Datasets:

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huggingface-datasets-processed

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train · 52.9k rows

sha stringlengths 40 40	text stringlengths 1 13.4M	id stringlengths 2 117	tags listlengths 1 7.91k	created_at stringlengths 25 25	metadata stringlengths 2 875k	last_modified stringlengths 25 25	arxiv listlengths 0 25	languages listlengths 0 7.91k	tags_str stringlengths 17 159k	text_str stringlengths 1 447k	text_lists listlengths 0 352	processed_texts listlengths 1 353	tokens_length listlengths 1 353	input_texts listlengths 1 40
fe35cf023c615d93d69cb04760094477747f2ae5	# Dataset of rockrock (Arknights) This is the dataset of rockrock (Arknights), containing 48 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 48 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 132 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 141 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 48 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 48 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 48 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 132 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 132 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 121 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 141 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 141 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|	AppleHarem/rockrock_arknights	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-12-17T13:38:09+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-12-17T13:38:14+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of rockrock (Arknights) =============================== This is the dataset of rockrock (Arknights), containing 48 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). This is a WebUI contains crawlers and other thing: (LittleAppleWebUI)	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
5ae14d948421cdf1bd294a5687457078e9818be9	A subset of wikitext combined with a sample of hindi wikipedia articles. Each line contains a paragraph from the article.	cmeraki/wiki_en_hi	[ "license:apache-2.0", "region:us" ]	2023-12-17T13:40:18+00:00	{"license": "apache-2.0"}	2023-12-17T13:44:58+00:00	[]	[]	TAGS #license-apache-2.0 #region-us	A subset of wikitext combined with a sample of hindi wikipedia articles. Each line contains a paragraph from the article.	[]	[ "TAGS\n#license-apache-2.0 #region-us \n" ]	[ 14 ]	[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
faccebcf3ed1966f603acedb98d3fa6fb1a4faad	# 和谐历史档案馆数据集 - Banned Historical Archives Datasets banned-historical-archives.github.io ## 说明 * todo文件夹存放暂未录入banned-historical-archives.github.io的文稿，已录入的文稿存放在archivesN文件夹中。录入方法参考github的文档。不要直接修改archivesN文件夹。archivesN的数据不定期从github同步。 * todo文件夹中，应及时删除已录入的文稿，避免重复录入。 * 不完整的文稿存放在“todo/不完整不清晰”文件夹，优先级最低	banned-historical-archives/banned-historical-archives	[ "size_categories:n>1T", "region:us" ]	2023-12-17T14:47:08+00:00	{"size_categories": ["n>1T"]}	2024-02-16T12:18:33+00:00	[]	[]	TAGS #size_categories-n>1T #region-us	# 和谐历史档案馆数据集 - Banned Historical Archives Datasets URL ## 说明 * todo文件夹存放暂未录入banned-URL.io的文稿，已录入的文稿存放在archivesN文件夹中。录入方法参考github的文档。不要直接修改archivesN文件夹。archivesN的数据不定期从github同步。 * todo文件夹中，应及时删除已录入的文稿，避免重复录入。 * 不完整的文稿存放在“todo/不完整不清晰”文件夹，优先级最低	[ "# 和谐历史档案馆数据集 - Banned Historical Archives Datasets\n\nURL", "## 说明\n\n* todo文件夹存放暂未录入banned-URL.io的文稿，已录入的文稿存放在archivesN文件夹中。录入方法参考github的文档。不要直接修改archivesN文件夹。archivesN的数据不定期从github同步。\n* todo文件夹中，应及时删除已录入的文稿，避免重复录入。\n* 不完整的文稿存放在“todo/不完整不清晰”文件夹，优先级最低" ]	[ "TAGS\n#size_categories-n>1T #region-us \n", "# 和谐历史档案馆数据集 - Banned Historical Archives Datasets\n\nURL", "## 说明\n\n* todo文件夹存放暂未录入banned-URL.io的文稿，已录入的文稿存放在archivesN文件夹中。录入方法参考github的文档。不要直接修改archivesN文件夹。archivesN的数据不定期从github同步。\n* todo文件夹中，应及时删除已录入的文稿，避免重复录入。\n* 不完整的文稿存放在“todo/不完整不清晰”文件夹，优先级最低" ]	[ 16, 18, 112 ]	[ "passage: TAGS\n#size_categories-n>1T #region-us \n# 和谐历史档案馆数据集 - Banned Historical Archives Datasets\n\nURL## 说明\n\n* todo文件夹存放暂未录入banned-URL.io的文稿，已录入的文稿存放在archivesN文件夹中。录入方法参考github的文档。不要直接修改archivesN文件夹。archivesN的数据不定期从github同步。\n* todo文件夹中，应及时删除已录入的文稿，避免重复录入。\n* 不完整的文稿存放在“todo/不完整不清晰”文件夹，优先级最低" ]
a1c1161bccd302f4a35bda5394df00e59ca6d8b2	## Description I explore the past so you don't have too! ## Prompt A channel run by an influencer and videoblogger called Jess. She often do weird challenges like "saying yes to everyone", "walking to corss the united states", "walk in new york dressed as a chicken" to get millions of views and likes. She also sometimes give tips and advices for make-up, beauty, dating etc, but she now makes random videos She is also a pro gamer, enjoying games like League of Legends, Fortnite, Call of Duty, The Sims, GTA 5, Baldur's Gate 3, but she now makes random videos	MichaelBoll/ai-tube-micgael	[ "license:cc-by-nc-sa-4.0", "region:us" ]	2023-12-17T14:57:42+00:00	{"license": "cc-by-nc-sa-4.0", "pretty_name": "Jess"}	2024-01-14T18:31:39+00:00	[]	[]	TAGS #license-cc-by-nc-sa-4.0 #region-us	## Description I explore the past so you don't have too! ## Prompt A channel run by an influencer and videoblogger called Jess. She often do weird challenges like "saying yes to everyone", "walking to corss the united states", "walk in new york dressed as a chicken" to get millions of views and likes. She also sometimes give tips and advices for make-up, beauty, dating etc, but she now makes random videos She is also a pro gamer, enjoying games like League of Legends, Fortnite, Call of Duty, The Sims, GTA 5, Baldur's Gate 3, but she now makes random videos	[ "## Description\n\nI explore the past so you don't have too!", "## Prompt\n\nA channel run by an influencer and videoblogger called Jess.\n\nShe often do weird challenges like \"saying yes to everyone\", \"walking to corss the united states\", \"walk in new york dressed as a chicken\" to get millions of views and likes.\n\nShe also sometimes give tips and advices for make-up, beauty, dating etc, but she now makes random videos\n\nShe is also a pro gamer, enjoying games like League of Legends, Fortnite, Call of Duty, The Sims, GTA 5, Baldur's Gate 3, but she now makes random videos" ]	[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "## Description\n\nI explore the past so you don't have too!", "## Prompt\n\nA channel run by an influencer and videoblogger called Jess.\n\nShe often do weird challenges like \"saying yes to everyone\", \"walking to corss the united states\", \"walk in new york dressed as a chicken\" to get millions of views and likes.\n\nShe also sometimes give tips and advices for make-up, beauty, dating etc, but she now makes random videos\n\nShe is also a pro gamer, enjoying games like League of Legends, Fortnite, Call of Duty, The Sims, GTA 5, Baldur's Gate 3, but she now makes random videos" ]	[ 19, 14, 131 ]	[ "passage: TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n## Description\n\nI explore the past so you don't have too!## Prompt\n\nA channel run by an influencer and videoblogger called Jess.\n\nShe often do weird challenges like \"saying yes to everyone\", \"walking to corss the united states\", \"walk in new york dressed as a chicken\" to get millions of views and likes.\n\nShe also sometimes give tips and advices for make-up, beauty, dating etc, but she now makes random videos\n\nShe is also a pro gamer, enjoying games like League of Legends, Fortnite, Call of Duty, The Sims, GTA 5, Baldur's Gate 3, but she now makes random videos" ]
ba5e50644c433408938665fc5d34b45a979b835f	Dataset содержит диалоги и различные новостные статьи на русском языке, а также их суммаризация.Dataset состоит из двух hugging face datasetов : - 'd0rj/samsum-ru' - 'IlyaGusev/gazeta'	CarlBrendt/Summ_Dialog_News	[ "task_categories:summarization", "task_categories:text-generation", "size_categories:10K<n<100K", "language:ru", "region:us" ]	2023-12-17T15:03:15+00:00	{"language": ["ru"], "size_categories": ["10K<n<100K"], "task_categories": ["summarization", "text-generation"], "pretty_name": "Summy", "dataset_info": {"features": [{"name": "info", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 560231829, "num_examples": 75697}, {"name": "test", "num_bytes": 55960270, "num_examples": 7609}, {"name": "validation", "num_bytes": 53624377, "num_examples": 7188}], "download_size": 340026369, "dataset_size": 669816476}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}, {"split": "validation", "path": "data/validation-*"}]}]}	2023-12-17T18:33:24+00:00	[]	[ "ru" ]	TAGS #task_categories-summarization #task_categories-text-generation #size_categories-10K<n<100K #language-Russian #region-us	Dataset содержит диалоги и различные новостные статьи на русском языке, а также их суммаризация.Dataset состоит из двух hugging face datasetов : - 'd0rj/samsum-ru' - 'IlyaGusev/gazeta'	[]	[ "TAGS\n#task_categories-summarization #task_categories-text-generation #size_categories-10K<n<100K #language-Russian #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-summarization #task_categories-text-generation #size_categories-10K<n<100K #language-Russian #region-us \n" ]
7e27479c70b0bffcf9930fa581741e76f374e9d0	# Dataset Card for "Boundary_detection_onelasttry" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	vigneshgs7/Boundary_detection_onelasttry	[ "region:us" ]	2023-12-17T15:16:30+00:00	{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1242702805.0, "num_examples": 25}], "download_size": 32579261, "dataset_size": 1242702805.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-17T15:16:57+00:00	[]	[]	TAGS #region-us	# Dataset Card for "Boundary_detection_onelasttry" More Information needed	[ "# Dataset Card for \"Boundary_detection_onelasttry\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"Boundary_detection_onelasttry\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"Boundary_detection_onelasttry\"\n\nMore Information needed" ]
d65cb606a8b1ba7104b517e37af9b081198807db	# Dataset Card for "wikipedia-pa-transliteration" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	kdcyberdude/wikipedia-pa-transliteration	[ "region:us" ]	2023-12-17T15:17:14+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "transliterated_text", "dtype": "string"}, {"name": "transliterated_title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 311038383, "num_examples": 51423}], "download_size": 137271151, "dataset_size": 311038383}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-17T19:29:55+00:00	[]	[]	TAGS #region-us	# Dataset Card for "wikipedia-pa-transliteration" More Information needed	[ "# Dataset Card for \"wikipedia-pa-transliteration\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"wikipedia-pa-transliteration\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia-pa-transliteration\"\n\nMore Information needed" ]
1fac2a4db3550c82587d5e1039ee712845a1f4f7	# Description This is the dataset repository used in the [pyiqa toolbox](https://github.com/chaofengc/IQA-PyTorch). Please refer to [Awesome Image Quality Assessment](https://github.com/chaofengc/Awesome-Image-Quality-Assessment) for details of each dataset # Disclaimer for This Dataset Collection This collection of datasets is compiled and maintained for academic, research, and educational purposes. It is important to note the following points regarding the datasets included in this Collection: - Rights & Permissions: Each dataset in this Collection is the property of its respective creators and owners. We do not claim ownership of any of these datasets. The inclusion of a dataset in this Collection does not imply any affiliation with or endorsement by the dataset's creators or owners. - Usage Guidelines: Users of this Collection are advised to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset. It is the user's responsibility to ensure compliance with these terms. - No Warranty: This Collection is provided "as is" without any warranties, express or implied, regarding the accuracy, completeness, performance, or suitability of the datasets for any particular purpose. Users should verify the appropriateness and quality of each dataset for their specific needs. - Attribution: Proper attribution should be given to the original creators or sources of the datasets when they are used for research, publication, or other purposes. - Changes and Updates: The contents of this Collection are subject to change without notice. We reserve the right to add, modify, or remove datasets from the Collection at any time. - Contact Information: For any inquiries or concerns regarding the datasets in this Collection, please contact the original creators or owners of the respective datasets.	chaofengc/IQA-Toolbox-Datasets	[ "license:cc-by-sa-4.0", "region:us" ]	2023-12-17T15:22:46+00:00	{"license": "cc-by-sa-4.0"}	2024-01-17T05:51:13+00:00	[]	[]	TAGS #license-cc-by-sa-4.0 #region-us	# Description This is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset # Disclaimer for This Dataset Collection This collection of datasets is compiled and maintained for academic, research, and educational purposes. It is important to note the following points regarding the datasets included in this Collection: - Rights & Permissions: Each dataset in this Collection is the property of its respective creators and owners. We do not claim ownership of any of these datasets. The inclusion of a dataset in this Collection does not imply any affiliation with or endorsement by the dataset's creators or owners. - Usage Guidelines: Users of this Collection are advised to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset. It is the user's responsibility to ensure compliance with these terms. - No Warranty: This Collection is provided "as is" without any warranties, express or implied, regarding the accuracy, completeness, performance, or suitability of the datasets for any particular purpose. Users should verify the appropriateness and quality of each dataset for their specific needs. - Attribution: Proper attribution should be given to the original creators or sources of the datasets when they are used for research, publication, or other purposes. - Changes and Updates: The contents of this Collection are subject to change without notice. We reserve the right to add, modify, or remove datasets from the Collection at any time. - Contact Information: For any inquiries or concerns regarding the datasets in this Collection, please contact the original creators or owners of the respective datasets.	[ "# Description\n\nThis is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset", "# Disclaimer for This Dataset Collection\nThis collection of datasets is compiled and maintained for academic, research, and educational purposes. It is important to note the following points regarding the datasets included in this Collection:\n\n- Rights & Permissions: Each dataset in this Collection is the property of its respective creators and owners. We do not claim ownership of any of these datasets. The inclusion of a dataset in this Collection does not imply any affiliation with or endorsement by the dataset's creators or owners.\n- Usage Guidelines: Users of this Collection are advised to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset. It is the user's responsibility to ensure compliance with these terms.\n- No Warranty: This Collection is provided \"as is\" without any warranties, express or implied, regarding the accuracy, completeness, performance, or suitability of the datasets for any particular purpose. Users should verify the appropriateness and quality of each dataset for their specific needs.\n- Attribution: Proper attribution should be given to the original creators or sources of the datasets when they are used for research, publication, or other purposes.\n- Changes and Updates: The contents of this Collection are subject to change without notice. We reserve the right to add, modify, or remove datasets from the Collection at any time.\n- Contact Information: For any inquiries or concerns regarding the datasets in this Collection, please contact the original creators or owners of the respective datasets." ]	[ "TAGS\n#license-cc-by-sa-4.0 #region-us \n", "# Description\n\nThis is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset", "# Disclaimer for This Dataset Collection\nThis collection of datasets is compiled and maintained for academic, research, and educational purposes. It is important to note the following points regarding the datasets included in this Collection:\n\n- Rights & Permissions: Each dataset in this Collection is the property of its respective creators and owners. We do not claim ownership of any of these datasets. The inclusion of a dataset in this Collection does not imply any affiliation with or endorsement by the dataset's creators or owners.\n- Usage Guidelines: Users of this Collection are advised to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset. It is the user's responsibility to ensure compliance with these terms.\n- No Warranty: This Collection is provided \"as is\" without any warranties, express or implied, regarding the accuracy, completeness, performance, or suitability of the datasets for any particular purpose. Users should verify the appropriateness and quality of each dataset for their specific needs.\n- Attribution: Proper attribution should be given to the original creators or sources of the datasets when they are used for research, publication, or other purposes.\n- Changes and Updates: The contents of this Collection are subject to change without notice. We reserve the right to add, modify, or remove datasets from the Collection at any time.\n- Contact Information: For any inquiries or concerns regarding the datasets in this Collection, please contact the original creators or owners of the respective datasets." ]	[ 17, 32, 358 ]	[ "passage: TAGS\n#license-cc-by-sa-4.0 #region-us \n# Description\n\nThis is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset# Disclaimer for This Dataset Collection\nThis collection of datasets is compiled and maintained for academic, research, and educational purposes. It is important to note the following points regarding the datasets included in this Collection:\n\n- Rights & Permissions: Each dataset in this Collection is the property of its respective creators and owners. We do not claim ownership of any of these datasets. The inclusion of a dataset in this Collection does not imply any affiliation with or endorsement by the dataset's creators or owners.\n- Usage Guidelines: Users of this Collection are advised to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset. It is the user's responsibility to ensure compliance with these terms.\n- No Warranty: This Collection is provided \"as is\" without any warranties, express or implied, regarding the accuracy, completeness, performance, or suitability of the datasets for any particular purpose. Users should verify the appropriateness and quality of each dataset for their specific needs.\n- Attribution: Proper attribution should be given to the original creators or sources of the datasets when they are used for research, publication, or other purposes.\n- Changes and Updates: The contents of this Collection are subject to change without notice. We reserve the right to add, modify, or remove datasets from the Collection at any time.\n- Contact Information: For any inquiries or concerns regarding the datasets in this Collection, please contact the original creators or owners of the respective datasets." ]
