acc_norm_stderr": 0.01455810654392407
},
"harness|hellaswag|10": {
"acc": 0.5230033857797252,
"acc_stderr": 0.004984497871025246,
"acc_norm": 0.714797849034057,
"acc_norm_stderr": 0.00450587908460685
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.040943762699967926,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.040943762699967926
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2236842105263158,
"acc_stderr": 0.033911609343436004,
"acc_norm": 0.2236842105263158,
"acc_norm_stderr": 0.033911609343436004
},
"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": 0.36981132075471695,
"acc_stderr": 0.029711421880107915,
"acc_norm": 0.36981132075471695,
"acc_norm_stderr": 0.029711421880107915
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2916666666666667,
"acc_stderr": 0.03800968060554858,
"acc_norm": 0.2916666666666667,
"acc_norm_stderr": 0.03800968060554858
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.17,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_stderr": 0.0349610148119118,
"acc_norm": 0.30057803468208094,
"acc_norm_stderr": 0.0349610148119118
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929777
},
"harness|hendrycksTest-computer_security|5": {
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"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_stderr": 0.03097669299853443,
"acc_norm": 0.3404255319148936,
"acc_norm_stderr": 0.03097669299853443
},
"harness|hendrycksTest-econometrics|5": {
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"acc_stderr": 0.0433913832257986,
"acc_norm": 0.30701754385964913,
"acc_norm_stderr": 0.0433913832257986
},
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"acc_norm": 0.3448275862068966,
"acc_norm_stderr": 0.03960933549451208
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.24867724867724866,
"acc_norm_stderr": 0.02226181769240017
},
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"acc_norm": 0.29365079365079366,
"acc_norm_stderr": 0.04073524322147126
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.26108374384236455,
"acc_norm_stderr": 0.030903796952114482
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_norm": 0.37305699481865284,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm_stderr": 0.022688042352424994
},
"harness|hendrycksTest-high_school_mathematics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.2689075630252101,
"acc_norm_stderr": 0.028801392193631276
},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm": 0.3798165137614679,
"acc_norm_stderr": 0.020808825617866244
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.2037037037037037,
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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-international_law|5": {
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"acc_norm": 0.4132231404958678,
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"acc_norm": 0.32142857142857145,
"acc_norm_stderr": 0.044328040552915185
},
"harness|hendrycksTest-management|5": {
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"acc_norm": 0.27184466019417475,
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},
"harness|hendrycksTest-marketing|5": {
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"acc_norm": 0.44871794871794873,
"acc_norm_stderr": 0.032583346493868806
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.41507024265644954,
"acc_norm_stderr": 0.017620137003655268
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.3988439306358382,
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"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.026362437574546534
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24581005586592178,
"acc_stderr": 0.014400296429225596,
"acc_norm": 0.24581005586592178,
"acc_norm_stderr": 0.014400296429225596
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.026568921015457152,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.026568921015457152
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.3408360128617363,
"acc_stderr": 0.026920841260776162,
"acc_norm": 0.3408360128617363,
"acc_norm_stderr": 0.026920841260776162
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.33641975308641975,
"acc_stderr": 0.026289734945952926,
"acc_norm": 0.33641975308641975,
"acc_norm_stderr": 0.026289734945952926
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.26595744680851063,
"acc_stderr": 0.026358065698880596,
"acc_norm": 0.26595744680851063,
"acc_norm_stderr": 0.026358065698880596
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.33116036505867014,
"acc_stderr": 0.012020128195985757,
"acc_norm": 0.33116036505867014,
"acc_norm_stderr": 0.012020128195985757
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.2867647058823529,
"acc_stderr": 0.02747227447323382,
"acc_norm": 0.2867647058823529,
"acc_norm_stderr": 0.02747227447323382
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.3235294117647059,
"acc_stderr": 0.018926082916083393,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.018926082916083393
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.37272727272727274,
"acc_stderr": 0.04631381319425464,
"acc_norm": 0.37272727272727274,
"acc_norm_stderr": 0.04631381319425464
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.24489795918367346,
"acc_stderr": 0.027529637440174913,
"acc_norm": 0.24489795918367346,
"acc_norm_stderr": 0.027529637440174913
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.4228855721393035,
"acc_stderr": 0.034932317774212816,
"acc_norm": 0.4228855721393035,
"acc_norm_stderr": 0.034932317774212816
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-virology|5": {
"acc": 0.42771084337349397,
"acc_stderr": 0.038515976837185335,
"acc_norm": 0.42771084337349397,
"acc_norm_stderr": 0.038515976837185335
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.03811079669833531,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.03811079669833531
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2484700122399021,
"mc1_stderr": 0.015127427096520681,
"mc2": 0.37298301233557335,
"mc2_stderr": 0.014007983938605419
}
}
```
### 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_xiaol__RWKV-v4-raven-14B-one-state
|
[
"region:us"
] |
2023-10-09T20:38:56+00:00
|
{"pretty_name": "Evaluation run of xiaol/RWKV-v4-raven-14B-one-state", "dataset_summary": "Dataset automatically created during the evaluation run of model [xiaol/RWKV-v4-raven-14B-one-state](https://huggingface.co/xiaol/RWKV-v4-raven-14B-one-state) 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_xiaol__RWKV-v4-raven-14B-one-state\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-09T21:38:42.028709](https://huggingface.co/datasets/open-llm-leaderboard/details_xiaol__RWKV-v4-raven-14B-one-state/blob/main/results_2023-10-09T21-38-42.028709.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\": 0.33924685524661535,\n \"acc_stderr\": 0.03400094010286168,\n \"acc_norm\": 0.3432206955736541,\n \"acc_norm_stderr\": 0.03399555734342263,\n \"mc1\": 0.2484700122399021,\n \"mc1_stderr\": 0.015127427096520681,\n \"mc2\": 0.37298301233557335,\n \"mc2_stderr\": 0.014007983938605419\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.41467576791808874,\n \"acc_stderr\": 0.014397070564409172,\n \"acc_norm\": 0.45733788395904434,\n \"acc_norm_stderr\": 0.01455810654392407\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5230033857797252,\n \"acc_stderr\": 0.004984497871025246,\n \"acc_norm\": 0.714797849034057,\n \"acc_norm_stderr\": 0.00450587908460685\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.2236842105263158,\n \"acc_stderr\": 0.033911609343436004,\n \"acc_norm\": 0.2236842105263158,\n \"acc_norm_stderr\": 0.033911609343436004\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\": 0.36981132075471695,\n \"acc_stderr\": 0.029711421880107915,\n \"acc_norm\": 0.36981132075471695,\n \"acc_norm_stderr\": 0.029711421880107915\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2916666666666667,\n \"acc_stderr\": 0.03800968060554858,\n \"acc_norm\": 0.2916666666666667,\n \"acc_norm_stderr\": 0.03800968060554858\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.30057803468208094,\n \"acc_stderr\": 0.0349610148119118,\n \"acc_norm\": 0.30057803468208094,\n \"acc_norm_stderr\": 0.0349610148119118\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929777,\n \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929777\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3404255319148936,\n \"acc_stderr\": 0.03097669299853443,\n \"acc_norm\": 0.3404255319148936,\n \"acc_norm_stderr\": 0.03097669299853443\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n \"acc_stderr\": 0.0433913832257986,\n \"acc_norm\": 0.30701754385964913,\n \"acc_norm_stderr\": 0.0433913832257986\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.3448275862068966,\n \"acc_stderr\": 0.03960933549451208,\n \"acc_norm\": 0.3448275862068966,\n \"acc_norm_stderr\": 0.03960933549451208\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24867724867724866,\n \"acc_stderr\": 0.02226181769240017,\n \"acc_norm\": 0.24867724867724866,\n \"acc_norm_stderr\": 0.02226181769240017\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\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3419354838709677,\n \"acc_stderr\": 0.026985289576552742,\n \"acc_norm\": 0.3419354838709677,\n \"acc_norm_stderr\": 0.026985289576552742\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114482,\n \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114482\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.48484848484848486,\n \"acc_stderr\": 0.03902551007374448,\n \"acc_norm\": 0.48484848484848486,\n \"acc_norm_stderr\": 0.03902551007374448\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.29292929292929293,\n \"acc_stderr\": 0.03242497958178815,\n \"acc_norm\": 0.29292929292929293,\n \"acc_norm_stderr\": 0.03242497958178815\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.37305699481865284,\n \"acc_stderr\": 0.03490205592048575,\n \"acc_norm\": 0.37305699481865284,\n \"acc_norm_stderr\": 0.03490205592048575\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.27692307692307694,\n \"acc_stderr\": 0.022688042352424994,\n \"acc_norm\": 0.27692307692307694,\n \"acc_norm_stderr\": 0.022688042352424994\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712166,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712166\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.2689075630252101,\n \"acc_stderr\": 0.028801392193631276,\n \"acc_norm\": 0.2689075630252101,\n \"acc_norm_stderr\": 0.028801392193631276\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2119205298013245,\n \"acc_stderr\": 0.033367670865679766,\n \"acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.033367670865679766\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.3798165137614679,\n \"acc_stderr\": 0.020808825617866244,\n \"acc_norm\": 0.3798165137614679,\n \"acc_norm_stderr\": 0.020808825617866244\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.2037037037037037,\n \"acc_stderr\": 0.027467401804057986,\n \"acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.027467401804057986\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.4166666666666667,\n \"acc_stderr\": 0.03460228327239171,\n \"acc_norm\": 0.4166666666666667,\n \"acc_norm_stderr\": 0.03460228327239171\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5232067510548524,\n \"acc_stderr\": 0.032512152011410174,\n \"acc_norm\": 0.5232067510548524,\n \"acc_norm_stderr\": 0.032512152011410174\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.47085201793721976,\n \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.47085201793721976,\n \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.3435114503816794,\n \"acc_stderr\": 0.041649760719448786,\n \"acc_norm\": 0.3435114503816794,\n \"acc_norm_stderr\": 0.041649760719448786\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.4132231404958678,\n \"acc_stderr\": 0.04495087843548408,\n \"acc_norm\": 0.4132231404958678,\n \"acc_norm_stderr\": 0.04495087843548408\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4351851851851852,\n \"acc_stderr\": 0.04792898170907061,\n \"acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.04792898170907061\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.3128834355828221,\n \"acc_stderr\": 0.036429145782924055,\n \"acc_norm\": 0.3128834355828221,\n \"acc_norm_stderr\": 0.036429145782924055\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n \"acc_stderr\": 0.044328040552915185,\n \"acc_norm\": 0.32142857142857145,\n \"acc_norm_stderr\": 0.044328040552915185\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.27184466019417475,\n \"acc_stderr\": 0.044052680241409216,\n \"acc_norm\": 0.27184466019417475,\n \"acc_norm_stderr\": 0.044052680241409216\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.44871794871794873,\n \"acc_stderr\": 0.032583346493868806,\n \"acc_norm\": 0.44871794871794873,\n \"acc_norm_stderr\": 0.032583346493868806\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.41507024265644954,\n \"acc_stderr\": 0.017620137003655268,\n \"acc_norm\": 0.41507024265644954,\n \"acc_norm_stderr\": 0.017620137003655268\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.026362437574546534,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.026362437574546534\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n \"acc_stderr\": 0.014400296429225596,\n \"acc_norm\": 0.24581005586592178,\n \"acc_norm_stderr\": 0.014400296429225596\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.026568921015457152,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.026568921015457152\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3408360128617363,\n \"acc_stderr\": 0.026920841260776162,\n \"acc_norm\": 0.3408360128617363,\n \"acc_norm_stderr\": 0.026920841260776162\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.33641975308641975,\n \"acc_stderr\": 0.026289734945952926,\n \"acc_norm\": 0.33641975308641975,\n \"acc_norm_stderr\": 0.026289734945952926\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880596,\n \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880596\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.33116036505867014,\n \"acc_stderr\": 0.012020128195985757,\n \"acc_norm\": 0.33116036505867014,\n \"acc_norm_stderr\": 0.012020128195985757\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.2867647058823529,\n \"acc_stderr\": 0.02747227447323382,\n \"acc_norm\": 0.2867647058823529,\n \"acc_norm_stderr\": 0.02747227447323382\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.018926082916083393,\n \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.018926082916083393\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.37272727272727274,\n \"acc_stderr\": 0.04631381319425464,\n \"acc_norm\": 0.37272727272727274,\n \"acc_norm_stderr\": 0.04631381319425464\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.027529637440174913,\n \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.027529637440174913\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.4228855721393035,\n \"acc_stderr\": 0.034932317774212816,\n \"acc_norm\": 0.4228855721393035,\n \"acc_norm_stderr\": 0.034932317774212816\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n 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|
2023-10-09T20:39:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of xiaol/RWKV-v4-raven-14B-one-state
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model xiaol/RWKV-v4-raven-14B-one-state 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 2023-10-09T21:38:42.028709(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 xiaol/RWKV-v4-raven-14B-one-state",
"## 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 xiaol/RWKV-v4-raven-14B-one-state 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 2023-10-09T21:38:42.028709(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 xiaol/RWKV-v4-raven-14B-one-state",
"## 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 xiaol/RWKV-v4-raven-14B-one-state 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 2023-10-09T21:38:42.028709(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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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7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of xiaol/RWKV-v4-raven-14B-one-state## 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 xiaol/RWKV-v4-raven-14B-one-state 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 2023-10-09T21:38:42.028709(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
56f6147e725e1d9425a3491b16d5a26077b19c48
|
# Dataset Card for "rule_learning_data_v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/rule_learning_data_v0
|
[
"region:us"
] |
2023-10-09T21:25:38+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "rule", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "configuration", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "filepath", "dtype": "string"}, {"name": "old_instruction", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6226117, "num_examples": 2009}], "download_size": 2213175, "dataset_size": 6226117}}
|
2023-10-09T21:28:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rule_learning_data_v0"
More Information needed
|
[
"# Dataset Card for \"rule_learning_data_v0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rule_learning_data_v0\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rule_learning_data_v0\"\n\nMore Information needed"
] |
1351fa72a41973254f8dd15375bbc94aeae679e0
|
# Dataset Card for "rule_learning_data_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/rule_learning_data_v1
|
[
"region:us"
] |
2023-10-09T21:30:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "rule", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "configuration", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "filepath", "dtype": "string"}, {"name": "old_instruction", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "codellama/CodeLlama-34b-hf---{\"do_sample\": false, \"max_new_tokens\": 256, \"truncate\": 15744, \"return_full_text\": false}", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7650436, "num_examples": 2009}], "download_size": 2660984, "dataset_size": 7650436}}
|
2023-10-10T15:29:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rule_learning_data_v1"
More Information needed
|
[
"# Dataset Card for \"rule_learning_data_v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rule_learning_data_v1\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rule_learning_data_v1\"\n\nMore Information needed"
] |
575adae0db8de56e7c268290b9cb3eb6d3c10a50
|
# Dataset Card for "rule-sql-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/rule-sql-v1
|
[
"region:us"
] |
2023-10-09T21:43:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "rule", "dtype": "string"}, {"name": "software", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 863452252, "num_examples": 262208}], "download_size": 225135160, "dataset_size": 863452252}}
|
2023-10-09T21:43:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rule-sql-v1"
More Information needed
|
[
"# Dataset Card for \"rule-sql-v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rule-sql-v1\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rule-sql-v1\"\n\nMore Information needed"
] |
c5cacb6ccb75c05751ac4ae9c873bdb8510dbe6e
|
# Dataset Card for "bert_pretrain_datasets"
This dataset is essentially a concatenation of the training set of the English Wikipedia (wikipedia.20220301.en.train) and the Book Corpus (bookcorpus.train).
This is exactly how I get this dataset:
```
from datasets import load_dataset, concatenate_datasets, load_from_disk
cache_dir = "/data/haob2/cache/"
# book corpus
bookcorpus = load_dataset("bookcorpus", split="train", cache_dir=cache_dir)
# english wikipedia
wiki = load_dataset("wikipedia", "20220301.en", split="train", cache_dir=cache_dir)
wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"])
# # concatenation
concat = concatenate_datasets([bookcorpus, wiki])
concat.push_to_hub("JackBAI/bert_pretrain_datasets")
```
Note that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset.
|
JackBAI/bert_pretrain_datasets
|
[
"region:us"
] |
2023-10-09T21:43:45+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24500165181, "num_examples": 80462898}], "download_size": 14400389487, "dataset_size": 24500165181}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-28T20:19:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "bert_pretrain_datasets"
This dataset is essentially a concatenation of the training set of the English Wikipedia (URL) and the Book Corpus (URL).
This is exactly how I get this dataset:
Note that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset.
|
[
"# Dataset Card for \"bert_pretrain_datasets\"\n\nThis dataset is essentially a concatenation of the training set of the English Wikipedia (URL) and the Book Corpus (URL).\n\nThis is exactly how I get this dataset:\n\n\n\nNote that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"bert_pretrain_datasets\"\n\nThis dataset is essentially a concatenation of the training set of the English Wikipedia (URL) and the Book Corpus (URL).\n\nThis is exactly how I get this dataset:\n\n\n\nNote that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset."
] |
[
6,
94
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"bert_pretrain_datasets\"\n\nThis dataset is essentially a concatenation of the training set of the English Wikipedia (URL) and the Book Corpus (URL).\n\nThis is exactly how I get this dataset:\n\n\n\nNote that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset."
] |
38066794586bc19c618d4c16862580a09fe23973
|
# Dataset Card for "embeddings_from_distilbert_masking_heaps_and_eval_part1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
johannes-garstenauer/embeddings_from_distilbert_masking_heaps_and_eval_part1
|
[
"region:us"
] |
2023-10-09T22:34:11+00:00
|
{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "pred", "dtype": "int64"}, {"name": "cls_layer_6", "sequence": "float32"}, {"name": "cls_layer_5", "sequence": "float32"}, {"name": "cls_layer_4", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 1281395185, "num_examples": 134495}], "download_size": 1491732485, "dataset_size": 1281395185}}
|
2023-10-09T22:36:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "embeddings_from_distilbert_masking_heaps_and_eval_part1"
More Information needed
|
[
"# Dataset Card for \"embeddings_from_distilbert_masking_heaps_and_eval_part1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"embeddings_from_distilbert_masking_heaps_and_eval_part1\"\n\nMore Information needed"
] |
[
6,
33
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"embeddings_from_distilbert_masking_heaps_and_eval_part1\"\n\nMore Information needed"
] |
ad0eceeae167936ab8698e109a6e85829a2908b8
|
# Dataset Card for Evaluation run of PocketDoc/Dans-TotSirocco-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PocketDoc/Dans-TotSirocco-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 [PocketDoc/Dans-TotSirocco-7b](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b) 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_PocketDoc__Dans-TotSirocco-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T12:54:48.005243](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b/blob/main/results_2023-10-23T12-54-48.005243.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": 0.44997902684563756,
"em_stderr": 0.00509477973209699,
"f1": 0.49544777684563845,
"f1_stderr": 0.00490923385938236,
"acc": 0.45978722729484023,
"acc_stderr": 0.01042644341108249
},
"harness|drop|3": {
"em": 0.44997902684563756,
"em_stderr": 0.00509477973209699,
"f1": 0.49544777684563845,
"f1_stderr": 0.00490923385938236
},
"harness|gsm8k|5": {
"acc": 0.1326762699014405,
"acc_stderr": 0.009343929131442216
},
"harness|winogrande|5": {
"acc": 0.7868981846882399,
"acc_stderr": 0.011508957690722764