490c80f0bff50c7fa720f4849281e7efb44946f3	# Dataset of highmore (Arknights) This is the dataset of highmore (Arknights), containing 48 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 48 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 133 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 145 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 48 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 48 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 48 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 133 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 133 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 106 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 145 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 145 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|	AppleHarem/highmore_arknights	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-12-17T15:39:44+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-12-17T15:39:52+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of highmore (Arknights) =============================== This is the dataset of highmore (Arknights), containing 48 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). This is a WebUI contains crawlers and other thing: (LittleAppleWebUI)	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
1cfc4b5b01a27d90d4d546c7547a4371285fb8d2	# Dataset Card for "NER" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	NguyenVanHieu1605/NER	[ "region:us" ]	2023-12-17T15:40:26+00:00	{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "NE_labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3474172, "num_examples": 14986}, {"name": "valid", "num_bytes": 872991, "num_examples": 3465}, {"name": "test", "num_bytes": 791856, "num_examples": 3683}], "download_size": 1253151, "dataset_size": 5139019}}	2023-12-17T15:40:46+00:00	[]	[]	TAGS #region-us	# Dataset Card for "NER" More Information needed	[ "# Dataset Card for \"NER\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"NER\"\n\nMore Information needed" ]	[ 6, 11 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"NER\"\n\nMore Information needed" ]
1c8f6468ab96f81788e54877c63bb5f7de635ebb	# Dataset Card for "ner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	lunarlist/ner	[ "region:us" ]	2023-12-17T15:59:48+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": "ner_tags", "sequence": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 72892413, "num_examples": 147734}, {"name": "validation", "num_bytes": 8664757, "num_examples": 16410}], "download_size": 17227566, "dataset_size": 81557170}}	2023-12-17T15:59:58+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ner" More Information needed	[ "# Dataset Card for \"ner\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ner\"\n\nMore Information needed" ]	[ 6, 11 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ner\"\n\nMore Information needed" ]
f333fc52ccdb832396fff66a4e583ea99dff81dc	# Dataset Card for "legal_chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	thanhdath/legal_chat	[ "region:us" ]	2023-12-17T16:15:35+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-"}, {"split": "test_sft", "path": "data/test_sft-"}]}], "dataset_info": {"features": [{"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_sft", "num_bytes": 646516984, "num_examples": 108780}, {"name": "test_sft", "num_bytes": 11923316, "num_examples": 2000}], "download_size": 213534245, "dataset_size": 658440300}}	2024-01-07T10:48:24+00:00	[]	[]	TAGS #region-us	# Dataset Card for "legal_chat" More Information needed	[ "# Dataset Card for \"legal_chat\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"legal_chat\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"legal_chat\"\n\nMore Information needed" ]
5ac4c4ae3740ca56c1544781785702fc9934f8bb	license: cc-by-nc-sa-4.0 pretty_name: bruhChannel --- ## Description random stuff ## Model SVD ## Style - fails, cooking, baking, funny videos, and more ## Tags - funny - cooking - baking - andmore ## Voice Julian ## Prompt A video channel which produces random stuff It often starts videos with titles like 'funny video <of what/> That Will Shock You!", "look at this". It features funny videos, cooking, baking, and more and interior should be pro-quality shots.	bruhrandomusername/bruh-channel	[ "region:us" ]	2023-12-17T16:27:21+00:00	{}	2023-12-17T16:47:00+00:00	[]	[]	TAGS #region-us	license: cc-by-nc-sa-4.0 pretty_name: bruhChannel --- ## Description random stuff ## Model SVD ## Style - fails, cooking, baking, funny videos, and more ## Tags - funny - cooking - baking - andmore ## Voice Julian ## Prompt A video channel which produces random stuff It often starts videos with titles like 'funny video <of what/> That Will Shock You!", "look at this". It features funny videos, cooking, baking, and more and interior should be pro-quality shots.	[ "## Description\n\nrandom stuff", "## Model\n\nSVD", "## Style\n\n- fails, cooking, baking, funny videos, and more", "## Tags\n\n- funny\n- cooking\n- baking\n- andmore", "## Voice\n\nJulian", "## Prompt\n\nA video channel which produces random stuff\nIt often starts videos with titles like 'funny video <of what/> That Will Shock You!\", \"look at this\".\nIt features funny videos, cooking, baking, and more\nand interior should be pro-quality shots." ]	[ "TAGS\n#region-us \n", "## Description\n\nrandom stuff", "## Model\n\nSVD", "## Style\n\n- fails, cooking, baking, funny videos, and more", "## Tags\n\n- funny\n- cooking\n- baking\n- andmore", "## Voice\n\nJulian", "## Prompt\n\nA video channel which produces random stuff\nIt often starts videos with titles like 'funny video <of what/> That Will Shock You!\", \"look at this\".\nIt features funny videos, cooking, baking, and more\nand interior should be pro-quality shots." ]	[ 6, 4, 4, 16, 12, 3, 63 ]	[ "passage: TAGS\n#region-us \n## Description\n\nrandom stuff## Model\n\nSVD## Style\n\n- fails, cooking, baking, funny videos, and more## Tags\n\n- funny\n- cooking\n- baking\n- andmore## Voice\n\nJulian## Prompt\n\nA video channel which produces random stuff\nIt often starts videos with titles like 'funny video <of what/> That Will Shock You!\", \"look at this\".\nIt features funny videos, cooking, baking, and more\nand interior should be pro-quality shots." ]
5f42b23313038b9d3c74c9f7942a3a106b24cb8f	# Dataset Card for "beijing_opera_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/beijing_opera_extract_unit	[ "region:us" ]	2023-12-17T16:33:14+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 1808834, "num_examples": 236}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 1808834, "num_examples": 236}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 2707522, "num_examples": 236}, {"name": "audiodec_24k_320d", "num_bytes": 5784962, "num_examples": 236}, {"name": "dac_16k", "num_bytes": 11573570, "num_examples": 236}, {"name": "dac_24k", "num_bytes": 32118786, "num_examples": 236}, {"name": "dac_44k", "num_bytes": 9803570, "num_examples": 236}, {"name": "encodec_24k", "num_bytes": 1361378, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 14477314, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 14477314, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 14477314, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 7287810, "num_examples": 236}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 14477314, "num_examples": 236}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 14477314, "num_examples": 236}, {"name": "speech_tokenizer_16k", "num_bytes": 3625090, "num_examples": 236}], "download_size": 19621421, "dataset_size": 150266926}}	2023-12-17T16:34:02+00:00	[]	[]	TAGS #region-us	# Dataset Card for "beijing_opera_extract_unit" More Information needed	[ "# Dataset Card for \"beijing_opera_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"beijing_opera_extract_unit\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"beijing_opera_extract_unit\"\n\nMore Information needed" ]
ec9a3452738efc2e3a723e9a8fddfeeb83f51748	ReActor Assets ================= The Fast and Simple Face Swap Extension [sd-webui-reactor](https://github.com/Gourieff/sd-webui-reactor) <br> [comfyui-reactor-node](https://github.com/Gourieff/comfyui-reactor-node) Models ------ \| file \| source \| license \| \|---------------------------------------------------------------------------------------------------------------------------------------\|-------------------------------------------------------------\|-------------------------------------------------------------------------\| \| [buffalo_l.zip](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/buffalo_l.zip) \| [DeepInsight](https://github.com/deepinsight/insightface) \| ![license](https://img.shields.io/badge/license-non_commercial-red) \| \| [codeformer-v0.1.0.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/codeformer-v0.1.0.pth) \| [sczhou](https://github.com/sczhou/CodeFormer) \| ![license](https://img.shields.io/badge/license-non_commercial-red) \| \| [GFPGANv1.3.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.3.pth) \| [TencentARC](https://github.com/TencentARC/GFPGAN) \| ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) \| \| [GFPGANv1.4.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.4.pth) \| [TencentARC](https://github.com/TencentARC/GFPGAN) \| ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) \| \| [inswapper_128.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) \| [DeepInsight](https://github.com/deepinsight/insightface) \| ![license](https://img.shields.io/badge/license-non_commercial-red) \| \| [inswapper_128_fp16.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx) \| [Hillobar](https://github.com/Hillobar/Rope) \| ![license](https://img.shields.io/badge/license-non_commercial-red) \|	Gourieff/ReActor	[ "license:mit", "region:us" ]	2023-12-17T16:57:34+00:00	{"license": "mit", "viewer": false}	2024-01-23T16:37:19+00:00	[]	[]	TAGS #license-mit #region-us	ReActor Assets ============== The Fast and Simple Face Swap Extension sd-webui-reactor comfyui-reactor-node Models ------ file: buffalo\_l.zip, source: DeepInsight, license: !license file: codeformer-v0.1.0.pth, source: sczhou, license: !license file: GFPGANv1.3.pth, source: TencentARC, license: !license file: GFPGANv1.4.pth, source: TencentARC, license: !license file: inswapper\_128.onnx, source: DeepInsight, license: !license file: inswapper\_128\_fp16.onnx, source: Hillobar, license: !license	[]	[ "TAGS\n#license-mit #region-us \n" ]	[ 11 ]	[ "passage: TAGS\n#license-mit #region-us \n" ]
4001c2c6cdf4310133d1e5399cdea0f5588c9fd5	# Dataset Card for "crema_d_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/crema_d_extract_unit	[ "region:us" ]	2023-12-17T16:59:08+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 60046688, "num_examples": 7438}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 60046688, "num_examples": 7438}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 89798688, "num_examples": 7438}, {"name": "audiodec_24k_320d", "num_bytes": 191074496, "num_examples": 7438}, {"name": "dac_16k", "num_bytes": 364945184, "num_examples": 7438}, {"name": "dac_24k", "num_bytes": 1012467712, "num_examples": 7438}, {"name": "dac_44k", "num_bytes": 309160184, "num_examples": 7438}, {"name": "encodec_24k", "num_bytes": 45111184, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 479311872, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 479311872, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 477407744, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 239391744, "num_examples": 7438}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 477407744, "num_examples": 7438}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 477407744, "num_examples": 7438}, {"name": "speech_tokenizer_16k", "num_bytes": 119669696, "num_examples": 7438}], "download_size": 682489172, "dataset_size": 4882559240}}	2023-12-17T17:01:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "crema_d_extract_unit" More Information needed	[ "# Dataset Card for \"crema_d_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"crema_d_extract_unit\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"crema_d_extract_unit\"\n\nMore Information needed" ]
7d2671790555d99208c308236445593442843964	# Datasets Summary The data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it. # Languages The dataset is based on Turkish. # Data Instances for datav2.csv * Title : Tavuklu Zade Kebabı, * Link: https://ye-mek.net/tarif/tavuklu-zade-kebabi, * Category: Ana-Yemek, * Materials: "['4 adet orta boy kemer patlıcan', '500 gr kuşbaşı doğranmış tavuk göğsü', '2 adet orta boy patates', '1 adet orta boy soğan', '2 adet yeşil biber', '1 adet orta boy domates', '2 diş sarımsak', '1 tatlı kaşığıdomates salçası', '5 yemek kaşığı zeytinyağı', 'Tuz', 'Karabiber', 'Üzeri İçin:', 'Rendelenmiş kaşar peynir']", * How to do: "Tavuklu zade kebabı yapımı için; geniş bir tencere içine 4-5 yemek kaşığı zeytinyağı döküp, ısıtın. Isınan yağın üzerine 500 gr kuşbaşı doğranmış tavuk etini koyun. Suyunu salıp, hafifçe çekene kadar pişirin.Daha sonra tavuk etlerinin üzerine 1 adet orta boy ince ince doğranmış soğan ve 2 adet küçük küçük doğranmış yeşil biberi ekleyin. 2-3 dakika ara ara karıştırarak, pişirmeye devam edin. Ardından tencereye 1 tatlı kaşığı domates salçası ve 1 adet orta boy ince ince doğranmış domates koyup, 1-2 dakika güzelce kavurun. Son olarak tavuklu harcın üzerine damak tadınıza göre tuz ve karabiber koyup, karıştırın. Tencerenin kapağını kapatıp, kısık ateş üzerinde domatesler yumuşayana kadar pişirin.Diğer tarafta 2 adet orta boy patatesin kabuğunu soyup, çok küçük olmayacak şekilde küpler halinde doğrayın. Doğradığınız patatesleri kızgın yağ içinde güzelce kızartın. Daha sonra patateslerin yağını iyice süzüp, hazırladığınız tavuklu harcın üzerine koyun. Tüm harcı güzelce karıştırıp, kenara alın.Daha sonra 4 adet orta büyüklükteki kemer patlıcanı alacalı olarak soyup, sap kısımlarını kesin. Bıçak yardımı ile uzunlamasına çok kalın ve ince olmayacak şekilde dilimleyin. Dilimlediğiniz patlıcanları kızgın yağ içinde arkalı önlü kızartın. Kızaran patlıcanları kağıt havlu üzerine alıp, yağlarının süzülmesini sağlayın.Diğer tarafta kızarttığınız patlıcanlardan 6 dilimini alıp, yarısı dışarıda kalacak şekilde orta boy bir kase içine biraz aralıklı olacak şekilde dizin. Patlıcanların orta kısmına tavuklu patates harcından koyun. Dışarı sarkan patlıcanları harcın üzerine güzelce kapatın. Ardından kaseyi diğer eliniz ile tutarak dikkatli bir şekilde ters çevirin. Kaseden çıkan tavuklu zade kebabını bir fırın kabı içine koyun. Üzerlerine rendelenmiş kaşar peynir serpiştirin. Önceden ısıtılmış 190 derece fırına verin. Üzeri hafifçe kızarana kadar yaklaşık 15 dakika pişirin.Tavuklu zade kebabı piştikten sonra fırından çıkartıp, sıcak olarak servis edebilirsiniz." # Data Instances for datav3.txt Sodalı Köfte nasıl yapılır? Sodalı Köfte için gerekli malzemeler: 500 gr kıyma 1 adet büyük boy kuru soğan 1/2 çay bardağıgaleta unu 1 tatlı kaşığı tuz 1 çay kaşığı dolusu kırmızı toz biber 1 çay kaşığı kırmızı pul biber 1 çay kaşığı kimyon 1/2 çay kaşığı karabiber 1/2 paket kabartma tozu 1 çay bardağı soda Sodalı Köfte Yapılışı: Sodalı köfte yapımı için derin bir kap içine 1 adet büyük boy soğan rendeleyin. Rendelediğiniz soğanın suyu varsa suyunu süzün. Ardından üzerine yarım kilo kıyma koyun. Daha sonra kaba yarım çay bardağı galeta unu, 1 tatlı kaşığı tuz, 1 çay kaşığı dolusu kırmızı toz biber, 1 çay kaşığı kırmızı pul biber, 1 çay kaşığı kimyon, yarım çay kaşığı karabiber ve yarım paket kabartma tozu koyun. Son olarak köfteli harcın üzerine 1 çay bardağı soda dökün. Tüm köfte harcını eliniz ile iyice yoğurun. Hazırladığınız sodalı köfte harcını buzdolabından en az 1 saat dinlenmeye bırakın.Daha sonra dinlenen köfte harcından ceviz büyüklüğünde parçalar alıp, elinizde yuvarlak ya da oval şeklini verin. Şekil verdiğiniz köfteleri yağlı kağıt serili fırın tepsisi içine dizin. Köftelerin yanına isteğe göre birkaç domates ve biber koyabilirsiniz.Sodalı köfteleri önceden ısıtılmış 200 derece fırına verin. Üzerleri güzelce kızarana kadar pişirin.Fırında sodalı köfteleriniz piştikten sonra sıcak olarak servis edebilirsiniz. # COLLECTION METHODOLOGY Python Web Scraping with BeautifulSoup # Source The source of the recipes is https://ye-mek.net	mertbozkurt/turkish-recipe	[ "task_categories:question-answering", "task_categories:conversational", "task_categories:text-generation", "size_categories:1K<n<10K", "language:tr", "license:mit", "region:us" ]	2023-12-17T17:16:26+00:00	{"language": ["tr"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "conversational", "text-generation"]}	2023-12-19T17:37:52+00:00	[]	[ "tr" ]	TAGS #task_categories-question-answering #task_categories-conversational #task_categories-text-generation #size_categories-1K<n<10K #language-Turkish #license-mit #region-us	# Datasets Summary The data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it. # Languages The dataset is based on Turkish. # Data Instances for URL * Title : Tavuklu Zade Kebabı, * Link: URL * Category: Ana-Yemek, * Materials: "['4 adet orta boy kemer patlıcan', '500 gr kuşbaşı doğranmış tavuk göğsü', '2 adet orta boy patates', '1 adet orta boy soğan', '2 adet yeşil biber', '1 adet orta boy domates', '2 diş sarımsak', '1 tatlı kaşığıdomates salçası', '5 yemek kaşığı zeytinyağı', 'Tuz', 'Karabiber', 'Üzeri İçin:', 'Rendelenmiş kaşar peynir']", * How to do: "Tavuklu zade kebabı yapımı için; geniş bir tencere içine 4-5 yemek kaşığı zeytinyağı döküp, ısıtın. Isınan yağın üzerine 500 gr kuşbaşı doğranmış tavuk etini koyun. Suyunu salıp, hafifçe çekene kadar pişirin.Daha sonra tavuk etlerinin üzerine 1 adet orta boy ince ince doğranmış soğan ve 2 adet küçük küçük doğranmış yeşil biberi ekleyin. 2-3 dakika ara ara karıştırarak, pişirmeye devam edin. Ardından tencereye 1 tatlı kaşığı domates salçası ve 1 adet orta boy ince ince doğranmış domates koyup, 1-2 dakika güzelce kavurun. Son olarak tavuklu harcın üzerine damak tadınıza göre tuz ve karabiber koyup, karıştırın. Tencerenin kapağını kapatıp, kısık ateş üzerinde domatesler yumuşayana kadar pişirin.Diğer tarafta 2 adet orta boy patatesin kabuğunu soyup, çok küçük olmayacak şekilde küpler halinde doğrayın. Doğradığınız patatesleri kızgın yağ içinde güzelce kızartın. Daha sonra patateslerin yağını iyice süzüp, hazırladığınız tavuklu harcın üzerine koyun. Tüm harcı güzelce karıştırıp, kenara alın.Daha sonra 4 adet orta büyüklükteki kemer patlıcanı alacalı olarak soyup, sap kısımlarını kesin. Bıçak yardımı ile uzunlamasına çok kalın ve ince olmayacak şekilde dilimleyin. Dilimlediğiniz patlıcanları kızgın yağ içinde arkalı önlü kızartın. Kızaran patlıcanları kağıt havlu üzerine alıp, yağlarının süzülmesini sağlayın.Diğer tarafta kızarttığınız patlıcanlardan 6 dilimini alıp, yarısı dışarıda kalacak şekilde orta boy bir kase içine biraz aralıklı olacak şekilde dizin. Patlıcanların orta kısmına tavuklu patates harcından koyun. Dışarı sarkan patlıcanları harcın üzerine güzelce kapatın. Ardından kaseyi diğer eliniz ile tutarak dikkatli bir şekilde ters çevirin. Kaseden çıkan tavuklu zade kebabını bir fırın kabı içine koyun. Üzerlerine rendelenmiş kaşar peynir serpiştirin. Önceden ısıtılmış 190 derece fırına verin. Üzeri hafifçe kızarana kadar yaklaşık 15 dakika pişirin.Tavuklu zade kebabı piştikten sonra fırından çıkartıp, sıcak olarak servis edebilirsiniz." # Data Instances for URL Sodalı Köfte nasıl yapılır? Sodalı Köfte için gerekli malzemeler: 500 gr kıyma 1 adet büyük boy kuru soğan 1/2 çay bardağıgaleta unu 1 tatlı kaşığı tuz 1 çay kaşığı dolusu kırmızı toz biber 1 çay kaşığı kırmızı pul biber 1 çay kaşığı kimyon 1/2 çay kaşığı karabiber 1/2 paket kabartma tozu 1 çay bardağı soda Sodalı Köfte Yapılışı: Sodalı köfte yapımı için derin bir kap içine 1 adet büyük boy soğan rendeleyin. Rendelediğiniz soğanın suyu varsa suyunu süzün. Ardından üzerine yarım kilo kıyma koyun. Daha sonra kaba yarım çay bardağı galeta unu, 1 tatlı kaşığı tuz, 1 çay kaşığı dolusu kırmızı toz biber, 1 çay kaşığı kırmızı pul biber, 1 çay kaşığı kimyon, yarım çay kaşığı karabiber ve yarım paket kabartma tozu koyun. Son olarak köfteli harcın üzerine 1 çay bardağı soda dökün. Tüm köfte harcını eliniz ile iyice yoğurun. Hazırladığınız sodalı köfte harcını buzdolabından en az 1 saat dinlenmeye bırakın.Daha sonra dinlenen köfte harcından ceviz büyüklüğünde parçalar alıp, elinizde yuvarlak ya da oval şeklini verin. Şekil verdiğiniz köfteleri yağlı kağıt serili fırın tepsisi içine dizin. Köftelerin yanına isteğe göre birkaç domates ve biber koyabilirsiniz.Sodalı köfteleri önceden ısıtılmış 200 derece fırına verin. Üzerleri güzelce kızarana kadar pişirin.Fırında sodalı köfteleriniz piştikten sonra sıcak olarak servis edebilirsiniz. # COLLECTION METHODOLOGY Python Web Scraping with BeautifulSoup # Source The source of the recipes is URL	[ "# Datasets Summary\nThe data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it.", "# Languages\nThe dataset is based on Turkish.", "# Data Instances for URL\n* Title : Tavuklu Zade Kebabı,\n* Link: URL\n* Category: Ana-Yemek,\n* Materials: \"['4 adet orta boy kemer patlıcan', '500 gr kuşbaşı doğranmış tavuk göğsü', '2 adet orta boy patates', '1 adet orta boy soğan', '2 adet yeşil biber', '1 adet orta boy domates', '2 diş sarımsak', '1 tatlı kaşığıdomates salçası', '5 yemek kaşığı zeytinyağı', 'Tuz', 'Karabiber', 'Üzeri İçin:', 'Rendelenmiş kaşar peynir']\",\n* How to do: \"Tavuklu zade kebabı yapımı için; geniş bir tencere içine 4-5 yemek kaşığı zeytinyağı döküp, ısıtın. Isınan yağın üzerine 500 gr kuşbaşı doğranmış tavuk etini koyun. Suyunu salıp, hafifçe çekene kadar pişirin.Daha sonra tavuk etlerinin üzerine 1 adet orta boy ince ince doğranmış soğan ve 2 adet küçük küçük doğranmış yeşil biberi ekleyin. 