}
}
```
### 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_PocketDoc__Dans-TotSirocco-7b
|
[
"region:us"
] |
2023-10-09T22:41:54+00:00
|
{"pretty_name": "Evaluation run of PocketDoc/Dans-TotSirocco-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [PocketDoc/Dans-TotSirocco-7b](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b) 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_PocketDoc__Dans-TotSirocco-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T12:54:48.005243](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b/blob/main/results_2023-10-23T12-54-48.005243.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\": 0.44997902684563756,\n \"em_stderr\": 0.00509477973209699,\n \"f1\": 0.49544777684563845,\n \"f1_stderr\": 0.00490923385938236,\n \"acc\": 0.45978722729484023,\n \"acc_stderr\": 0.01042644341108249\n },\n \"harness|drop|3\": {\n \"em\": 0.44997902684563756,\n \"em_stderr\": 0.00509477973209699,\n \"f1\": 0.49544777684563845,\n \"f1_stderr\": 0.00490923385938236\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1326762699014405,\n \"acc_stderr\": 0.009343929131442216\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722764\n }\n}\n```", "repo_url": "https://huggingface.co/PocketDoc/Dans-TotSirocco-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_10_09T23_41_30.846721", "path": ["**/details_harness|arc:challenge|25_2023-10-09T23-41-30.846721.parquet"]}, {"split": "2023_10_10T03_08_42.670420", "path": ["**/details_harness|arc:challenge|25_2023-10-10T03-08-42.670420.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-10T03-08-42.670420.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T12_54_48.005243", "path": ["**/details_harness|drop|3_2023-10-23T12-54-48.005243.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T12-54-48.005243.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T12_54_48.005243", "path": ["**/details_harness|gsm8k|5_2023-10-23T12-54-48.005243.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T12-54-48.005243.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_09T23_41_30.846721", "path": ["**/details_harness|hellaswag|10_2023-10-09T23-41-30.846721.parquet"]}, {"split": "2023_10_10T03_08_42.670420", "path": ["**/details_harness|hellaswag|10_2023-10-10T03-08-42.670420.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-10T03-08-42.670420.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_09T23_41_30.846721", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T23-41-30.846721.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T23-41-30.846721.parquet", 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|
2023-10-29T09:59:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PocketDoc/Dans-TotSirocco-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 PocketDoc/Dans-TotSirocco-7b 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 2023-10-23T12:54:48.005243(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and 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 PocketDoc/Dans-TotSirocco-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 PocketDoc/Dans-TotSirocco-7b 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 2023-10-23T12:54:48.005243(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 PocketDoc/Dans-TotSirocco-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 PocketDoc/Dans-TotSirocco-7b 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 2023-10-23T12:54:48.005243(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and 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 PocketDoc/Dans-TotSirocco-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 PocketDoc/Dans-TotSirocco-7b 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 2023-10-23T12:54:48.005243(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
8e7f8829f632521c97a9ca418e73e6c03a330325
|
# Dataset Card for "train_split_with_embeddings_bert_base_portuguese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iara-project/train_split_with_embeddings_bert_base_portuguese
|
[
"region:us"
] |
2023-10-09T22:46:27+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "news_id", "dtype": "int64"}, {"name": "embeddings", "sequence": "float64"}, {"name": "sentence", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1670924670, "num_examples": 176114}], "download_size": 1232112225, "dataset_size": 1670924670}}
|
2023-10-09T22:47:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "train_split_with_embeddings_bert_base_portuguese"
More Information needed
|
[
"# Dataset Card for \"train_split_with_embeddings_bert_base_portuguese\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"train_split_with_embeddings_bert_base_portuguese\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"train_split_with_embeddings_bert_base_portuguese\"\n\nMore Information needed"
] |
735d8c25d871980dd89167011efafc6ee580c2d8
|
# Dataset Info
This dataset consists of paired audio and text data sourced from the following book:
- **Title**: Къэрмокъуэ М. Щихухэр иджыри мэкI. Япэ тхылъ.
- **Publication**: Нальчик: Эльбрус, 1999
## Audio Specifications
- **Sample Rate**: 16,000 Hz
- **Total Length**: 10:36:40
- **Source**: [adigabook.ru](http://www.adigabook.ru/?p=1148)
## Processing Information
Audio-text pairs for this dataset were extracted and aligned using META AI's [forced alignment algorithm](https://github.com/facebookresearch/fairseq/tree/main/examples/mms/data_prep).
|
anzorq/sixuxar_yijiri_mak7
|
[
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:kbd",
"license:mit",
"region:us"
] |
2023-10-09T23:23:03+00:00
|
{"language": ["kbd"], "license": "mit", "task_categories": ["automatic-speech-recognition", "text-to-speech"], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 337947909.07, "num_examples": 6579}], "download_size": 727728499, "dataset_size": 337947909.07}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-11T04:19:31+00:00
|
[] |
[
"kbd"
] |
TAGS
#task_categories-automatic-speech-recognition #task_categories-text-to-speech #language-Kabardian #license-mit #region-us
|
# Dataset Info
This dataset consists of paired audio and text data sourced from the following book:
- Title: Къэрмокъуэ М. Щихухэр иджыри мэкI. Япэ тхылъ.
- Publication: Нальчик: Эльбрус, 1999
## Audio Specifications
- Sample Rate: 16,000 Hz
- Total Length: 10:36:40
- Source: URL
## Processing Information
Audio-text pairs for this dataset were extracted and aligned using META AI's forced alignment algorithm.
|
[
"# Dataset Info\n\nThis dataset consists of paired audio and text data sourced from the following book:\n\n- Title: Къэрмокъуэ М. Щихухэр иджыри мэкI. Япэ тхылъ.\n- Publication: Нальчик: Эльбрус, 1999",
"## Audio Specifications\n\n- Sample Rate: 16,000 Hz\n- Total Length: 10:36:40\n- Source: URL",
"## Processing Information\n\nAudio-text pairs for this dataset were extracted and aligned using META AI's forced alignment algorithm."
] |
[
"TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #language-Kabardian #license-mit #region-us \n",
"# Dataset Info\n\nThis dataset consists of paired audio and text data sourced from the following book:\n\n- Title: Къэрмокъуэ М. Щихухэр иджыри мэкI. Япэ тхылъ.\n- Publication: Нальчик: Эльбрус, 1999",
"## Audio Specifications\n\n- Sample Rate: 16,000 Hz\n- Total Length: 10:36:40\n- Source: URL",
"## Processing Information\n\nAudio-text pairs for this dataset were extracted and aligned using META AI's forced alignment algorithm."
] |
[
46,
68,
24,
31
] |
[
"passage: TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #language-Kabardian #license-mit #region-us \n# Dataset Info\n\nThis dataset consists of paired audio and text data sourced from the following book:\n\n- Title: Къэрмокъуэ М. Щихухэр иджыри мэкI. Япэ тхылъ.\n- Publication: Нальчик: Эльбрус, 1999## Audio Specifications\n\n- Sample Rate: 16,000 Hz\n- Total Length: 10:36:40\n- Source: URL## Processing Information\n\nAudio-text pairs for this dataset were extracted and aligned using META AI's forced alignment algorithm."
] |
b31f0ea7cc12a60bd97d161ad190108e5ef70bbe
|
# Dataset Card for "02dd1f44"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/02dd1f44
|
[
"region:us"
] |
2023-10-09T23:35:20+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 158, "num_examples": 10}], "download_size": 1302, "dataset_size": 158}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-09T23:35:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "02dd1f44"
More Information needed
|
[
"# Dataset Card for \"02dd1f44\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"02dd1f44\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"02dd1f44\"\n\nMore Information needed"
] |
0d132c56ed9b8c8a3740834ed5757ce3e9699f91
|
# Dataset card for baneks
## Table of contents
- [Dataset description](#dataset-description)
- [Dataset summary](#dataset-summary)
- [Dataset structure](#dataset-structure)
- [Dataset instance](#dataset-instance)
- [Dataset fields](#dataset-fields)
## Dataset description
- **Homepage:** [baneks homepage]()
- **Repository:** [baneks repository](https://huggingface.co/datasets/zeio/baneks)
- **Point of contact:** [Zeio Nara](mailto:[email protected])
- **Dataset version:** `10.10.2023`
### Dataset summary
This dataset contains anekdotes parsed from a few vk social network communities. Since the dataset is regularly updated, there is no fixed number of entries, so stay tuned.
There are three configurations available withing the dataset:
- **inflated** - a transparent configuration, which introduces minimal changes to the source data;
- **censored** - same as inflated, but entries with same texts are grouped and aggregated;
- **default** - same as censored, but profane words are replaced with their inferred original form in cases when they were censored initially.
## Dataset structure
### Data instance
An example of an entry from the dataset is given below:
```json
{
"text": "- Папа, а кто такие алкоголики? - Ну, сынок.. Вот, видишь - четыре гендера стоят? А алкоголику кажется, что там восемь гендеров - Пап, там два гендера.",
"published": "16-09-2023 01:38",
"id": 497393,
"n-likes": 13,
"n-views": 804,
"accessed": "16-09-2023 01:51",
"source": "anekdotikategoriib"
}
```
### Data fields
Each dataset entry therefore consists of the following fields:
- `text` - text representation of the anecdote;
- `published` - publication date of the corresponding post in the format `DD-MM-YYYY hh:mm`;
- `id` - id of the corresponding post;
- `n-likes` - number of likes received by the corresponding post up to the access date;
- `n-views` - number of views received by the corresponding post up to the access date;
- `accessed`- access date of the corresponding post in the format `DD-MM-YYYY hh:mm`;
- `source` - community name in which the corresponding post has been published.
|
zeio/baneks
|
[
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:original",
"size_categories:10K<n<100K",
"language:ru",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"art",
"humour",
"jokes",
"region:us"
] |
2023-10-09T23:49:24+00:00
|
{"language_creators": ["crowdsourced", "original"], "language": ["ru", "en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "baneks", "tags": ["not-for-all-audiences", "art", "humour", "jokes"], "annotation_creators": ["crowdsourced", "original"]}
|
2023-10-12T17:39:40+00:00
|
[] |
[
"ru",
"en"
] |
TAGS
#task_categories-text-generation #language_creators-crowdsourced #language_creators-original #size_categories-10K<n<100K #language-Russian #language-English #license-apache-2.0 #not-for-all-audiences #art #humour #jokes #region-us
|
# Dataset card for baneks
## Table of contents
- Dataset description
- Dataset summary
- Dataset structure
- Dataset instance
- Dataset fields
## Dataset description
- Homepage: [baneks homepage]()
- Repository: baneks repository
- Point of contact: Zeio Nara
- Dataset version: '10.10.2023'
### Dataset summary
This dataset contains anekdotes parsed from a few vk social network communities. Since the dataset is regularly updated, there is no fixed number of entries, so stay tuned.
There are three configurations available withing the dataset:
- inflated - a transparent configuration, which introduces minimal changes to the source data;
- censored - same as inflated, but entries with same texts are grouped and aggregated;
- default - same as censored, but profane words are replaced with their inferred original form in cases when they were censored initially.
## Dataset structure
### Data instance
An example of an entry from the dataset is given below:
### Data fields
Each dataset entry therefore consists of the following fields:
- 'text' - text representation of the anecdote;
- 'published' - publication date of the corresponding post in the format 'DD-MM-YYYY hh:mm';
- 'id' - id of the corresponding post;
- 'n-likes' - number of likes received by the corresponding post up to the access date;
- 'n-views' - number of views received by the corresponding post up to the access date;
- 'accessed'- access date of the corresponding post in the format 'DD-MM-YYYY hh:mm';
- 'source' - community name in which the corresponding post has been published.
|
[
"# Dataset card for baneks",
"## Table of contents\n\n- Dataset description\n - Dataset summary\n- Dataset structure\n - Dataset instance\n - Dataset fields",
"## Dataset description\n\n- Homepage: [baneks homepage]()\n- Repository: baneks repository\n- Point of contact: Zeio Nara\n- Dataset version: '10.10.2023'",
"### Dataset summary\n\nThis dataset contains anekdotes parsed from a few vk social network communities. Since the dataset is regularly updated, there is no fixed number of entries, so stay tuned.\n\nThere are three configurations available withing the dataset:\n\n- inflated - a transparent configuration, which introduces minimal changes to the source data;\n- censored - same as inflated, but entries with same texts are grouped and aggregated;\n- default - same as censored, but profane words are replaced with their inferred original form in cases when they were censored initially.",
"## Dataset structure",
"### Data instance\n\nAn example of an entry from the dataset is given below:",
"### Data fields\n\nEach dataset entry therefore consists of the following fields:\n\n- 'text' - text representation of the anecdote;\n- 'published' - publication date of the corresponding post in the format 'DD-MM-YYYY hh:mm';\n- 'id' - id of the corresponding post;\n- 'n-likes' - number of likes received by the corresponding post up to the access date;\n- 'n-views' - number of views received by the corresponding post up to the access date;\n- 'accessed'- access date of the corresponding post in the format 'DD-MM-YYYY hh:mm';\n- 'source' - community name in which the corresponding post has been published."