2-3 dakika ara ara karıştırarak, pişirmeye devam edin. Ardından tencereye 1 tatlı kaşığı domates salçası ve 1 adet orta boy ince ince doğranmış domates koyup, 1-2 dakika güzelce kavurun. Son olarak tavuklu harcın üzerine damak tadınıza göre tuz ve karabiber koyup, karıştırın. Tencerenin kapağını kapatıp, kısık ateş üzerinde domatesler yumuşayana kadar pişirin.Diğer tarafta 2 adet orta boy patatesin kabuğunu soyup, çok küçük olmayacak şekilde küpler halinde doğrayın. Doğradığınız patatesleri kızgın yağ içinde güzelce kızartın. Daha sonra patateslerin yağını iyice süzüp, hazırladığınız tavuklu harcın üzerine koyun. Tüm harcı güzelce karıştırıp, kenara alın.Daha sonra 4 adet orta büyüklükteki kemer patlıcanı alacalı olarak soyup, sap kısımlarını kesin. Bıçak yardımı ile uzunlamasına çok kalın ve ince olmayacak şekilde dilimleyin. Dilimlediğiniz patlıcanları kızgın yağ içinde arkalı önlü kızartın. Kızaran patlıcanları kağıt havlu üzerine alıp, yağlarının süzülmesini sağlayın.Diğer tarafta kızarttığınız patlıcanlardan 6 dilimini alıp, yarısı dışarıda kalacak şekilde orta boy bir kase içine biraz aralıklı olacak şekilde dizin. Patlıcanların orta kısmına tavuklu patates harcından koyun. Dışarı sarkan patlıcanları harcın üzerine güzelce kapatın. Ardından kaseyi diğer eliniz ile tutarak dikkatli bir şekilde ters çevirin. Kaseden çıkan tavuklu zade kebabını bir fırın kabı içine koyun. Üzerlerine rendelenmiş kaşar peynir serpiştirin. Önceden ısıtılmış 190 derece fırına verin. Üzeri hafifçe kızarana kadar yaklaşık 15 dakika pişirin.Tavuklu zade kebabı piştikten sonra fırından çıkartıp, sıcak olarak servis edebilirsiniz.\"", "# Data Instances for URL\nSodalı Köfte nasıl yapılır?\n\nSodalı Köfte için gerekli malzemeler: \n 500 gr kıyma\n 1 adet büyük boy kuru soğan\n 1/2 çay bardağıgaleta unu\n 1 tatlı kaşığı tuz\n 1 çay kaşığı dolusu kırmızı toz biber\n 1 çay kaşığı kırmızı pul biber\n 1 çay kaşığı kimyon \n 1/2 çay kaşığı karabiber\n 1/2 paket kabartma tozu\n 1 çay bardağı soda\n \nSodalı Köfte Yapılışı: \nSodalı köfte yapımı için derin bir kap içine 1 adet büyük boy soğan rendeleyin.\nRendelediğiniz soğanın suyu varsa suyunu süzün.\nArdından üzerine yarım kilo kıyma koyun.\nDaha sonra kaba yarım çay bardağı galeta unu, 1 tatlı kaşığı tuz, 1 çay kaşığı dolusu kırmızı toz biber, 1 çay kaşığı kırmızı pul biber, 1 çay kaşığı kimyon, yarım çay kaşığı karabiber ve yarım paket kabartma tozu koyun.\nSon olarak köfteli harcın üzerine 1 çay bardağı soda dökün.\nTüm köfte harcını eliniz ile iyice yoğurun.\nHazırladığınız sodalı köfte harcını buzdolabından en az 1 saat dinlenmeye bırakın.Daha sonra dinlenen köfte harcından ceviz büyüklüğünde parçalar alıp, elinizde yuvarlak ya da oval şeklini verin.\nŞekil verdiğiniz köfteleri yağlı kağıt serili fırın tepsisi içine dizin.\nKöftelerin yanına isteğe göre birkaç domates ve biber koyabilirsiniz.Sodalı köfteleri önceden ısıtılmış 200 derece fırına verin.\nÜzerleri güzelce kızarana kadar pişirin.Fırında sodalı köfteleriniz piştikten sonra sıcak olarak servis edebilirsiniz.", "# COLLECTION METHODOLOGY\nPython Web Scraping with BeautifulSoup", "# Source\nThe source of the recipes is URL" ]	[ "TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text-generation #size_categories-1K<n<10K #language-Turkish #license-mit #region-us \n", "# Datasets Summary\nThe data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it.", "# Languages\nThe dataset is based on Turkish.", "# Data Instances for URL\n* Title : Tavuklu Zade Kebabı,\n* Link: URL\n* Category: Ana-Yemek,\n* Materials: \"['4 adet orta boy kemer patlıcan', '500 gr kuşbaşı doğranmış tavuk göğsü', '2 adet orta boy patates', '1 adet orta boy soğan', '2 adet yeşil biber', '1 adet orta boy domates', '2 diş sarımsak', '1 tatlı kaşığıdomates salçası', '5 yemek kaşığı zeytinyağı', 'Tuz', 'Karabiber', 'Üzeri İçin:', 'Rendelenmiş kaşar peynir']\",\n* How to do: \"Tavuklu zade kebabı yapımı için; geniş bir tencere içine 4-5 yemek kaşığı zeytinyağı döküp, ısıtın. Isınan yağın üzerine 500 gr kuşbaşı doğranmış tavuk etini koyun. Suyunu salıp, hafifçe çekene kadar pişirin.Daha sonra tavuk etlerinin üzerine 1 adet orta boy ince ince doğranmış soğan ve 2 adet küçük küçük doğranmış yeşil biberi ekleyin. 2-3 dakika ara ara karıştırarak, pişirmeye devam edin. Ardından tencereye 1 tatlı kaşığı domates salçası ve 1 adet orta boy ince ince doğranmış domates koyup, 1-2 dakika güzelce kavurun. Son olarak tavuklu harcın üzerine damak tadınıza göre tuz ve karabiber koyup, karıştırın. Tencerenin kapağını kapatıp, kısık ateş üzerinde domatesler yumuşayana kadar pişirin.Diğer tarafta 2 adet orta boy patatesin kabuğunu soyup, çok küçük olmayacak şekilde küpler halinde doğrayın. Doğradığınız patatesleri kızgın yağ içinde güzelce kızartın. Daha sonra patateslerin yağını iyice süzüp, hazırladığınız tavuklu harcın üzerine koyun. Tüm harcı güzelce karıştırıp, kenara alın.Daha sonra 4 adet orta büyüklükteki kemer patlıcanı alacalı olarak soyup, sap kısımlarını kesin. Bıçak yardımı ile uzunlamasına çok kalın ve ince olmayacak şekilde dilimleyin. Dilimlediğiniz patlıcanları kızgın yağ içinde arkalı önlü kızartın. Kızaran patlıcanları kağıt havlu üzerine alıp, yağlarının süzülmesini sağlayın.Diğer tarafta kızarttığınız patlıcanlardan 6 dilimini alıp, yarısı dışarıda kalacak şekilde orta boy bir kase içine biraz aralıklı olacak şekilde dizin. Patlıcanların orta kısmına tavuklu patates harcından koyun. Dışarı sarkan patlıcanları harcın üzerine güzelce kapatın. Ardından kaseyi diğer eliniz ile tutarak dikkatli bir şekilde ters çevirin. Kaseden çıkan tavuklu zade kebabını bir fırın kabı içine koyun. Üzerlerine rendelenmiş kaşar peynir serpiştirin. Önceden ısıtılmış 190 derece fırına verin. Üzeri hafifçe kızarana kadar yaklaşık 15 dakika pişirin.Tavuklu zade kebabı piştikten sonra fırından çıkartıp, sıcak olarak servis edebilirsiniz.\"", "# Data Instances for URL\nSodalı Köfte nasıl yapılır?\n\nSodalı Köfte için gerekli malzemeler: \n 500 gr kıyma\n 1 adet büyük boy kuru soğan\n 1/2 çay bardağıgaleta unu\n 1 tatlı kaşığı tuz\n 1 çay kaşığı dolusu kırmızı toz biber\n 1 çay kaşığı kırmızı pul biber\n 1 çay kaşığı kimyon \n 1/2 çay kaşığı karabiber\n 1/2 paket kabartma tozu\n 1 çay bardağı soda\n \nSodalı Köfte Yapılışı: \nSodalı köfte yapımı için derin bir kap içine 1 adet büyük boy soğan rendeleyin.\nRendelediğiniz soğanın suyu varsa suyunu süzün.\nArdından üzerine yarım kilo kıyma koyun.\nDaha sonra kaba yarım çay bardağı galeta unu, 1 tatlı kaşığı tuz, 1 çay kaşığı dolusu kırmızı toz biber, 1 çay kaşığı kırmızı pul biber, 1 çay kaşığı kimyon, yarım çay kaşığı karabiber ve yarım paket kabartma tozu koyun.\nSon olarak köfteli harcın üzerine 1 çay bardağı soda dökün.\nTüm köfte harcını eliniz ile iyice yoğurun.\nHazırladığınız sodalı köfte harcını buzdolabından en az 1 saat dinlenmeye bırakın.Daha sonra dinlenen köfte harcından ceviz büyüklüğünde parçalar alıp, elinizde yuvarlak ya da oval şeklini verin.\nŞekil verdiğiniz köfteleri yağlı kağıt serili fırın tepsisi içine dizin.\nKöftelerin yanına isteğe göre birkaç domates ve biber koyabilirsiniz.Sodalı köfteleri önceden ısıtılmış 200 derece fırına verin.\nÜzerleri güzelce kızarana kadar pişirin.Fırında sodalı köfteleriniz piştikten sonra sıcak olarak servis edebilirsiniz.", "# COLLECTION METHODOLOGY\nPython Web Scraping with BeautifulSoup", "# Source\nThe source of the recipes is URL" ]	[ 62, 35, 12, 706, 368, 17, 10 ]	[ "passage: TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text-generation #size_categories-1K<n<10K #language-Turkish #license-mit #region-us \n# Datasets Summary\nThe data set contains Turkish food recipes. It includes: title, url, category, required materials and how to make it.# Languages\nThe dataset is based on Turkish." ]
a4892bfed9687a37c704527a05e57e90e94cc091	# Dataset Card for "hardhat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	hf-vision/hardhat	[ "region:us" ]	2023-12-17T17:18:12+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "id", "sequence": "int64"}, {"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 207310502.085, "num_examples": 5297}, {"name": "test", "num_bytes": 67741175.806, "num_examples": 1766}], "download_size": 266886357, "dataset_size": 275051677.89100003}}	2023-12-17T18:29:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for "hardhat" More Information needed	[ "# Dataset Card for \"hardhat\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"hardhat\"\n\nMore Information needed" ]	[ 6, 12 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"hardhat\"\n\nMore Information needed" ]
b865b11fd8a07ba1ccd6adcba2ac47435ac6edd1	# Dataset of lunacub (Arknights) This is the dataset of lunacub (Arknights), containing 38 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 38 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 104 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 114 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 38 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 38 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 38 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 104 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 104 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 87 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 114 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 114 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|	AppleHarem/lunacub_arknights	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-12-17T17:25:26+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-12-17T17:25:31+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of lunacub (Arknights) ============================== This is the dataset of lunacub (Arknights), containing 38 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). This is a WebUI contains crawlers and other thing: (LittleAppleWebUI)	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
4d57e1ba0185970376a2b94a3417ed671819689f	# Dataset Card for "gunshot_triangulation_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/gunshot_triangulation_extract_unit	[ "region:us" ]	2023-12-17T17:40:51+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 214680, "num_examples": 88}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 214680, "num_examples": 88}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 318872, "num_examples": 88}, {"name": "audiodec_24k_320d", "num_bytes": 680728, "num_examples": 88}, {"name": "dac_16k", "num_bytes": 1442456, "num_examples": 88}, {"name": "dac_24k", "num_bytes": 4000792, "num_examples": 88}, {"name": "dac_44k", "num_bytes": 1373816, "num_examples": 88}, {"name": "encodec_24k", "num_bytes": 161880, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 1725464, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 1725464, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 1702936, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 869400, "num_examples": 88}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 1702936, "num_examples": 88}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 1702936, "num_examples": 88}, {"name": "speech_tokenizer_16k", "num_bytes": 427288, "num_examples": 88}], "download_size": 2845431, "dataset_size": 18264328}}	2023-12-17T17:41:24+00:00	[]	[]	TAGS #region-us	# Dataset Card for "gunshot_triangulation_extract_unit" More Information needed	[ "# Dataset Card for \"gunshot_triangulation_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"gunshot_triangulation_extract_unit\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"gunshot_triangulation_extract_unit\"\n\nMore Information needed" ]
f6421855fda9cea145621afffff52e57d89f07b7	# Dataset Card for "libricount_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/libricount_extract_unit	[ "region:us" ]	2023-12-17T17:43:52+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 45943560, "num_examples": 5720}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 45943560, "num_examples": 5720}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 68823560, "num_examples": 5720}, {"name": "audiodec_24k_320d", "num_bytes": 146707080, "num_examples": 5720}, {"name": "dac_16k", "num_bytes": 280417800, "num_examples": 5720}, {"name": "dac_24k", "num_bytes": 778378120, "num_examples": 5720}, {"name": "dac_44k", "num_bytes": 237517800, "num_examples": 5720}, {"name": "encodec_24k", "num_bytes": 34549320, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 368368520, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 368368520, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 366904200, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 183864200, "num_examples": 5720}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 366904200, "num_examples": 5720}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 366904200, "num_examples": 5720}, {"name": "speech_tokenizer_16k", "num_bytes": 91795080, "num_examples": 5720}], "download_size": 593516002, "dataset_size": 3751389720}}	2023-12-17T17:45:25+00:00	[]	[]	TAGS #region-us	# Dataset Card for "libricount_extract_unit" More Information needed	[ "# Dataset Card for \"libricount_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"libricount_extract_unit\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"libricount_extract_unit\"\n\nMore Information needed" ]
f5376a6d1110f191613d6b144ff135a8aea8024e	# Dataset of vigil (Arknights) This is the dataset of vigil (Arknights), containing 16 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 16 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 41 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 46 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 16 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 16 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 16 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 41 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 41 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 37 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 46 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 46 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|	AppleHarem/vigil_arknights	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-12-17T18:02:26+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-12-17T18:02:29+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of vigil (Arknights) ============================ This is the dataset of vigil (Arknights), containing 16 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). This is a WebUI contains crawlers and other thing: (LittleAppleWebUI)	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
e3235c6765c6725445acd2628a7231626f5264a2	Question and answer pairs for the first 100 entries of aerospace mechanism symposia 5000 word chunk entries. Full file of entries is here: https://github.com/dsmueller3760/aerospace_chatbot/blob/llm_training/data/AMS/ams_data_answers.jsonl See this repository for details: https://github.com/dsmueller3760/aerospace_chatbot/tree/main Prompts generated using TheBloke/Llama-2-7B-Chat-GGUF Format representative of mistral's instruct llms: * https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 * Example dataset: https://huggingface.co/datasets/centroIA/MistralInstructScenarios `<s>[INST] {prompt} [/INST]`	ai-aerospace/ams_data_train_mistral_v0.1_100	[ "license:apache-2.0", "region:us" ]	2023-12-17T18:06:34+00:00	{"license": "apache-2.0", "base-model": "TheBloke/Llama-2-7B-Chat-GGUF"}	2023-12-17T19:56:35+00:00	[]	[]	TAGS #license-apache-2.0 #region-us	Question and answer pairs for the first 100 entries of aerospace mechanism symposia 5000 word chunk entries. Full file of entries is here: URL See this repository for details: URL Prompts generated using TheBloke/Llama-2-7B-Chat-GGUF Format representative of mistral's instruct llms: * URL * Example dataset: URL '<s>[INST] {prompt} [/INST]'	[]	[ "TAGS\n#license-apache-2.0 #region-us \n" ]	[ 14 ]	[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
4d6c240da6ee953f7a25406abb8570d211c4dc92	# Clustered Protein-Protein Complexes This dataset is similar to `AmelieSchreiber/pha_clustered_protein_complexes`, but is larger. The methods used for clustering are the same, with different hyperparameters. The threshold percentage for DBSCAN used to create the clusters is `0.35`.	AmelieSchreiber/pha_clustered_protein_complexes_30K	[ "license:mit", "region:us" ]	2023-12-17T18:27:19+00:00	{"license": "mit"}	2023-12-17T18:30:34+00:00	[]	[]	TAGS #license-mit #region-us	# Clustered Protein-Protein Complexes This dataset is similar to 'AmelieSchreiber/pha_clustered_protein_complexes', but is larger. The methods used for clustering are the same, with different hyperparameters. The threshold percentage for DBSCAN used to create the clusters is '0.35'.	[ "# Clustered Protein-Protein Complexes\nThis dataset is similar to 'AmelieSchreiber/pha_clustered_protein_complexes', but is larger. The methods used for clustering are the same, \nwith different hyperparameters. The threshold percentage for DBSCAN used to create the clusters is '0.35'." ]	[ "TAGS\n#license-mit #region-us \n", "# Clustered Protein-Protein Complexes\nThis dataset is similar to 'AmelieSchreiber/pha_clustered_protein_complexes', but is larger. The methods used for clustering are the same, \nwith different hyperparameters. The threshold percentage for DBSCAN used to create the clusters is '0.35'." ]	[ 11, 81 ]	[ "passage: TAGS\n#license-mit #region-us \n# Clustered Protein-Protein Complexes\nThis dataset is similar to 'AmelieSchreiber/pha_clustered_protein_complexes', but is larger. The methods used for clustering are the same, \nwith different hyperparameters. The threshold percentage for DBSCAN used to create the clusters is '0.35'." ]
8b521febedb2f41518ff4a3df3ff18f1e4cba7dd	# Dataset of firewhistle (Arknights) This is the dataset of firewhistle (Arknights), containing 15 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 15 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 41 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 45 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 15 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 15 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 15 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 41 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 41 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 34 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 45 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 45 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|	AppleHarem/firewhistle_arknights	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-12-17T18:44:22+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-12-17T18:44:25+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of firewhistle (Arknights) ================================== This is the dataset of firewhistle (Arknights), containing 15 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). This is a WebUI contains crawlers and other thing: (LittleAppleWebUI)	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