] |
[
"TAGS\n#task_categories-text-generation #language_creators-crowdsourced #language_creators-original #size_categories-10K<n<100K #language-Russian #language-English #license-apache-2.0 #not-for-all-audiences #art #humour #jokes #region-us \n",
"# Dataset card for baneks",
"## Table of contents\n\n- Dataset description\n - Dataset summary\n- Dataset structure\n - Dataset instance\n - Dataset fields",
"## Dataset description\n\n- Homepage: [baneks homepage]()\n- Repository: baneks repository\n- Point of contact: Zeio Nara\n- Dataset version: '10.10.2023'",
"### Dataset summary\n\nThis dataset contains anekdotes parsed from a few vk social network communities. Since the dataset is regularly updated, there is no fixed number of entries, so stay tuned.\n\nThere are three configurations available withing the dataset:\n\n- inflated - a transparent configuration, which introduces minimal changes to the source data;\n- censored - same as inflated, but entries with same texts are grouped and aggregated;\n- default - same as censored, but profane words are replaced with their inferred original form in cases when they were censored initially.",
"## Dataset structure",
"### Data instance\n\nAn example of an entry from the dataset is given below:",
"### Data fields\n\nEach dataset entry therefore consists of the following fields:\n\n- 'text' - text representation of the anecdote;\n- 'published' - publication date of the corresponding post in the format 'DD-MM-YYYY hh:mm';\n- 'id' - id of the corresponding post;\n- 'n-likes' - number of likes received by the corresponding post up to the access date;\n- 'n-views' - number of views received by the corresponding post up to the access date;\n- 'accessed'- access date of the corresponding post in the format 'DD-MM-YYYY hh:mm';\n- 'source' - community name in which the corresponding post has been published."
] |
[
81,
7,
26,
45,
133,
4,
17,
167
] |
[
"passage: TAGS\n#task_categories-text-generation #language_creators-crowdsourced #language_creators-original #size_categories-10K<n<100K #language-Russian #language-English #license-apache-2.0 #not-for-all-audiences #art #humour #jokes #region-us \n# Dataset card for baneks## Table of contents\n\n- Dataset description\n - Dataset summary\n- Dataset structure\n - Dataset instance\n - Dataset fields## Dataset description\n\n- Homepage: [baneks homepage]()\n- Repository: baneks repository\n- Point of contact: Zeio Nara\n- Dataset version: '10.10.2023'### Dataset summary\n\nThis dataset contains anekdotes parsed from a few vk social network communities. Since the dataset is regularly updated, there is no fixed number of entries, so stay tuned.\n\nThere are three configurations available withing the dataset:\n\n- inflated - a transparent configuration, which introduces minimal changes to the source data;\n- censored - same as inflated, but entries with same texts are grouped and aggregated;\n- default - same as censored, but profane words are replaced with their inferred original form in cases when they were censored initially.## Dataset structure### Data instance\n\nAn example of an entry from the dataset is given below:### Data fields\n\nEach dataset entry therefore consists of the following fields:\n\n- 'text' - text representation of the anecdote;\n- 'published' - publication date of the corresponding post in the format 'DD-MM-YYYY hh:mm';\n- 'id' - id of the corresponding post;\n- 'n-likes' - number of likes received by the corresponding post up to the access date;\n- 'n-views' - number of views received by the corresponding post up to the access date;\n- 'accessed'- access date of the corresponding post in the format 'DD-MM-YYYY hh:mm';\n- 'source' - community name in which the corresponding post has been published."
] |
c6e209f419fcacf65aabe6a607ed8dd61b7557f5
|
# Dataset Card for "PMIndiaSum"
## Dataset Description
#### Summary
PMIndiaSum is a new multilingual and massively parallel headline summarization corpus focused on languages in India. Our corpus covers four language families, 14 languages, and the largest to date, 196 language pairs. It provides a testing ground for all cross-lingual pairs.
#### Supported tasks
Monolingual, multilingual and cross-lingual summarization for languages in India.
#### Languages
Assamese, Bengali, Gujarati, Hindi, Kannada, Marathi, Malayalam, Manipuri, Punjabi, Oriya, Telugu, Tamil, Urdu, English
## Example Usage
#### Monolingual and cross-lingual summarization
#### Multilingual summarization
## Dataset Structure
#### Data instances
We show an example of a Telugu-Hindi cross-lingual pair from PMIndiaSum:
```
{
"source_url": "https://www.pmindia.gov.in/te/news_updates/%E0%B0%8E%E0%B0%B2%E0%B0%95%E0%B1%8D%E0%B0%9F%E0%B1%8D%E0%B0%B0%E0%B0%BE%E0%B0%A8%E0%B0%BF%E0%B0%95%E0%B1%8D%E0%B0%B8%E0%B1%8D-%E0%B0%87%E0%B0%82%E0%B0%95%E0%B0%BE-%E0%B0%B8%E0%B0%AE%E0%B0%BE/"
"target_url": "https://www.pmindia.gov.in/hi/news_updates/%E0%A4%AA%E0%A5%8D%E0%A4%B0%E0%A4%A7%E0%A4%BE%E0%A4%A8%E0%A4%AE%E0%A4%82%E0%A4%A4%E0%A5%8D%E0%A4%B0%E0%A5%80-%E0%A4%B6%E0%A5%8D%E0%A4%B0%E0%A5%80-%E0%A4%A8%E0%A4%B0%E0%A5%87%E0%A4%A8%E0%A5%8D-45/"
"text": "ఎలక్ట్రానిక్స్, ఇంకా సమాచార సాంకేతిక విజ్ఞానం రంగంలో ద్వైపాక్షిక సహకారాన్ని పెంపొందింపచేయడంలో భారతదేశానికి మరియు అంగోలా కు మధ్య అవగాహనపూర్వక ఒప్పందాన్ని (ఎమ్ఒయు ను) గురించి ప్రధాన మంత్రి శ్రీ నరేంద్ర మోదీ అధ్యక్షతన జరిగిన కేంద్ర మంత్రివర్గ సమావేశం దృష్టి కి తీసుకువచ్చారు. ఈ ఎమ్ఒయు ఇ-గవర్నెన్స్, సమాచార సాంకేతిక విజ్ఞాన సంబంధ విద్య కు అవసరమైన మానవ వనరుల వికాసం, సమాచార భద్రత, ఎలక్ట్రానిక్స్ హార్డ్ వేర్ తయారీ, ఐటి ఎంబెడెడ్ సాఫ్ట్ వేర్ ఇండస్ట్రీ, టెలిమెడిసిన్ తదితర రంగాలలో సన్నిహిత సహకారాన్ని పెంపొందింపచేయడానికి ఉద్దేశించినటువంటిది"
"summary": "मंत्रिमंडल को इलेक्ट्रॉनिक्स एवं संचना प्रौद्योगिकी के क्षेत्र में द्विपक्षीय सहयोग के लिए भारत और अंगोला के बीच समझौता ज्ञापन से अवगत कराया गया"
}
```
#### Data fields
- 'source_url': A string representing the source article URL
- 'target_url': A string representing the target article URL
- 'text': A string containing the article text
- 'summary': A string containing the article summary
### Load dataset using hf-dataset class
```python
from datasets import load_dataset
dataset = load_dataset("PMIndiaData/PMIndiaSum", "hindi-telugu")
# you can use the combination of any of the following config names as a second argument:
# "assamese", "bengali", "english", "gujarati", "hindi", "kannada", "malayalm", "manipuri", "marathi", "punjabi", "odia", "telugu", "tamil", "urdu"
```
## Creation Details
#### Data source
The data source is [PMIndia](https://www.pmindia.gov.in/) with license information at [here](https://www.pmindia.gov.in/en/website-policies/).