6f6df29324a61109722c156240db734f4f2bcf63	## Dataset Description Microsoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K Java open source project hosted on GitHub. This is smaller subset of the assembled version of the methods2test dataset. It provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available. The subset is created by randomly selecting only one sample from each of the 91k projects. The mapping between test case and focal methods are based heuristics rules and Java developer's best practice. More information could be found here: - [methods2test Github repo](https://github.com/microsoft/methods2test) - [Methods2Test: A dataset of focal methods mapped to test cases](https://arxiv.org/pdf/2203.12776.pdf) ## Dataset Schema ``` t: <TEST_CASE> t+tc: <TEST_CASE> <TEST_CLASS_NAME> fm: <FOCAL_METHOD> fm+fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD> fm+fc+c: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> fm+fc+c+m: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> fm+fc+c+m+f: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS> ``` ## Focal Context - fm: this representation incorporates exclusively the source code of the focal method. Intuitively, this contains the most important information for generating accurate test cases for the given method. - fm+fc: this representations adds the focal class name, which can provide meaningful semantic information to the model. - fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this augmentation is that the test case may require instantiating an object of the focal class in order to properly test the focal method. - fm+fc+c+m: this representation adds the signatures of the other public methods in the focal class. The rationale which motivated this inclusion is that the test case may need to invoke other auxiliary methods within the class (e.g., getters, setters) to set up or tear down the testing environment. - fm+fc+c+m+f : this representation adds the public fields of the focal class. The motivation is that test cases may need to inspect the status of the public fields to properly test a focal method. ![image/png](https://huggingface.co/datasets/andstor/methods2test/resolve/main/figure-1-focal-context.png) The different levels of focal contexts are the following: ``` T: test case t+tc: test case + test class name FM: focal method fm+fc: focal method + focal class name fm+fc+c: focal method + focal class name + constructor signatures fm+fc+c+m: focal method + focal class name + constructor signatures + public method signatures fm+fc+c+m+f: focal method + focal class name + constructor signatures + public method signatures + public fields ``` ## Limitations The original authors validate the heuristics by inspecting a statistically significant sample (confidence level of 95% within 10% margin of error) of 97 samples from the training set. Two authors independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct link between the test case and the corresponding focal method ## Contribution All thanks to the original authors.	andstor/methods2test_small	[ "task_categories:text-generation", "language:en", "license:mit", "unit test", "java", "code", "arxiv:2203.12776", "region:us" ]	2023-12-17T20:26:53+00:00	{"language": ["en"], "license": "mit", "task_categories": ["text-generation"], "configs": [{"config_name": "fm", "data_files": [{"split": "train", "path": "data/fm/train-"}, {"split": "test", "path": "data/fm/test-"}, {"split": "validation", "path": "data/fm/validation-"}]}, {"config_name": "fm_indented", "data_files": [{"split": "train", "path": "data/fm_indented/train-"}, {"split": "test", "path": "data/fm_indented/test-"}, {"split": "validation", "path": "data/fm_indented/validation-"}]}, {"config_name": "fm+t", "data_files": [{"split": "train", "path": "data/fm+t/train-"}, {"split": "test", "path": "data/fm+t/test-"}, {"split": "validation", "path": "data/fm+t/validation-"}]}, {"config_name": "fm+fc", "data_files": [{"split": "train", "path": "data/fm+fc/train-"}, {"split": "test", "path": "data/fm+fc/test-"}, {"split": "validation", "path": "data/fm+fc/validation-"}]}, {"config_name": "fm+fc+t+tc", "data_files": [{"split": "train", "path": "data/fm+fc+t+tc/train-"}, {"split": "test", "path": "data/fm+fc+t+tc/test-"}, {"split": "validation", "path": "data/fm+fc+t+tc/validation-"}]}, {"config_name": "fm+fc+c", "data_files": [{"split": "train", "path": "data/fm+fc+c/train-"}, {"split": "test", "path": "data/fm+fc+c/test-"}, {"split": "validation", "path": "data/fm+fc+c/validation-"}]}, {"config_name": "fm+fc+c+t+tc", "data_files": [{"split": "train", "path": "data/fm+fc+c+t+tc/train-"}, {"split": "test", "path": "data/fm+fc+c+t+tc/test-"}, {"split": "validation", "path": "data/fm+fc+c+t+tc/validation-"}]}, {"config_name": "fm+fc+c+m", "data_files": [{"split": "train", "path": "data/fm+fc+c+m/train-"}, {"split": "test", "path": "data/fm+fc+c+m/test-"}, {"split": "validation", "path": "data/fm+fc+c+m/validation-"}]}, {"config_name": "fm+fc+c+m+t+tc", "data_files": [{"split": "train", "path": "data/fm+fc+c+m+t+tc/train-"}, {"split": "test", "path": "data/fm+fc+c+m+t+tc/test-"}, {"split": "validation", "path": "data/fm+fc+c+m+t+tc/validation-"}]}, {"config_name": "fm+fc+c+m+f", "data_files": [{"split": "train", "path": "data/fm+fc+c+m+f/train-"}, {"split": "test", "path": "data/fm+fc+c+m+f/test-"}, {"split": "validation", "path": "data/fm+fc+c+m+f/validation-"}]}, {"config_name": "fm+fc+c+m+f+t+tc", "data_files": [{"split": "train", "path": "data/fm+fc+c+m+f+t+tc/train-"}, {"split": "test", "path": "data/fm+fc+c+m+f+t+tc/test-"}, {"split": "validation", "path": "data/fm+fc+c+m+f+t+tc/validation-"}]}, {"config_name": "t", "data_files": [{"split": "train", "path": "data/t/train-"}, {"split": "test", "path": "data/t/test-"}, {"split": "validation", "path": "data/t/validation-"}]}, {"config_name": 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{"name": "validation", "num_bytes": 689647, "num_examples": 953}], "download_size": 2434024, "dataset_size": 5588009}, {"config_name": "t+tc", "features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5648321, "num_examples": 7440}, {"name": "test", "num_bytes": 761386, "num_examples": 1017}, {"name": "validation", "num_bytes": 867350, "num_examples": 953}], "download_size": 3024686, "dataset_size": 7277057}, {"config_name": "t_indented", "features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4606253, "num_examples": 7440}, {"name": "test", "num_bytes": 623576, "num_examples": 1017}, {"name": "validation", "num_bytes": 734221, "num_examples": 953}], "download_size": 2496661, "dataset_size": 5964050}], "tags": ["unit test", "java", "code"]}	2023-12-23T02:25:15+00:00	[ "2203.12776" ]	[ "en" ]	TAGS #task_categories-text-generation #language-English #license-mit #unit test #java #code #arxiv-2203.12776 #region-us	## Dataset Description Microsoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K Java open source project hosted on GitHub. This is smaller subset of the assembled version of the methods2test dataset. It provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available. The subset is created by randomly selecting only one sample from each of the 91k projects. The mapping between test case and focal methods are based heuristics rules and Java developer's best practice. More information could be found here: - methods2test Github repo - Methods2Test: A dataset of focal methods mapped to test cases ## Dataset Schema ## Focal Context - fm: this representation incorporates exclusively the source code of the focal method. Intuitively, this contains the most important information for generating accurate test cases for the given method. - fm+fc: this representations adds the focal class name, which can provide meaningful semantic information to the model. - fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this augmentation is that the test case may require instantiating an object of the focal class in order to properly test the focal method. - fm+fc+c+m: this representation adds the signatures of the other public methods in the focal class. The rationale which motivated this inclusion is that the test case may need to invoke other auxiliary methods within the class (e.g., getters, setters) to set up or tear down the testing environment. - fm+fc+c+m+f : this representation adds the public fields of the focal class. The motivation is that test cases may need to inspect the status of the public fields to properly test a focal method. !image/png The different levels of focal contexts are the following: ## Limitations The original authors validate the heuristics by inspecting a statistically significant sample (confidence level of 95% within 10% margin of error) of 97 samples from the training set. Two authors independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct link between the test case and the corresponding focal method ## Contribution All thanks to the original authors.	[ "## Dataset Description\nMicrosoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. \nIt contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K\nJava open source project hosted on GitHub.\n\nThis is smaller subset of the assembled version of the methods2test dataset.\nIt provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available.\nThe subset is created by randomly selecting only one sample from each of the 91k projects.\n\n\nThe mapping between test case and focal methods are based heuristics rules and Java developer's best practice.\n\nMore information could be found here:\n- methods2test Github repo\n- Methods2Test: A dataset of focal methods mapped to test cases", "## Dataset Schema", "## Focal Context\n- fm: this representation incorporates exclusively the source\ncode of the focal method. Intuitively, this contains the most\nimportant information for generating accurate test cases for\nthe given method.\n- fm+fc: this representations adds the focal class name, which\ncan provide meaningful semantic information to the model.\n- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this\naugmentation is that the test case may require instantiating\nan object of the focal class in order to properly test the focal\nmethod.\n- fm+fc+c+m: this representation adds the signatures of the\nother public methods in the focal class. The rationale which\nmotivated this inclusion is that the test case may need to\ninvoke other auxiliary methods within the class (e.g., getters,\nsetters) to set up or tear down the testing environment.\n- fm+fc+c+m+f : this representation adds the public fields of\nthe focal class. The motivation is that test cases may need to\ninspect the status of the public fields to properly test a focal\nmethod.\n\n!image/png\n\n The different levels of focal contexts are the following:", "## Limitations\nThe original authors validate the heuristics by inspecting a\nstatistically significant sample (confidence level of 95% within 10%\nmargin of error) of 97 samples from the training set. Two authors\nindependently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct\nlink between the test case and the corresponding focal method", "## Contribution\nAll thanks to the original authors." ]	[ "TAGS\n#task_categories-text-generation #language-English #license-mit #unit test #java #code #arxiv-2203.12776 #region-us \n", "## Dataset Description\nMicrosoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. \nIt contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K\nJava open source project hosted on GitHub.\n\nThis is smaller subset of the assembled version of the methods2test dataset.\nIt provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available.\nThe subset is created by randomly selecting only one sample from each of the 91k projects.\n\n\nThe mapping between test case and focal methods are based heuristics rules and Java developer's best practice.\n\nMore information could be found here:\n- methods2test Github repo\n- Methods2Test: A dataset of focal methods mapped to test cases", "## Dataset Schema", "## Focal Context\n- fm: this representation incorporates exclusively the source\ncode of the focal method. Intuitively, this contains the most\nimportant information for generating accurate test cases for\nthe given method.\n- fm+fc: this representations adds the focal class name, which\ncan provide meaningful semantic information to the model.\n- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this\naugmentation is that the test case may require instantiating\nan object of the focal class in order to properly test the focal\nmethod.\n- fm+fc+c+m: this representation adds the signatures of the\nother public methods in the focal class. The rationale which\nmotivated this inclusion is that the test case may need to\ninvoke other auxiliary methods within the class (e.g., getters,\nsetters) to set up or tear down the testing environment.\n- fm+fc+c+m+f : this representation adds the public fields of\nthe focal class. The motivation is that test cases may need to\ninspect the status of the public fields to properly test a focal\nmethod.\n\n!image/png\n\n The different levels of focal contexts are the following:", "## Limitations\nThe original authors validate the heuristics by inspecting a\nstatistically significant sample (confidence level of 95% within 10%\nmargin of error) of 97 samples from the training set. Two authors\nindependently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct\nlink between the test case and the corresponding focal method", "## Contribution\nAll thanks to the original authors." ]	[ 42, 198, 5, 275, 85, 11 ]	[ "passage: TAGS\n#task_categories-text-generation #language-English #license-mit #unit test #java #code #arxiv-2203.12776 #region-us \n## Dataset Description\nMicrosoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. \nIt contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K\nJava open source project hosted on GitHub.\n\nThis is smaller subset of the assembled version of the methods2test dataset.\nIt provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available.\nThe subset is created by randomly selecting only one sample from each of the 91k projects.\n\n\nThe mapping between test case and focal methods are based heuristics rules and Java developer's best practice.\n\nMore information could be found here:\n- methods2test Github repo\n- Methods2Test: A dataset of focal methods mapped to test cases## Dataset Schema" ]
678bde4068ca2b17ac27f14d379c6530cadd8397	# Dataset of hammann (Azur Lane) This is the dataset of hammann (Azur Lane), containing 482 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 482 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 1338 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 1529 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 482 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 482 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 482 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 1338 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 1338 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 1193 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 1529 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 1529 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|	AppleHarem/hammann_azurlane	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-12-17T20:42:14+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-12-17T20:43:46+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of hammann (Azur Lane) ============================== This is the dataset of hammann (Azur Lane), containing 482 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). This is a WebUI contains crawlers and other thing: (LittleAppleWebUI)	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
9714be1caf114e4b235a9080737fe59d1404b713	# COST Dataset The COST dataset includes the following components for training and evaluating MLLMs on object-level perception tasks: - RGB Images obtained from the [COCO-2017](https://cocodataset.org/#download) dataset. - Segmentation Maps for semantic, instance, and panoptic segmentation tasks, obtained using the publicly available [DiNAT-L OneFormer](https://github.com/SHI-Labs/OneFormer#coco) model trained on the COCO dataset. - Questions obtained by prompting [GPT-4](https://chat.openai.com/) for object identification and object order perception tasks. You can find the questions in [questions.py](vcoder_llava/questions.py). - Depth Maps obtained using the publicly available ViT-L/14 distilled variant of [DINOv2 DPT](https://github.com/facebookresearch/dinov2#pretrained-heads---depth-estimation) model trained on the NYUd dataset. We represent the information from the segmentation maps and depth maps in text form to obtain the final question-answer pairs. Please refer to Sec 3.1 in our paper for more details. <p align="center"> <img src="VCoder-COST.svg" width="100%" class="center"/> </p> We provide different splits of the COST dataset for training and evaluation. \| split \| Number of Images \| Number of QnA pairs \| splits from COCO \| \| :-------: \| :------------------: \| :---------------------: \| :------------------: \| \| train \| 280k \| 280k \| train2017, test2017, unlabeled2017 \| \| val \| 5k \| 5k \| val2017 \| ## File Structure ```text coco_segm_text ├── depth │ └── test │ │ └── ... │ └── train │ │ └── depth # contains depth maps for the train2017 split │ │ └── panoptic_order.txt # contains answers for object order perception task on images in test2017 split │ └── unlabeled │ │ └── ... │ └── val │ │ └── ... ├── test │ └── ... ├── train │ └── instance_inference # contains instance masks for train2017 split │ └── instance.txt # contains answers for instance object identification task on images in train2017 split │ └── panoptic_inference # contains panoptic masks for train2017 split │ └── panoptic.txt # contains answers for panoptic object identification task on images in train2017 split │ └── semantic_inference # contains semantic masks for train2017 split │ └── semantic.txt # contains answers for instance object identification task on images in train2017 split ├── unlabeled │ └── ... ├── val │ └── ... ``` ## Citation If you use COST dataset, please consider starring ⭐ us on [GitHub](https://github.com/SHI-Labs/VCoder) and citing 📚 us in your research! ```bibtex @article{jain2023vcoder, title={{VCoder: Versatile Vision Encoders for Multimodal Large Language Models}}, author={Jitesh Jain and Jianwei Yang and Humphrey Shi}, journal={arXiv}, year={2023} } ```	shi-labs/COST	[ "license:mit", "region:us" ]	2023-12-17T21:36:54+00:00	{"license": "mit"}	2023-12-21T23:43:50+00:00	[]	[]	TAGS #license-mit #region-us	COST Dataset ============ The COST dataset includes the following components for training and evaluating MLLMs on object-level perception tasks: * RGB Images obtained from the COCO-2017 dataset. * Segmentation Maps for semantic, instance, and panoptic segmentation tasks, obtained using the publicly available DiNAT-L OneFormer model trained on the COCO dataset. * Questions obtained by prompting GPT-4 for object identification and object order perception tasks. You can find the questions in URL. * Depth Maps obtained using the publicly available ViT-L/14 distilled variant of DINOv2 DPT model trained on the NYUd dataset. We represent the information from the segmentation maps and depth maps in text form to obtain the final question-answer pairs. Please refer to Sec 3.1 in our paper for more details. ![](URL) We provide different splits of the COST dataset for training and evaluation. File Structure -------------- If you use COST dataset, please consider starring ⭐ us on GitHub and citing us in your research!	[]	[ "TAGS\n#license-mit #region-us \n" ]	[ 11 ]	[ "passage: TAGS\n#license-mit #region-us \n" ]
d5cbb662ee4a23958508264be77f119ff38b2da0	This dataset contains Q&A based on what kind of questions do people ask online with their respective answers. This can be used as an LLM project where we finetune an LLM and create a chatbot where one can ask the chatbot any question related to Apple products and get respective answers. ## To use this dataset try running the following code: - dataset = load_dataset("Aashi/All_About_Apple_Devices", data_files={"train": "QandA.csv"}) - df_train = dataset['train'].to_pandas()	Aashi/All_About_Apple_Devices	[ "task_categories:question-answering", "task_categories:conversational", "size_categories:n<1K", "language:en", "license:mit", "tech", "region:us" ]	2023-12-17T23:51:57+00:00	{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["question-answering", "conversational"], "pretty_name": "A3D \ud83d\ude80", "tags": ["tech"]}	2024-01-06T05:12:34+00:00	[]	[ "en" ]	TAGS #task_categories-question-answering #task_categories-conversational #size_categories-n<1K #language-English #license-mit #tech #region-us	This dataset contains Q&A based on what kind of questions do people ask online with their respective answers. This can be used as an LLM project where we finetune an LLM and create a chatbot where one can ask the chatbot any question related to Apple products and get respective answers. ## To use this dataset try running the following code: - dataset = load_dataset("Aashi/All_About_Apple_Devices", data_files={"train": "URL"}) - df_train = dataset['train'].to_pandas()	[ "## To use this dataset try running the following code:\n\n- dataset = load_dataset(\"Aashi/All_About_Apple_Devices\", data_files={\"train\": \"URL\"})\n\n- df_train = dataset['train'].to_pandas()" ]	[ "TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-n<1K #language-English #license-mit #tech #region-us \n", "## To use this dataset try running the following code:\n\n- dataset = load_dataset(\"Aashi/All_About_Apple_Devices\", data_files={\"train\": \"URL\"})\n\n- df_train = dataset['train'].to_pandas()" ]	[ 49, 74 ]	[ "passage: TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-n<1K #language-English #license-mit #tech #region-us \n## To use this dataset try running the following code:\n\n- dataset = load_dataset(\"Aashi/All_About_Apple_Devices\", data_files={\"train\": \"URL\"})\n\n- df_train = dataset['train'].to_pandas()" ]
69fa1b709db684121fab67ebf30abd5e3dde19f6	# Hamza - Uzbek-Russian parallel corpora. ## Overview Hamza is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian. ## Creation Created using [lingtrain](https://github.com/averkij/lingtrain-aligner). Text mined from different websites and telegram channels. ### Format The dataset is presented in TMX (Translation Memory eXchange).	nixon0630/hamza_1.0	[ "task_categories:translation", "language:uz", "language:ru", "license:mit", "region:us" ]	2023-12-18T00:59:36+00:00	{"language": ["uz", "ru"], "license": "mit", "task_categories": ["translation"], "pretty_name": "uzbek-russian_parallel_corpora"}	2023-12-18T01:18:36+00:00	[]	[ "uz", "ru" ]	TAGS #task_categories-translation #language-Uzbek #language-Russian #license-mit #region-us	# Hamza - Uzbek-Russian parallel corpora. ## Overview Hamza is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian. ## Creation Created using lingtrain. Text mined from different websites and telegram channels. ### Format The dataset is presented in TMX (Translation Memory eXchange).	[ "# Hamza - Uzbek-Russian parallel corpora.", "## Overview\nHamza is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian.", "## Creation\nCreated using lingtrain. Text mined from different websites and telegram channels.", "### Format\nThe dataset is presented in TMX (Translation Memory eXchange)." ]	[ "TAGS\n#task_categories-translation #language-Uzbek #language-Russian #license-mit #region-us \n", "# Hamza - Uzbek-Russian parallel corpora.", "## Overview\nHamza is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian.", "## Creation\nCreated using lingtrain. Text mined from different websites and telegram channels.", "### Format\nThe dataset is presented in TMX (Translation Memory eXchange)." ]	[ 30, 11, 23, 22, 20 ]	[ "passage: TAGS\n#task_categories-translation #language-Uzbek #language-Russian #license-mit #region-us \n# Hamza - Uzbek-Russian parallel corpora.## Overview\nHamza is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian.## Creation\nCreated using lingtrain. Text mined from different websites and telegram channels.### Format\nThe dataset is presented in TMX (Translation Memory eXchange)." ]
78778d1baa8417a5502ec2838ee409c7376d8e5c	# Dataset Card for "openai_summarize_generated_20k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mnoukhov/openai_summarize_generated_20k	[ "region:us" ]	2023-12-18T01:31:35+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35982323, "num_examples": 20000}], "download_size": 21908175, "dataset_size": 35982323}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-18T01:31:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "openai_summarize_generated_20k" More Information needed	[ "# Dataset Card for \"openai_summarize_generated_20k\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"openai_summarize_generated_20k\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"openai_summarize_generated_20k\"\n\nMore Information needed" ]
e56da8eee7a96d786dff93cb5e4c8aaefd73e04d	# Dataset Card for Evaluation run of Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2](https://huggingface.co/Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Dans-DiscountModels__TinyLlama-1.1B-FFT-Test2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T02:54:33.981160](https://huggingface.co/datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyLlama-1.1B-FFT-Test2/blob/main/results_2023-12-18T02-54-33.981160.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": 0.2607212816484538, "acc_stderr": 0.030867007452377485, "acc_norm": 0.26236232884330074, "acc_norm_stderr": 0.03165482907989167, "mc1": 0.20563035495716034, "mc1_stderr": 0.014148482219460972, "mc2": 0.3631613697388407, "mc2_stderr": 0.013778296923024373 }, "harness\|arc:challenge\|25": { "acc": 0.31399317406143346, "acc_stderr": 0.013562691224726291, "acc_norm": 0.34215017064846415, "acc_norm_stderr": 0.013864152159177278 }, "harness\|hellaswag\|10": { "acc": 0.44054969129655447, "acc_stderr": 0.004954384702021655, "acc_norm": 0.5795658235411273, "acc_norm_stderr": 0.004926198483948714 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.23, "acc_stderr": 0.042295258468165044, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165044 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.17777777777777778, "acc_stderr": 0.03302789859901717, "acc_norm": 0.17777777777777778, "acc_norm_stderr": 0.03302789859901717 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.21710526315789475, "acc_stderr": 0.03355045304882921, "acc_norm": 0.21710526315789475, "acc_norm_stderr": 0.03355045304882921 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.27547169811320754, "acc_stderr": 0.027495663683724077, "acc_norm": 0.27547169811320754, "acc_norm_stderr": 0.027495663683724077 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2708333333333333, "acc_stderr": 0.03716177437566017, "acc_norm": 0.2708333333333333, "acc_norm_stderr": 0.03716177437566017 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036624, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036624 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.23121387283236994, "acc_stderr": 0.03214737302029469, "acc_norm": 0.23121387283236994, "acc_norm_stderr": 0.03214737302029469 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.2553191489361702, "acc_stderr": 0.0285048564705142, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.0285048564705142 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2631578947368421, "acc_stderr": 0.04142439719489362, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.04142439719489362 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.21379310344827587, "acc_stderr": 0.034165204477475494, "acc_norm": 0.21379310344827587, "acc_norm_stderr": 0.034165204477475494 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.20634920634920634, "acc_stderr": 0.020842290930114683, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.020842290930114683 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.03932537680392871, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.03932537680392871 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.21935483870967742, "acc_stderr": 0.02354079935872329, "acc_norm": 0.21935483870967742, "acc_norm_stderr": 0.02354079935872329 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.21182266009852216, "acc_stderr": 0.02874898368994107, "acc_norm": 0.21182266009852216, "acc_norm_stderr": 0.02874898368994107 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.24242424242424243, "acc_stderr": 0.03346409881055953, "acc_norm": 0.24242424242424243, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.22727272727272727, "acc_stderr": 0.029857515673386396, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.029857515673386396 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.22797927461139897, "acc_stderr": 0.030276909945178263, "acc_norm": 0.22797927461139897, "acc_norm_stderr": 0.030276909945178263 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.24358974358974358, "acc_stderr": 0.02176373368417392, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.02176373368417392 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2518518518518518, "acc_stderr": 0.026466117538959912, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.026466117538959912 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.23109243697478993, "acc_stderr": 0.027381406927868963, "acc_norm": 0.23109243697478993, "acc_norm_stderr": 0.027381406927868963 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.23178807947019867, "acc_stderr": 0.034454062719870546, "acc_norm": 0.23178807947019867, "acc_norm_stderr": 0.034454062719870546 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.21834862385321102, "acc_stderr": 0.01771260052872273, "acc_norm": 0.21834862385321102, "acc_norm_stderr": 0.01771260052872273 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502325, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502325 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.028867431449849313, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.028867431449849313 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.27848101265822783, "acc_stderr": 0.02917868230484256, "acc_norm": 0.27848101265822783, "acc_norm_stderr": 0.02917868230484256 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.37668161434977576, "acc_stderr": 0.032521134899291884, "acc_norm": 0.37668161434977576, "acc_norm_stderr": 0.032521134899291884 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.22900763358778625, "acc_stderr": 0.036853466317118506, "acc_norm": 0.22900763358778625, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.043300437496507437, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.043300437496507437 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.26380368098159507, "acc_stderr": 0.034624199316156234, "acc_norm": 0.26380368098159507, "acc_norm_stderr": 0.034624199316156234 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.29464285714285715, "acc_stderr": 0.04327040932578728, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.04327040932578728 }, "harness\|hendrycksTest-management\|5": { "acc": 0.1553398058252427, "acc_stderr": 0.03586594738573973, "acc_norm": 0.1553398058252427, "acc_norm_stderr": 0.03586594738573973 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.029343114798094472, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.029343114798094472 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.24904214559386972, "acc_stderr": 0.015464676163395977, "acc_norm": 0.24904214559386972, "acc_norm_stderr": 0.015464676163395977 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.2543352601156069, "acc_stderr": 0.02344582627654555, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.02344582627654555 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.25027932960893856, "acc_stderr": 0.014487500852850414, "acc_norm": 0.25027932960893856, "acc_norm_stderr": 0.014487500852850414 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.22875816993464052, "acc_stderr": 0.024051029739912255, "acc_norm": 0.22875816993464052, "acc_norm_stderr": 0.024051029739912255 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.28938906752411575, "acc_stderr": 0.025755865922632935, "acc_norm": 0.28938906752411575, "acc_norm_stderr": 0.025755865922632935 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2716049382716049, "acc_stderr": 0.02474862449053737, "acc_norm": 0.2716049382716049, "acc_norm_stderr": 0.02474862449053737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432414, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432414 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2438070404172099, "acc_stderr": 0.010966507972178475, "acc_norm": 0.2438070404172099, "acc_norm_stderr": 0.010966507972178475 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4375, "acc_stderr": 0.030134614954403924, "acc_norm": 0.4375, "acc_norm_stderr": 0.030134614954403924 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.01740181671142765, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.01740181671142765 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2818181818181818, "acc_stderr": 0.043091187099464585, "acc_norm": 0.2818181818181818, "acc_norm_stderr": 0.043091187099464585 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.15510204081632653, "acc_stderr": 0.0231747988612186, "acc_norm": 0.15510204081632653, "acc_norm_stderr": 0.0231747988612186 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.26506024096385544, "acc_stderr": 0.03436024037944967, "acc_norm": 0.26506024096385544, "acc_norm_stderr": 0.03436024037944967 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.28654970760233917, "acc_stderr": 0.03467826685703826, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.03467826685703826 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.20563035495716034, "mc1_stderr": 0.014148482219460972, "mc2": 0.3631613697388407, "mc2_stderr": 0.013778296923024373 }, "harness\|winogrande\|5": { "acc": 0.5880031570639306, "acc_stderr": 0.013833112857645935 }, "harness\|gsm8k\|5": { "acc": 0.0037907505686125853, "acc_stderr": 0.0016927007401502019 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_Dans-DiscountModels__TinyLlama-1.1B-FFT-Test2	[ "region:us" ]	2023-12-18T02:56:51+00:00	{"pretty_name": "Evaluation run of Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2](https://huggingface.co/Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Dans-DiscountModels__TinyLlama-1.1B-FFT-Test2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T02:54:33.981160](https://huggingface.co/datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyLlama-1.1B-FFT-Test2/blob/main/results_2023-12-18T02-54-33.981160.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\": 0.2607212816484538,\n \"acc_stderr\": 0.030867007452377485,\n \"acc_norm\": 0.26236232884330074,\n \"acc_norm_stderr\": 0.03165482907989167,\n \"mc1\": 0.20563035495716034,\n \"mc1_stderr\": 0.014148482219460972,\n \"mc2\": 0.3631613697388407,\n \"mc2_stderr\": 0.013778296923024373\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.31399317406143346,\n \"acc_stderr\": 0.013562691224726291,\n \"acc_norm\": 0.34215017064846415,\n \"acc_norm_stderr\": 0.013864152159177278\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.44054969129655447,\n \"acc_stderr\": 0.004954384702021655,\n \"acc_norm\": 0.5795658235411273,\n \"acc_norm_stderr\": 0.004926198483948714\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.17777777777777778,\n \"acc_stderr\": 0.03302789859901717,\n \"acc_norm\": 0.17777777777777778,\n \"acc_norm_stderr\": 0.03302789859901717\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.21710526315789475,\n \"acc_stderr\": 0.03355045304882921,\n \"acc_norm\": 0.21710526315789475,\n \"acc_norm_stderr\": 0.03355045304882921\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.27547169811320754,\n \"acc_stderr\": 0.027495663683724077,\n \"acc_norm\": 0.27547169811320754,\n \"acc_norm_stderr\": 0.027495663683724077\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2708333333333333,\n \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.2708333333333333,\n \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.23121387283236994,\n \"acc_stderr\": 0.03214737302029469,\n \"acc_norm\": 0.23121387283236994,\n \"acc_norm_stderr\": 0.03214737302029469\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.2553191489361702,\n \"acc_stderr\": 0.0285048564705142,\n \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.0285048564705142\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2631578947368421,\n \"acc_stderr\": 0.04142439719489362,\n \"acc_norm\": 0.2631578947368421,\n \"acc_norm_stderr\": 0.04142439719489362\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.21379310344827587,\n \"acc_stderr\": 0.034165204477475494,\n \"acc_norm\": 0.21379310344827587,\n \"acc_norm_stderr\": 0.034165204477475494\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.20634920634920634,\n \"acc_stderr\": 0.020842290930114683,\n \"acc_norm\": 0.20634920634920634,\n \"acc_norm_stderr\": 0.020842290930114683\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.03932537680392871,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.03932537680392871\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.21935483870967742,\n \"acc_stderr\": 0.02354079935872329,\n \"acc_norm\": 0.21935483870967742,\n \"acc_norm_stderr\": 0.02354079935872329\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.21182266009852216,\n \"acc_stderr\": 0.02874898368994107,\n \"acc_norm\": 0.21182266009852216,\n \"acc_norm_stderr\": 0.02874898368994107\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.029857515673386396,\n \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.029857515673386396\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.22797927461139897,\n \"acc_stderr\": 0.030276909945178263,\n \"acc_norm\": 0.22797927461139897,\n \"acc_norm_stderr\": 0.030276909945178263\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.24358974358974358,\n \"acc_stderr\": 0.02176373368417392,\n \"acc_norm\": 0.24358974358974358,\n \"acc_norm_stderr\": 0.02176373368417392\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2518518518518518,\n \"acc_stderr\": 0.026466117538959912,\n \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.026466117538959912\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868963,\n \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868963\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.23178807947019867,\n \"acc_stderr\": 0.034454062719870546,\n \"acc_norm\": 0.23178807947019867,\n \"acc_norm_stderr\": 0.034454062719870546\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.21834862385321102,\n \"acc_stderr\": 0.01771260052872273,\n \"acc_norm\": 0.21834862385321102,\n \"acc_norm_stderr\": 0.01771260052872273\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502325,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502325\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.028867431449849313,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.028867431449849313\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.27848101265822783,\n \"acc_stderr\": 0.02917868230484256,\n \"acc_norm\": 0.27848101265822783,\n \"acc_norm_stderr\": 0.02917868230484256\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.37668161434977576,\n \"acc_stderr\": 0.032521134899291884,\n \"acc_norm\": 0.37668161434977576,\n \"acc_norm_stderr\": 0.032521134899291884\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.034624199316156234,\n \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.034624199316156234\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.04327040932578728,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.04327040932578728\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.1553398058252427,\n \"acc_stderr\": 0.03586594738573973,\n \"acc_norm\": 0.1553398058252427,\n \"acc_norm_stderr\": 0.03586594738573973\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T02-54-33.981160.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T02-54-33.981160.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T02_54_33.981160", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T02-54-33.981160.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T02-54-33.981160.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T02_54_33.981160", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T02-54-33.981160.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T02-54-33.981160.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T02_54_33.981160", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T02-54-33.981160.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T02-54-33.981160.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T02_54_33.981160", "path": ["/details_harness\|winogrande\|5_2023-12-18T02-54-33.981160.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T02-54-33.981160.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T02_54_33.981160", "path": ["results_2023-12-18T02-54-33.981160.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T02-54-33.981160.parquet"]}]}]}	2023-12-18T02:57:33+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2 Dataset automatically created during the evaluation run of model Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T02:54:33.981160(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2\n\n\n\nDataset automatically created during the evaluation run of model Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T02:54:33.981160(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2\n\n\n\nDataset automatically created during the evaluation run of model Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T02:54:33.981160(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 201, 66, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2\n\n\n\nDataset automatically created during the evaluation run of model Dans-DiscountModels/TinyLlama-1.1B-FFT-Test2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T02:54:33.981160(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