We also extensively used materials from the [PMIndia parallel corpus](https://arxiv.org/abs/2001.09907) and its [code](https://github.com/bhaddow/pmindia-crawler).
#### Data construction details
You can find more details in our [paper](https://arxiv.org/abs/2305.08828).
## Other Information
#### License
Our materials are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). We also request that you respect the [policies]([https://www.pmindia.gov.in/en/website-policies/](https://www.pmindia.gov.in/en/website-policies/)) from the source website.
#### Materials
- **Code repository:** [https://github.com/ashokurlana/pmindiasum](https://github.com/ashokurlana/pmindiasum)
- **Raw data also available at:** [https://drive.google.com/file/d/1KkJ4UbDprtoeeCA6wxfMknWXykYgnLUY/view?usp=sharing](https://drive.google.com/file/d/1KkJ4UbDprtoeeCA6wxfMknWXykYgnLUY/view?usp=sharing)
- **Description paper:** [PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India](https://arxiv.org/abs/2305.08828) at EMNLP Findings 2023.
#### Citation
Our work will be published at EMNLP Findings 2023. If you use our code or data, please kindly cite the following:
```
@inproceedings{urlana-etal-2023-pmindiasum,
title={{PMIndiaSum}: Multilingual and Cross-lingual Headline Summarization for Languages in {India}},
author={Urlana, Ashok and Chen, Pinzhen and Zhao, Zheng and Cohen, Shay B. and Shrivastava, Manish and Haddow, Barry},
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
url ={https://arxiv.org/abs/2305.08828},
year={2023}
}
```
#### Contributors
Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow
#### Contact
Ashok Urlana ([email protected])
|
PMIndiaData/PMIndiaSum
|
[
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:mr",
"language:ml",
"language:mni",
"language:kn",
"language:pa",
"language:ta",
"language:or",
"language:te",
"language:ur",
"language:en",
"license:cc-by-4.0",
"arxiv:2001.09907",
"arxiv:2305.08828",
"region:us"
] |
2023-10-10T00:00:46+00:00
|
{"language": ["as", "bn", "gu", "hi", "mr", "ml", "mni", "kn", "pa", "ta", "or", "te", "ur", "en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["summarization"], "config_names": ["assamese-assamese"], "configs": [{"config_name": "assamese-assamese", "data_files": [{"split": "train", "path": "assamese-assamese/train.csv"}, {"split": "test", "path": "assamese-assamese/test.csv"}, {"split": "valid", "path": "assamese-assamese/valid.csv"}], "default": true}]}
|
2023-11-09T19:26:00+00:00
|
[
"2001.09907",
"2305.08828"
] |
[
"as",
"bn",
"gu",
"hi",
"mr",
"ml",
"mni",
"kn",
"pa",
"ta",
"or",
"te",
"ur",
"en"
] |
TAGS
#task_categories-summarization #size_categories-100K<n<1M #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Marathi #language-Malayalam #language-Manipuri #language-Kannada #language-Panjabi #language-Tamil #language-Oriya (macrolanguage) #language-Telugu #language-Urdu #language-English #license-cc-by-4.0 #arxiv-2001.09907 #arxiv-2305.08828 #region-us
|
# Dataset Card for "PMIndiaSum"
## Dataset Description
#### Summary
PMIndiaSum is a new multilingual and massively parallel headline summarization corpus focused on languages in India. Our corpus covers four language families, 14 languages, and the largest to date, 196 language pairs. It provides a testing ground for all cross-lingual pairs.
#### Supported tasks
Monolingual, multilingual and cross-lingual summarization for languages in India.
#### Languages
Assamese, Bengali, Gujarati, Hindi, Kannada, Marathi, Malayalam, Manipuri, Punjabi, Oriya, Telugu, Tamil, Urdu, English
## Example Usage
#### Monolingual and cross-lingual summarization
#### Multilingual summarization
## Dataset Structure
#### Data instances
We show an example of a Telugu-Hindi cross-lingual pair from PMIndiaSum:
#### Data fields
- 'source_url': A string representing the source article URL
- 'target_url': A string representing the target article URL
- 'text': A string containing the article text
- 'summary': A string containing the article summary
### Load dataset using hf-dataset class
## Creation Details
#### Data source
The data source is PMIndia with license information at here.
We also extensively used materials from the PMIndia parallel corpus and its code.
#### Data construction details
You can find more details in our paper.
## Other Information
#### License
Our materials are licensed under CC BY 4.0. We also request that you respect the policies) from the source website.
#### Materials
- Code repository: URL
- Raw data also available at: URL
- Description paper: PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India at EMNLP Findings 2023.