73ed35fa61fbb3720cad03a7ad6584e47ba4e98a	# Dataset Card for Evaluation run of quantumaikr/quantum-trinity-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [quantumaikr/quantum-trinity-v0.1](https://huggingface.co/quantumaikr/quantum-trinity-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_quantumaikr__quantum-trinity-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T03:19:56.363034](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__quantum-trinity-v0.1/blob/main/results_2023-12-18T03-19-56.363034.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": 0.6572247685368219, "acc_stderr": 0.031992528267718985, "acc_norm": 0.657053972912952, "acc_norm_stderr": 0.032653378327314075, "mc1": 0.5495716034271726, "mc1_stderr": 0.01741726437196764, "mc2": 0.6927593169679473, "mc2_stderr": 0.01502938815172874 }, "harness\|arc:challenge\|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002508, "acc_norm": 0.7252559726962458, "acc_norm_stderr": 0.013044617212771227 }, "harness\|hellaswag\|10": { "acc": 0.7104162517426807, "acc_stderr": 0.004526422125860672, "acc_norm": 0.8827922724556861, "acc_norm_stderr": 0.0032101025071772497 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720386, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720386 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.02385479568097112, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.02385479568097112 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524565, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524565 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8532110091743119, "acc_stderr": 0.01517314184512625, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.01517314184512625 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5509259259259259, "acc_stderr": 0.03392238405321617, "acc_norm": 0.5509259259259259, "acc_norm_stderr": 0.03392238405321617 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240644, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240644 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290916, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290916 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.031024411740572213, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.031024411740572213 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8244274809160306, "acc_stderr": 0.03336820338476074, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.03336820338476074 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.03226219377286775, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286775 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8378033205619413, "acc_stderr": 0.013182222616720887, "acc_norm": 0.8378033205619413, "acc_norm_stderr": 0.013182222616720887 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4793296089385475, "acc_stderr": 0.016708205559996137, "acc_norm": 0.4793296089385475, "acc_norm_stderr": 0.016708205559996137 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826528, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826528 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46870925684485004, "acc_stderr": 0.012745204626083138, "acc_norm": 0.46870925684485004, "acc_norm_stderr": 0.012745204626083138 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462937, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462937 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5495716034271726, "mc1_stderr": 0.01741726437196764, "mc2": 0.6927593169679473, "mc2_stderr": 0.01502938815172874 }, "harness\|winogrande\|5": { "acc": 0.8255722178374112, "acc_stderr": 0.010665187902498428 }, "harness\|gsm8k\|5": { "acc": 0.7020470053070508, "acc_stderr": 0.012597932232914517 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_quantumaikr__quantum-trinity-v0.1	[ "region:us" ]	2023-12-18T03:22:48+00:00	{"pretty_name": "Evaluation run of quantumaikr/quantum-trinity-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [quantumaikr/quantum-trinity-v0.1](https://huggingface.co/quantumaikr/quantum-trinity-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_quantumaikr__quantum-trinity-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T03:19:56.363034](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__quantum-trinity-v0.1/blob/main/results_2023-12-18T03-19-56.363034.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\": 0.6572247685368219,\n \"acc_stderr\": 0.031992528267718985,\n \"acc_norm\": 0.657053972912952,\n \"acc_norm_stderr\": 0.032653378327314075,\n \"mc1\": 0.5495716034271726,\n \"mc1_stderr\": 0.01741726437196764,\n \"mc2\": 0.6927593169679473,\n \"mc2_stderr\": 0.01502938815172874\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6945392491467577,\n \"acc_stderr\": 0.013460080478002508,\n \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7104162517426807,\n \"acc_stderr\": 0.004526422125860672,\n \"acc_norm\": 0.8827922724556861,\n \"acc_norm_stderr\": 0.0032101025071772497\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\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\": 0.6936416184971098,\n \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097112,\n \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097112\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524565,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524565\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8532110091743119,\n \"acc_stderr\": 0.01517314184512625,\n \"acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.01517314184512625\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8378033205619413,\n \"acc_stderr\": 0.013182222616720887,\n \"acc_norm\": 0.8378033205619413,\n \"acc_norm_stderr\": 0.013182222616720887\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4793296089385475,\n \"acc_stderr\": 0.016708205559996137,\n \"acc_norm\": 0.4793296089385475,\n \"acc_norm_stderr\": 0.016708205559996137\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826528,\n \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826528\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46870925684485004,\n \"acc_stderr\": 0.012745204626083138,\n \"acc_norm\": 0.46870925684485004,\n \"acc_norm_stderr\": 0.012745204626083138\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 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The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T03:19:56.363034(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of quantumaikr/quantum-trinity-v0.1\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-trinity-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T03:19:56.363034(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of quantumaikr/quantum-trinity-v0.1\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-trinity-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T03:19:56.363034(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 185, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of quantumaikr/quantum-trinity-v0.1\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-trinity-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T03:19:56.363034(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
3180be925b4f5f656cf67bece08b13812b21e9d9	Please concatenate all the content of README files in the repo, while we only provide the link of README file in root for each repository in the ML-Bench. You can find README crawler and BM25 retrieval 'script' in [our github](https://github.com/gersteinlab/ML-Bench/tree/master/script)	KPTK/ML-Bench	[ "region:us" ]	2023-12-18T03:24:58+00:00	{}	2023-12-18T03:29:12+00:00	[]	[]	TAGS #region-us	Please concatenate all the content of README files in the repo, while we only provide the link of README file in root for each repository in the ML-Bench. You can find README crawler and BM25 retrieval 'script' in our github	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
92c86f34fabdc423ecc7ad0fa76739a7102a4bc6	# Dataset Card for "dcase2016_task2_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/dcase2016_task2_extract_unit	[ "region:us" ]	2023-12-18T03:45:25+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 13827534, "num_examples": 72}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 13827534, "num_examples": 72}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 20739534, "num_examples": 72}, {"name": "audiodec_24k_320d", "num_bytes": 44241486, "num_examples": 72}, {"name": "dac_16k", "num_bytes": 84608718, "num_examples": 72}, {"name": "dac_24k", "num_bytes": 234909006, "num_examples": 72}, {"name": "dac_44k", "num_bytes": 69429102, "num_examples": 72}, {"name": "encodec_24k", "num_bytes": 10372110, "num_examples": 72}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 110622030, "num_examples": 72}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 110622030, "num_examples": 72}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 110622030, "num_examples": 72}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 55326030, "num_examples": 72}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 110622030, "num_examples": 72}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 110622030, "num_examples": 72}, {"name": "speech_tokenizer_16k", "num_bytes": 27657294, "num_examples": 72}], "download_size": 175718409, "dataset_size": 1128048498}}	2023-12-19T10:46:03+00:00	[]	[]	TAGS #region-us	# Dataset Card for "dcase2016_task2_extract_unit" More Information needed	[ "# Dataset Card for \"dcase2016_task2_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"dcase2016_task2_extract_unit\"\n\nMore Information needed" ]	[ 6, 23 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"dcase2016_task2_extract_unit\"\n\nMore Information needed" ]
bbaf5f06198740e58444ce771e0f4afa6e2caede	# Dataset Card for "openai_summarize_generated_20k_relabelled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mnoukhov/openai_summarize_generated_20k_relabel_410m_dpo1	[ "region:us" ]	2023-12-18T03:47:52+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35982323, "num_examples": 20000}], "download_size": 21903259, "dataset_size": 35982323}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-18T03:48:00+00:00	[]	[]	TAGS #region-us	# Dataset Card for "openai_summarize_generated_20k_relabelled" More Information needed	[ "# Dataset Card for \"openai_summarize_generated_20k_relabelled\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"openai_summarize_generated_20k_relabelled\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"openai_summarize_generated_20k_relabelled\"\n\nMore Information needed" ]
9624228ef19608258aa3df0c3ba160a12ec44ed2	This dataset is a low-resolution sea-level-pressure field for testing the global weather model. File information: - Variable: sea-level-pressure (slp) - Spatial resolution: 4 degree lat and lon - Spatial dimension: 46(lat) * 90(lon) - Temporal resolution: 1 day - Number of snapshots: 7300 (approx 20 years) - Size: 230 MB	Zhouf23/slp-2002-2022-4degree	[ "size_categories:1K<n<10K", "license:mit", "climate", "region:us" ]	2023-12-18T03:51:51+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "slp_toy", "tags": ["climate"]}	2023-12-18T04:32:46+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #climate #region-us	This dataset is a low-resolution sea-level-pressure field for testing the global weather model. File information: - Variable: sea-level-pressure (slp) - Spatial resolution: 4 degree lat and lon - Spatial dimension: 46(lat) * 90(lon) - Temporal resolution: 1 day - Number of snapshots: 7300 (approx 20 years) - Size: 230 MB	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #climate #region-us \n" ]	[ 27 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #climate #region-us \n" ]
cf10be2cf8109cab2baf594457c6ad6944962180	# About dataset [The Philosophy Data Project](https://philosophydata.com/index.html) is a corpus and a set of anaylsis based philosophy texts, totaling over 50 texts and 30 authors, made by Kourosh Alizadeh. - `school`: Broad categorization of which school of thought each book belongs to. Sometimes, this classification can be vague or depend on interpretation. Thankfully, texts in this corpus are all distinctive examples of respective school of thought, so at leat here they are reasonable. - `sentence_spacy` and `sentence_str`: Actual sentences from the texts. Note that some sentences are omitted due to its short length, etc. Refer to the next section for details. Other items are straightforward. ## Other details For overview on how the data is prepared, and what to make out of the data itself, check [the author's comment on it](https://philosophydata.com/interpret.html). To actually browse how the data preparation, check out [the official github repository](https://github.com/kcalizadeh/phil_nlp).	yjkim27/The-Philosophy-Data-Project	[ "language:en", "llm", "language-modeling", "region:us" ]	2023-12-18T03:58:04+00:00	{"language": ["en"], "pretty_name": "The-Philosophy-Data-Project", "tags": ["llm", "language-modeling"]}	2023-12-18T05:07:06+00:00	[]	[ "en" ]	TAGS #language-English #llm #language-modeling #region-us	# About dataset The Philosophy Data Project is a corpus and a set of anaylsis based philosophy texts, totaling over 50 texts and 30 authors, made by Kourosh Alizadeh. - 'school': Broad categorization of which school of thought each book belongs to. Sometimes, this classification can be vague or depend on interpretation. Thankfully, texts in this corpus are all distinctive examples of respective school of thought, so at leat here they are reasonable. - 'sentence_spacy' and 'sentence_str': Actual sentences from the texts. Note that some sentences are omitted due to its short length, etc. Refer to the next section for details. Other items are straightforward. ## Other details For overview on how the data is prepared, and what to make out of the data itself, check the author's comment on it. To actually browse how the data preparation, check out the official github repository.	[ "# About dataset\n\nThe Philosophy Data Project is a corpus and a set of anaylsis based philosophy texts, totaling over 50 texts and 30 authors, made by Kourosh Alizadeh.\n\n- 'school': Broad categorization of which school of thought each book belongs to. Sometimes, this classification can be vague or depend on interpretation. Thankfully, texts in this corpus are all distinctive examples of respective school of thought, so at leat here they are reasonable.\n- 'sentence_spacy' and 'sentence_str': Actual sentences from the texts. Note that some sentences are omitted due to its short length, etc. Refer to the next section for details. \n\nOther items are straightforward.", "## Other details\n\nFor overview on how the data is prepared, and what to make out of the data itself, check the author's comment on it.\n\nTo actually browse how the data preparation, check out the official github repository." ]	[ "TAGS\n#language-English #llm #language-modeling #region-us \n", "# About dataset\n\nThe Philosophy Data Project is a corpus and a set of anaylsis based philosophy texts, totaling over 50 texts and 30 authors, made by Kourosh Alizadeh.\n\n- 'school': Broad categorization of which school of thought each book belongs to. Sometimes, this classification can be vague or depend on interpretation. Thankfully, texts in this corpus are all distinctive examples of respective school of thought, so at leat here they are reasonable.\n- 'sentence_spacy' and 'sentence_str': Actual sentences from the texts. Note that some sentences are omitted due to its short length, etc. Refer to the next section for details. \n\nOther items are straightforward.", "## Other details\n\nFor overview on how the data is prepared, and what to make out of the data itself, check the author's comment on it.\n\nTo actually browse how the data preparation, check out the official github repository." ]	[ 18, 168, 52 ]	[ "passage: TAGS\n#language-English #llm #language-modeling #region-us \n# About dataset\n\nThe Philosophy Data Project is a corpus and a set of anaylsis based philosophy texts, totaling over 50 texts and 30 authors, made by Kourosh Alizadeh.\n\n- 'school': Broad categorization of which school of thought each book belongs to. Sometimes, this classification can be vague or depend on interpretation. Thankfully, texts in this corpus are all distinctive examples of respective school of thought, so at leat here they are reasonable.\n- 'sentence_spacy' and 'sentence_str': Actual sentences from the texts. Note that some sentences are omitted due to its short length, etc. Refer to the next section for details. \n\nOther items are straightforward.## Other details\n\nFor overview on how the data is prepared, and what to make out of the data itself, check the author's comment on it.\n\nTo actually browse how the data preparation, check out the official github repository." ]
35ddebe66af13939dec0206fee588f31613823bf	# Dataset Card for "mridangam_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/mridangam_extract_unit	[ "region:us" ]	2023-12-18T04:14:09+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 9307086, "num_examples": 6977}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 9307086, "num_examples": 6977}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 13772366, "num_examples": 6977}, {"name": "audiodec_24k_320d", "num_bytes": 29512478, "num_examples": 6977}, {"name": "dac_16k", "num_bytes": 28061262, "num_examples": 6977}, {"name": "dac_24k", "num_bytes": 110110782, "num_examples": 6977}, {"name": "dac_44k", "num_bytes": 35680146, "num_examples": 6977}, {"name": "encodec_24k", "num_bytes": 7130262, "num_examples": 6977}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 74388542, "num_examples": 6977}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 74388542, "num_examples": 6977}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 74388542, "num_examples": 6977}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 38666302, "num_examples": 6977}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 74388542, "num_examples": 6977}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 74388542, "num_examples": 6977}, {"name": "speech_tokenizer_16k", "num_bytes": 18795806, "num_examples": 6977}], "download_size": 98187324, "dataset_size": 672286286}}	2023-12-18T04:15:15+00:00	[]	[]	TAGS #region-us	# Dataset Card for "mridangam_extract_unit" More Information needed	[ "# Dataset Card for \"mridangam_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"mridangam_extract_unit\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"mridangam_extract_unit\"\n\nMore Information needed" ]
5668bf10a3ac51add544c55b7155a60e01ce257c	This dataset is created for testing global climate model - Variable: sea-level-pressure - Spatial resolution: 2 degree lat and lon - Spatial dim: (90 lat, 181 lon) - Temporal resolution: 1 day - Number of snamshots: 7300	Zhouf23/slp-2002-2022-2degree	[ "license:mit", "region:us" ]	2023-12-18T04:26:48+00:00	{"license": "mit"}	2023-12-18T04:31:15+00:00	[]	[]	TAGS #license-mit #region-us	This dataset is created for testing global climate model - Variable: sea-level-pressure - Spatial resolution: 2 degree lat and lon - Spatial dim: (90 lat, 181 lon) - Temporal resolution: 1 day - Number of snamshots: 7300	[]	[ "TAGS\n#license-mit #region-us \n" ]	[ 11 ]	[ "passage: TAGS\n#license-mit #region-us \n" ]
8ffc12a3cd8c6b47c7c15f23cf25ed85b8f91097	# Dataset Card for "gunshot_triangulation_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/gunshot_triangulation_synth	[ "region:us" ]	2023-12-18T05:22:59+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "original", "path": "data/original-"}, {"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k_12bps", "path": "data/encodec_24k_12bps-"}, {"split": "encodec_24k_1_5bps", "path": "data/encodec_24k_1_5bps-"}, {"split": "encodec_24k_24bps", "path": "data/encodec_24k_24bps-"}, {"split": "encodec_24k_3bps", "path": "data/encodec_24k_3bps-"}, {"split": "encodec_24k_6bps", "path": "data/encodec_24k_6bps-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "original", "num_bytes": 12677868.0, "num_examples": 88}, {"name": "academicodec_hifi_16k_320d", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 6313784.0, "num_examples": 88}, {"name": "audiodec_24k_320d", "num_bytes": 6341956.0, "num_examples": 88}, {"name": "dac_16k", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "dac_24k", "num_bytes": 6341944.0, "num_examples": 88}, {"name": "dac_44k", "num_bytes": 11648344.0, "num_examples": 88}, {"name": "encodec_24k_12bps", "num_bytes": 6341944.0, "num_examples": 88}, {"name": "encodec_24k_1_5bps", "num_bytes": 6341944.0, "num_examples": 88}, {"name": "encodec_24k_24bps", "num_bytes": 6341944.0, "num_examples": 88}, {"name": "encodec_24k_3bps", "num_bytes": 6341944.0, "num_examples": 88}, {"name": "encodec_24k_6bps", "num_bytes": 6341944.0, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 4229944.0, "num_examples": 88}, {"name": "speech_tokenizer_16k", "num_bytes": 4229944.0, "num_examples": 88}], "download_size": 110782805, "dataset_size": 117333056.0}}	2024-01-28T02:52:25+00:00	[]	[]	TAGS #region-us	# Dataset Card for "gunshot_triangulation_synth" More Information needed	[ "# Dataset Card for \"gunshot_triangulation_synth\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"gunshot_triangulation_synth\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"gunshot_triangulation_synth\"\n\nMore Information needed" ]
adc79f39136d8bc4ade70ceee5667b7be8358115	# Dataset Card for Evaluation run of perlthoughts/neural-chat-v3-3-8x7b-MoE <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [perlthoughts/neural-chat-v3-3-8x7b-MoE](https://huggingface.co/perlthoughts/neural-chat-v3-3-8x7b-MoE) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_perlthoughts__neural-chat-v3-3-8x7b-MoE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T05:24:06.077139](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__neural-chat-v3-3-8x7b-MoE/blob/main/results_2023-12-18T05-24-06.077139.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": 0.6274049176829637, "acc_stderr": 0.03275930044853432, "acc_norm": 0.6268605636213929, "acc_norm_stderr": 0.033440518650225654, "mc1": 0.47368421052631576, "mc1_stderr": 0.017479241161975526, "mc2": 0.631965606310606, "mc2_stderr": 0.015067807381751251 }, "harness\|arc:challenge\|25": { "acc": 0.6459044368600683, "acc_stderr": 0.013975454122756562, "acc_norm": 0.6663822525597269, "acc_norm_stderr": 0.013778687054176536 }, "harness\|hellaswag\|10": { "acc": 0.666301533559052, "acc_stderr": 0.004705697745222153, "acc_norm": 0.8543118900617407, "acc_norm_stderr": 0.003520722505332094 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6513157894736842, "acc_stderr": 0.03878139888797612, "acc_norm": 0.6513157894736842, "acc_norm_stderr": 0.03878139888797612 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7083333333333334, "acc_stderr": 0.038009680605548594, "acc_norm": 0.7083333333333334, "acc_norm_stderr": 0.038009680605548594 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5574468085106383, "acc_stderr": 0.032469569197899575, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.032469569197899575 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.37566137566137564, "acc_stderr": 0.024942368931159798, "acc_norm": 0.37566137566137564, "acc_norm_stderr": 0.024942368931159798 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7419354838709677, "acc_stderr": 0.02489246917246283, "acc_norm": 0.7419354838709677, "acc_norm_stderr": 0.02489246917246283 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592154, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592154 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386414, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386414 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.617948717948718, "acc_stderr": 0.024635549163908234, "acc_norm": 0.617948717948718, "acc_norm_stderr": 0.024635549163908234 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6386554621848739, "acc_stderr": 0.03120469122515002, "acc_norm": 0.6386554621848739, "acc_norm_stderr": 0.03120469122515002 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8293577981651377, "acc_stderr": 0.016129271025099867, "acc_norm": 0.8293577981651377, "acc_norm_stderr": 0.016129271025099867 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7794117647058824, "acc_stderr": 0.02910225438967408, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.02910225438967408 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6591928251121076, "acc_stderr": 0.0318114974705536, "acc_norm": 0.6591928251121076, "acc_norm_stderr": 0.0318114974705536 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.042365112580946315, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.042365112580946315 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7055214723926381, "acc_stderr": 0.03581165790474082, "acc_norm": 0.7055214723926381, "acc_norm_stderr": 0.03581165790474082 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077802, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077802 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8020434227330779, "acc_stderr": 0.014248873549217575, "acc_norm": 0.8020434227330779, "acc_norm_stderr": 0.014248873549217575 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.02454761779480383, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.02454761779480383 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.45027932960893857, "acc_stderr": 0.016639615236845807, "acc_norm": 0.45027932960893857, "acc_norm_stderr": 0.016639615236845807 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6928104575163399, "acc_stderr": 0.02641560191438898, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.02641560191438898 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7006172839506173, "acc_stderr": 0.025483115601195455, "acc_norm": 0.7006172839506173, "acc_norm_stderr": 0.025483115601195455 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.45390070921985815, "acc_stderr": 0.02970045324729146, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.02970045324729146 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4256844850065189, "acc_stderr": 0.012628393551811947, "acc_norm": 0.4256844850065189, "acc_norm_stderr": 0.012628393551811947 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6323529411764706, "acc_stderr": 0.029289413409403192, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.029289413409403192 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6274509803921569, "acc_stderr": 0.019559646809215923, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.019559646809215923 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421606, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421606 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.47368421052631576, "mc1_stderr": 0.017479241161975526, "mc2": 0.631965606310606, "mc2_stderr": 0.015067807381751251 }, "harness\|winogrande\|5": { "acc": 0.7971586424625099, "acc_stderr": 0.011301439925936662 }, "harness\|gsm8k\|5": { "acc": 0.6982562547384382, "acc_stderr": 0.012643544762873358 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_perlthoughts__neural-chat-v3-3-8x7b-MoE	[ "region:us" ]	2023-12-18T05:26:59+00:00	{"pretty_name": "Evaluation run of perlthoughts/neural-chat-v3-3-8x7b-MoE", "dataset_summary": "Dataset automatically created during the evaluation run of model [perlthoughts/neural-chat-v3-3-8x7b-MoE](https://huggingface.co/perlthoughts/neural-chat-v3-3-8x7b-MoE) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_perlthoughts__neural-chat-v3-3-8x7b-MoE\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T05:24:06.077139](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__neural-chat-v3-3-8x7b-MoE/blob/main/results_2023-12-18T05-24-06.077139.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\": 0.6274049176829637,\n \"acc_stderr\": 0.03275930044853432,\n \"acc_norm\": 0.6268605636213929,\n \"acc_norm_stderr\": 0.033440518650225654,\n \"mc1\": 0.47368421052631576,\n \"mc1_stderr\": 0.017479241161975526,\n \"mc2\": 0.631965606310606,\n \"mc2_stderr\": 0.015067807381751251\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6459044368600683,\n \"acc_stderr\": 0.013975454122756562,\n \"acc_norm\": 0.6663822525597269,\n \"acc_norm_stderr\": 0.013778687054176536\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.666301533559052,\n \"acc_stderr\": 0.004705697745222153,\n \"acc_norm\": 0.8543118900617407,\n \"acc_norm_stderr\": 0.003520722505332094\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.03878139888797612,\n \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.03878139888797612\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7083333333333334,\n \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n \"acc_norm_stderr\": 0.038009680605548594\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159798,\n \"acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159798\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7419354838709677,\n \"acc_stderr\": 0.02489246917246283,\n \"acc_norm\": 0.7419354838709677,\n \"acc_norm_stderr\": 0.02489246917246283\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.617948717948718,\n \"acc_stderr\": 0.024635549163908234,\n \"acc_norm\": 0.617948717948718,\n \"acc_norm_stderr\": 0.024635549163908234\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.03120469122515002,\n \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.03120469122515002\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099867,\n \"acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099867\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967408,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967408\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6591928251121076,\n \"acc_stderr\": 0.0318114974705536,\n \"acc_norm\": 0.6591928251121076,\n \"acc_norm_stderr\": 0.0318114974705536\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.022509033937077802,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.022509033937077802\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8020434227330779,\n \"acc_stderr\": 0.014248873549217575,\n \"acc_norm\": 0.8020434227330779,\n \"acc_norm_stderr\": 0.014248873549217575\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.02454761779480383,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.02454761779480383\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.45027932960893857,\n \"acc_stderr\": 0.016639615236845807,\n \"acc_norm\": 0.45027932960893857,\n \"acc_norm_stderr\": 0.016639615236845807\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.02641560191438898,\n \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.02641560191438898\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6784565916398714,\n \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195455,\n \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195455\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.45390070921985815,\n \"acc_stderr\": 0.02970045324729146,\n \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.02970045324729146\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4256844850065189,\n \"acc_stderr\": 0.012628393551811947,\n \"acc_norm\": 0.4256844850065189,\n \"acc_norm_stderr\": 0.012628393551811947\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6323529411764706,\n \"acc_stderr\": 0.029289413409403192,\n \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.029289413409403192\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6274509803921569,\n \"acc_stderr\": 0.019559646809215923,\n \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.019559646809215923\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n \"acc_stderr\": 0.026814951200421606,\n \"acc_norm\": 0.8258706467661692,\n \"acc_norm_stderr\": 0.026814951200421606\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.47368421052631576,\n \"mc1_stderr\": 0.017479241161975526,\n \"mc2\": 0.631965606310606,\n \"mc2_stderr\": 0.015067807381751251\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7971586424625099,\n \"acc_stderr\": 0.011301439925936662\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6982562547384382,\n \"acc_stderr\": 0.012643544762873358\n }\n}\n```", "repo_url": "https://huggingface.co/perlthoughts/neural-chat-v3-3-8x7b-MoE", "leaderboard_url": 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["/details_harness\|truthfulqa:mc\|0_2023-12-18T05-24-06.077139.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T05-24-06.077139.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T05_24_06.077139", "path": ["/details_harness\|winogrande\|5_2023-12-18T05-24-06.077139.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T05-24-06.077139.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T05_24_06.077139", "path": ["results_2023-12-18T05-24-06.077139.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T05-24-06.077139.parquet"]}]}]}	2023-12-18T05:27:50+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of perlthoughts/neural-chat-v3-3-8x7b-MoE Dataset automatically created during the evaluation run of model perlthoughts/neural-chat-v3-3-8x7b-MoE on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T05:24:06.077139(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of perlthoughts/neural-chat-v3-3-8x7b-MoE\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/neural-chat-v3-3-8x7b-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T05:24:06.077139(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of perlthoughts/neural-chat-v3-3-8x7b-MoE\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/neural-chat-v3-3-8x7b-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T05:24:06.077139(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 199, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of perlthoughts/neural-chat-v3-3-8x7b-MoE\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/neural-chat-v3-3-8x7b-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T05:24:06.077139(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
546e817570c04fceb977cdf7a4e9fbd5eb97d59c	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha1](https://huggingface.co/MisterRid/wendigo-14b-alpha1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T06:01:22.262879](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha1/blob/main/results_2023-12-18T06-01-22.262879.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": 0.5746351990995501, "acc_stderr": 0.03390742119619079, "acc_norm": 0.5814001050651504, "acc_norm_stderr": 0.03463267101438414, "mc1": 0.39657282741738065, "mc1_stderr": 0.017124930942023515, "mc2": 0.5375635889879792, "mc2_stderr": 0.015746421140624522 }, "harness\|arc:challenge\|25": { "acc": 0.5392491467576792, "acc_stderr": 0.014566303676636583, "acc_norm": 0.5648464163822525, "acc_norm_stderr": 0.014487986197186038 }, "harness\|hellaswag\|10": { "acc": 0.5795658235411273, "acc_stderr": 0.004926198483948707, "acc_norm": 0.7719577773351922, "acc_norm_stderr": 0.004187124964848515 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.562962962962963, "acc_stderr": 0.042849586397534015, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.042849586397534015 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880274, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880274 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6458333333333334, "acc_stderr": 0.039994111357535424, "acc_norm": 0.6458333333333334, "acc_norm_stderr": 0.039994111357535424 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5148936170212766, "acc_stderr": 0.03267151848924777, "acc_norm": 0.5148936170212766, "acc_norm_stderr": 0.03267151848924777 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.42758620689655175, "acc_stderr": 0.041227371113703316, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.041227371113703316 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155247, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155247 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04285714285714281, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04285714285714281 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6870967741935484, "acc_stderr": 0.02637756702864586, "acc_norm": 0.6870967741935484, "acc_norm_stderr": 0.02637756702864586 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4039408866995074, "acc_stderr": 0.034524539038220406, "acc_norm": 0.4039408866995074, "acc_norm_stderr": 0.034524539038220406 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091707, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091707 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7373737373737373, "acc_stderr": 0.031353050095330855, "acc_norm": 0.7373737373737373, "acc_norm_stderr": 0.031353050095330855 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8031088082901554, "acc_stderr": 0.02869787397186068, "acc_norm": 0.8031088082901554, "acc_norm_stderr": 0.02869787397186068 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6102564102564103, "acc_stderr": 0.024726967886647078, "acc_norm": 0.6102564102564103, "acc_norm_stderr": 0.024726967886647078 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6176470588235294, "acc_stderr": 0.03156663099215416, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.03156663099215416 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526733, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526733 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8091743119266055, "acc_stderr": 0.01684767640009109, "acc_norm": 0.8091743119266055, "acc_norm_stderr": 0.01684767640009109 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4583333333333333, "acc_stderr": 0.03398110890294636, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7107843137254902, "acc_stderr": 0.031822318676475544, "acc_norm": 0.7107843137254902, "acc_norm_stderr": 0.031822318676475544 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6835443037974683, "acc_stderr": 0.03027497488021898, "acc_norm": 0.6835443037974683, "acc_norm_stderr": 0.03027497488021898 