Our work will be published at EMNLP Findings 2023. If you use our code or data, please kindly cite the following:
#### Contributors
Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow
#### Contact
Ashok Urlana (ashokurlana@URL)
|
[
"# Dataset Card for \"PMIndiaSum\"",
"## Dataset Description",
"#### Summary\nPMIndiaSum is a new multilingual and massively parallel headline summarization corpus focused on languages in India. Our corpus covers four language families, 14 languages, and the largest to date, 196 language pairs. It provides a testing ground for all cross-lingual pairs.",
"#### Supported tasks\nMonolingual, multilingual and cross-lingual summarization for languages in India.",
"#### Languages\nAssamese, Bengali, Gujarati, Hindi, Kannada, Marathi, Malayalam, Manipuri, Punjabi, Oriya, Telugu, Tamil, Urdu, English",
"## Example Usage",
"#### Monolingual and cross-lingual summarization",
"#### Multilingual summarization",
"## Dataset Structure",
"#### Data instances\nWe show an example of a Telugu-Hindi cross-lingual pair from PMIndiaSum:",
"#### Data fields\n\n - 'source_url': A string representing the source article URL\n - 'target_url': A string representing the target article URL\n - 'text': A string containing the article text\n - 'summary': A string containing the article summary",
"### Load dataset using hf-dataset class",
"## Creation Details",
"#### Data source\nThe data source is PMIndia with license information at here.\n\nWe also extensively used materials from the PMIndia parallel corpus and its code.",
"#### Data construction details\nYou can find more details in our paper.",
"## Other Information",
"#### License\nOur materials are licensed under CC BY 4.0. We also request that you respect the policies) from the source website.",
"#### Materials\n- Code repository: URL\n- Raw data also available at: URL \n- Description paper: PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India at EMNLP Findings 2023.\n\nOur work will be published at EMNLP Findings 2023. If you use our code or data, please kindly cite the following:",
"#### Contributors\nAshok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow",
"#### Contact\nAshok Urlana (ashokurlana@URL)"
] |
[
"TAGS\n#task_categories-summarization #size_categories-100K<n<1M #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Marathi #language-Malayalam #language-Manipuri #language-Kannada #language-Panjabi #language-Tamil #language-Oriya (macrolanguage) #language-Telugu #language-Urdu #language-English #license-cc-by-4.0 #arxiv-2001.09907 #arxiv-2305.08828 #region-us \n",
"# Dataset Card for \"PMIndiaSum\"",
"## Dataset Description",
"#### Summary\nPMIndiaSum is a new multilingual and massively parallel headline summarization corpus focused on languages in India. Our corpus covers four language families, 14 languages, and the largest to date, 196 language pairs. It provides a testing ground for all cross-lingual pairs.",
"#### Supported tasks\nMonolingual, multilingual and cross-lingual summarization for languages in India.",
"#### Languages\nAssamese, Bengali, Gujarati, Hindi, Kannada, Marathi, Malayalam, Manipuri, Punjabi, Oriya, Telugu, Tamil, Urdu, English",
"## Example Usage",
"#### Monolingual and cross-lingual summarization",
"#### Multilingual summarization",
"## Dataset Structure",
"#### Data instances\nWe show an example of a Telugu-Hindi cross-lingual pair from PMIndiaSum:",
"#### Data fields\n\n - 'source_url': A string representing the source article URL\n - 'target_url': A string representing the target article URL\n - 'text': A string containing the article text\n - 'summary': A string containing the article summary",
"### Load dataset using hf-dataset class",
"## Creation Details",
"#### Data source\nThe data source is PMIndia with license information at here.\n\nWe also extensively used materials from the PMIndia parallel corpus and its code.",
"#### Data construction details\nYou can find more details in our paper.",
"## Other Information",
"#### License\nOur materials are licensed under CC BY 4.0. We also request that you respect the policies) from the source website.",
"#### Materials\n- Code repository: URL\n- Raw data also available at: URL \n- Description paper: PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India at EMNLP Findings 2023.\n\nOur work will be published at EMNLP Findings 2023. If you use our code or data, please kindly cite the following:",
"#### Contributors\nAshok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow",
"#### Contact\nAshok Urlana (ashokurlana@URL)"
] |
[
131,
11,
4,
68,
27,
35,
5,
13,
8,
6,
25,
61,
13,
4,
34,
14,
3,
27,
82,
36,
15
] |
[
"passage: TAGS\n#task_categories-summarization #size_categories-100K<n<1M #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Marathi #language-Malayalam #language-Manipuri #language-Kannada #language-Panjabi #language-Tamil #language-Oriya (macrolanguage) #language-Telugu #language-Urdu #language-English #license-cc-by-4.0 #arxiv-2001.09907 #arxiv-2305.08828 #region-us \n# Dataset Card for \"PMIndiaSum\"## Dataset Description#### Summary\nPMIndiaSum is a new multilingual and massively parallel headline summarization corpus focused on languages in India. Our corpus covers four language families, 14 languages, and the largest to date, 196 language pairs. It provides a testing ground for all cross-lingual pairs.#### Supported tasks\nMonolingual, multilingual and cross-lingual summarization for languages in India.#### Languages\nAssamese, Bengali, Gujarati, Hindi, Kannada, Marathi, Malayalam, Manipuri, Punjabi, Oriya, Telugu, Tamil, Urdu, English## Example Usage#### Monolingual and cross-lingual summarization#### Multilingual summarization## Dataset Structure#### Data instances\nWe show an example of a Telugu-Hindi cross-lingual pair from PMIndiaSum:#### Data fields\n\n - 'source_url': A string representing the source article URL\n - 'target_url': A string representing the target article URL\n - 'text': A string containing the article text\n - 'summary': A string containing the article summary### Load dataset using hf-dataset class## Creation Details#### Data source\nThe data source is PMIndia with license information at here.\n\nWe also extensively used materials from the PMIndia parallel corpus and its code.#### Data construction details\nYou can find more details in our paper.## Other Information#### License\nOur materials are licensed under CC BY 4.0. We also request that you respect the policies) from the source website."
] |
3cce5ce372a18144385b0497c590df7585293ac8
|
# Dataset Card for "sur_test_rt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/sur_test_rt5
|
[
"region:us"
] |
2023-10-10T01:04:05+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 1141002885, "num_examples": 900000}], "download_size": 279016062, "dataset_size": 1141002885}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-10T01:04:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sur_test_rt5"
More Information needed
|
[
"# Dataset Card for \"sur_test_rt5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sur_test_rt5\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sur_test_rt5\"\n\nMore Information needed"
] |
7f493309afa301238e1b31822db9887c8ae5bfa8
|
# Dataset Card for "MisaHub_WCE_Segmentation_train_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Aaryan333/MisaHub_WCE_Segmentation_train_val
|
[
"region:us"
] |
2023-10-10T01:16:44+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 131460889.53022918, "num_examples": 2094}, {"name": "validation", "num_bytes": 32711768.699770816, "num_examples": 524}], "download_size": 162770574, "dataset_size": 164172658.23}}
|
2023-10-10T01:17:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MisaHub_WCE_Segmentation_train_val"
More Information needed
|
[
"# Dataset Card for \"MisaHub_WCE_Segmentation_train_val\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MisaHub_WCE_Segmentation_train_val\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MisaHub_WCE_Segmentation_train_val\"\n\nMore Information needed"
] |
e722d25c8fcfa53111d8c5ff5474dc01cb05e2de
|
# Dataset Card for "diffusiondb-seq2seq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roborovski/diffusiondb-seq2seq
|
[
"region:us"
] |
2023-10-10T01:25:27+00:00
|
{"dataset_info": {"features": [{"name": "subject", "dtype": "string"}, {"name": "descriptor", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10079006, "num_examples": 93834}], "download_size": 6236928, "dataset_size": 10079006}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-10T02:04:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "diffusiondb-seq2seq"
More Information needed
|
[
"# Dataset Card for \"diffusiondb-seq2seq\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"diffusiondb-seq2seq\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"diffusiondb-seq2seq\"\n\nMore Information needed"
] |
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