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6457399103139013, "acc_stderr": 0.032100621541349864, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.032100621541349864 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6412213740458015, "acc_stderr": 0.04206739313864908, "acc_norm": 0.6412213740458015, "acc_norm_stderr": 0.04206739313864908 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6319018404907976, "acc_stderr": 0.03789213935838396, "acc_norm": 0.6319018404907976, "acc_norm_stderr": 0.03789213935838396 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260595, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260595 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8247863247863247, "acc_stderr": 0.024904439098918228, "acc_norm": 0.8247863247863247, "acc_norm_stderr": 0.024904439098918228 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7662835249042146, "acc_stderr": 0.015133383278988832, "acc_norm": 0.7662835249042146, "acc_norm_stderr": 0.015133383278988832 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6040462427745664, "acc_stderr": 0.026329813341946236, "acc_norm": 0.6040462427745664, "acc_norm_stderr": 0.026329813341946236 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.29497206703910617, "acc_stderr": 0.015251931579208178, "acc_norm": 0.29497206703910617, "acc_norm_stderr": 0.015251931579208178 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6209150326797386, "acc_stderr": 0.02778014120702333, "acc_norm": 0.6209150326797386, "acc_norm_stderr": 0.02778014120702333 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.662379421221865, "acc_stderr": 0.02685882587948854, "acc_norm": 0.662379421221865, "acc_norm_stderr": 0.02685882587948854 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6574074074074074, "acc_stderr": 0.02640614597362566, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.02640614597362566 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4219858156028369, "acc_stderr": 0.029462189233370593, "acc_norm": 0.4219858156028369, "acc_norm_stderr": 0.029462189233370593 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4015645371577575, "acc_stderr": 0.012520315120147096, "acc_norm": 0.4015645371577575, "acc_norm_stderr": 0.012520315120147096 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681397, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681397 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6013071895424836, "acc_stderr": 0.019808281317449848, "acc_norm": 0.6013071895424836, "acc_norm_stderr": 0.019808281317449848 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5714285714285714, "acc_stderr": 0.031680911612338825, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.031680911612338825 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7114427860696517, "acc_stderr": 0.03203841040213321, "acc_norm": 0.7114427860696517, "acc_norm_stderr": 0.03203841040213321 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.463855421686747, "acc_stderr": 0.03882310850890593, "acc_norm": 0.463855421686747, "acc_norm_stderr": 0.03882310850890593 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7426900584795322, "acc_stderr": 0.03352799844161865, "acc_norm": 0.7426900584795322, "acc_norm_stderr": 0.03352799844161865 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.39657282741738065, "mc1_stderr": 0.017124930942023515, "mc2": 0.5375635889879792, "mc2_stderr": 0.015746421140624522 }, "harness\|winogrande\|5": { "acc": 0.7300710339384373, "acc_stderr": 0.012476433372002603 }, "harness\|gsm8k\|5": { "acc": 0.23805913570887036, "acc_stderr": 0.0117312787484209 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha1	[ "region:us" ]	2023-12-18T06:04:17+00:00	{"pretty_name": "Evaluation run of MisterRid/wendigo-14b-alpha1", "dataset_summary": "Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha1](https://huggingface.co/MisterRid/wendigo-14b-alpha1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T06:01:22.262879](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha1/blob/main/results_2023-12-18T06-01-22.262879.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\": 0.5746351990995501,\n \"acc_stderr\": 0.03390742119619079,\n \"acc_norm\": 0.5814001050651504,\n \"acc_norm_stderr\": 0.03463267101438414,\n \"mc1\": 0.39657282741738065,\n \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.5375635889879792,\n \"mc2_stderr\": 0.015746421140624522\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5392491467576792,\n \"acc_stderr\": 0.014566303676636583,\n \"acc_norm\": 0.5648464163822525,\n \"acc_norm_stderr\": 0.014487986197186038\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5795658235411273,\n \"acc_stderr\": 0.004926198483948707,\n \"acc_norm\": 0.7719577773351922,\n \"acc_norm_stderr\": 0.004187124964848515\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.562962962962963,\n \"acc_stderr\": 0.042849586397534015,\n \"acc_norm\": 0.562962962962963,\n \"acc_norm_stderr\": 0.042849586397534015\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880274,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880274\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6458333333333334,\n \"acc_stderr\": 0.039994111357535424,\n \"acc_norm\": 0.6458333333333334,\n \"acc_norm_stderr\": 0.039994111357535424\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155247,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155247\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.35714285714285715,\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\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6870967741935484,\n \"acc_stderr\": 0.02637756702864586,\n \"acc_norm\": 0.6870967741935484,\n \"acc_norm_stderr\": 0.02637756702864586\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4039408866995074,\n \"acc_stderr\": 0.034524539038220406,\n \"acc_norm\": 0.4039408866995074,\n \"acc_norm_stderr\": 0.034524539038220406\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091707,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091707\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7373737373737373,\n \"acc_stderr\": 0.031353050095330855,\n \"acc_norm\": 0.7373737373737373,\n \"acc_norm_stderr\": 0.031353050095330855\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8031088082901554,\n \"acc_stderr\": 0.02869787397186068,\n \"acc_norm\": 0.8031088082901554,\n \"acc_norm_stderr\": 0.02869787397186068\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647078,\n \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647078\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.03156663099215416,\n \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.03156663099215416\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8091743119266055,\n \"acc_stderr\": 0.01684767640009109,\n \"acc_norm\": 0.8091743119266055,\n \"acc_norm_stderr\": 0.01684767640009109\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7107843137254902,\n \"acc_stderr\": 0.031822318676475544,\n \"acc_norm\": 0.7107843137254902,\n \"acc_norm_stderr\": 0.031822318676475544\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.6835443037974683,\n \"acc_stderr\": 0.03027497488021898,\n \"acc_norm\": 0.6835443037974683,\n \"acc_norm_stderr\": 0.03027497488021898\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6457399103139013,\n \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.6412213740458015,\n \"acc_stderr\": 0.04206739313864908,\n \"acc_norm\": 0.6412213740458015,\n \"acc_norm_stderr\": 0.04206739313864908\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6851851851851852,\n \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8247863247863247,\n \"acc_stderr\": 0.024904439098918228,\n \"acc_norm\": 0.8247863247863247,\n \"acc_norm_stderr\": 0.024904439098918228\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7662835249042146,\n \"acc_stderr\": 0.015133383278988832,\n \"acc_norm\": 0.7662835249042146,\n \"acc_norm_stderr\": 0.015133383278988832\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.6040462427745664,\n \"acc_stderr\": 0.026329813341946236,\n \"acc_norm\": 0.6040462427745664,\n \"acc_norm_stderr\": 0.026329813341946236\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.29497206703910617,\n \"acc_stderr\": 0.015251931579208178,\n \"acc_norm\": 0.29497206703910617,\n \"acc_norm_stderr\": 0.015251931579208178\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6209150326797386,\n \"acc_stderr\": 0.02778014120702333,\n \"acc_norm\": 0.6209150326797386,\n \"acc_norm_stderr\": 0.02778014120702333\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.662379421221865,\n \"acc_stderr\": 0.02685882587948854,\n \"acc_norm\": 0.662379421221865,\n \"acc_norm_stderr\": 0.02685882587948854\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.6574074074074074,\n \"acc_stderr\": 0.02640614597362566,\n \"acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.02640614597362566\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4015645371577575,\n \"acc_stderr\": 0.012520315120147096,\n \"acc_norm\": 0.4015645371577575,\n \"acc_norm_stderr\": 0.012520315120147096\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6013071895424836,\n \"acc_stderr\": 0.019808281317449848,\n \"acc_norm\": 0.6013071895424836,\n \"acc_norm_stderr\": 0.019808281317449848\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6272727272727273,\n \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.031680911612338825,\n \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.031680911612338825\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7114427860696517,\n \"acc_stderr\": 0.03203841040213321,\n \"acc_norm\": 0.7114427860696517,\n \"acc_norm_stderr\": 0.03203841040213321\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.463855421686747,\n \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.39657282741738065,\n \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.5375635889879792,\n \"mc2_stderr\": 0.015746421140624522\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7300710339384373,\n \"acc_stderr\": 0.012476433372002603\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.23805913570887036,\n \"acc_stderr\": 0.0117312787484209\n }\n}\n```", "repo_url": "https://huggingface.co/MisterRid/wendigo-14b-alpha1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|gsm8k\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hellaswag\|10_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T06-01-22.262879.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-18T06-01-22.262879.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-01-22.262879.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T06_01_22.262879", "path": ["results_2023-12-18T06-01-22.262879.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T06-01-22.262879.parquet"]}]}]}	2023-12-18T06:05:05+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha1 Dataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T06:01:22.262879(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha1\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:01:22.262879(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha1\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:01:22.262879(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 185, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha1\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T06:01:22.262879(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
94eb6bce8fa239f75358ee264c840479196f24e8	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha2](https://huggingface.co/MisterRid/wendigo-14b-alpha2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T06:03:21.055340](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha2/blob/main/results_2023-12-18T06-03-21.055340.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": 0.5760376255323894, "acc_stderr": 0.03389255049926726, "acc_norm": 0.5830693356885244, "acc_norm_stderr": 0.03462115663481434, "mc1": 0.3929008567931457, "mc1_stderr": 0.017097248285233065, "mc2": 0.5371025434721111, "mc2_stderr": 0.015786315933755037 }, "harness\|arc:challenge\|25": { "acc": 0.5290102389078498, "acc_stderr": 0.014586776355294321, "acc_norm": 0.5665529010238908, "acc_norm_stderr": 0.014481376224558902 }, "harness\|hellaswag\|10": { "acc": 0.5812587134037045, "acc_stderr": 0.004923445627861517, "acc_norm": 0.77185819557857, "acc_norm_stderr": 0.004187768949417078 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411022, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411022 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04292596718256981, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04292596718256981 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.618421052631579, "acc_stderr": 0.03953173377749194, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.03953173377749194 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6597222222222222, "acc_stderr": 0.039621355734862175, "acc_norm": 0.6597222222222222, "acc_norm_stderr": 0.039621355734862175 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6011560693641619, "acc_stderr": 0.037336266553835096, "acc_norm": 0.6011560693641619, "acc_norm_stderr": 0.037336266553835096 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4413793103448276, "acc_stderr": 0.04137931034482758, "acc_norm": 0.4413793103448276, "acc_norm_stderr": 0.04137931034482758 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3412698412698413, "acc_stderr": 0.04240799327574924, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.04240799327574924 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6870967741935484, "acc_stderr": 0.02637756702864586, "acc_norm": 0.6870967741935484, "acc_norm_stderr": 0.02637756702864586 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3842364532019704, "acc_stderr": 0.0342239856565755, "acc_norm": 0.3842364532019704, "acc_norm_stderr": 0.0342239856565755 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.036085410115739666, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.036085410115739666 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03173071239071724, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03173071239071724 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7823834196891192, "acc_stderr": 0.02977866303775295, "acc_norm": 0.7823834196891192, "acc_norm_stderr": 0.02977866303775295 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6050420168067226, "acc_stderr": 0.03175367846096626, "acc_norm": 0.6050420168067226, "acc_norm_stderr": 0.03175367846096626 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8128440366972477, "acc_stderr": 0.01672268452620014, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.01672268452620014 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.0309645179269234, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.0309645179269234 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6624472573839663, "acc_stderr": 0.03078154910202622, "acc_norm": 0.6624472573839663, "acc_norm_stderr": 0.03078154910202622 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6502242152466368, "acc_stderr": 0.03200736719484503, "acc_norm": 0.6502242152466368, "acc_norm_stderr": 0.03200736719484503 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.03749492448709698, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.03749492448709698 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6441717791411042, "acc_stderr": 0.03761521380046735, "acc_norm": 0.6441717791411042, "acc_norm_stderr": 0.03761521380046735 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5178571428571429, "acc_stderr": 0.04742762361243011, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.04742762361243011 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260595, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260595 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8205128205128205, "acc_stderr": 0.025140935950335445, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.025140935950335445 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7726692209450831, "acc_stderr": 0.014987270640946012, "acc_norm": 0.7726692209450831, "acc_norm_stderr": 0.014987270640946012 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5838150289017341, "acc_stderr": 0.02653818910470547, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.02653818910470547 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.32849162011173183, "acc_stderr": 0.01570793539849645, "acc_norm": 0.32849162011173183, "acc_norm_stderr": 0.01570793539849645 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.630718954248366, "acc_stderr": 0.02763417668960266, "acc_norm": 0.630718954248366, "acc_norm_stderr": 0.02763417668960266 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.662379421221865, "acc_stderr": 0.026858825879488533, "acc_norm": 0.662379421221865, "acc_norm_stderr": 0.026858825879488533 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6697530864197531, "acc_stderr": 0.026168298456732846, "acc_norm": 0.6697530864197531, "acc_norm_stderr": 0.026168298456732846 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.39361702127659576, "acc_stderr": 0.029144544781596147, "acc_norm": 0.39361702127659576, "acc_norm_stderr": 0.029144544781596147 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.39048239895697523, "acc_stderr": 0.012460135913945077, "acc_norm": 0.39048239895697523, "acc_norm_stderr": 0.012460135913945077 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6323529411764706, "acc_stderr": 0.029289413409403192, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.029289413409403192 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6078431372549019, "acc_stderr": 0.019751726508762637, "acc_norm": 0.6078431372549019, "acc_norm_stderr": 0.019751726508762637 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670238, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670238 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5795918367346938, "acc_stderr": 0.03160106993449601, "acc_norm": 0.5795918367346938, "acc_norm_stderr": 0.03160106993449601 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7164179104477612, "acc_stderr": 0.03187187537919795, "acc_norm": 0.7164179104477612, "acc_norm_stderr": 0.03187187537919795 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542126, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542126 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7543859649122807, "acc_stderr": 0.0330140594698725, "acc_norm": 0.7543859649122807, "acc_norm_stderr": 0.0330140594698725 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3929008567931457, "mc1_stderr": 0.017097248285233065, "mc2": 0.5371025434721111, "mc2_stderr": 0.015786315933755037 }, "harness\|winogrande\|5": { "acc": 0.7363851617995264, "acc_stderr": 0.012382849299658466 }, "harness\|gsm8k\|5": { "acc": 0.22896133434420016, "acc_stderr": 0.011573412892418219 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha2	[ "region:us" ]	2023-12-18T06:06:23+00:00	{"pretty_name": "Evaluation run of MisterRid/wendigo-14b-alpha2", "dataset_summary": "Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha2](https://huggingface.co/MisterRid/wendigo-14b-alpha2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T06:03:21.055340](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha2/blob/main/results_2023-12-18T06-03-21.055340.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\": 0.5760376255323894,\n \"acc_stderr\": 0.03389255049926726,\n \"acc_norm\": 0.5830693356885244,\n \"acc_norm_stderr\": 0.03462115663481434,\n \"mc1\": 0.3929008567931457,\n \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5371025434721111,\n \"mc2_stderr\": 0.015786315933755037\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5290102389078498,\n \"acc_stderr\": 0.014586776355294321,\n \"acc_norm\": 0.5665529010238908,\n \"acc_norm_stderr\": 0.014481376224558902\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5812587134037045,\n \"acc_stderr\": 0.004923445627861517,\n \"acc_norm\": 0.77185819557857,\n \"acc_norm_stderr\": 0.004187768949417078\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411022,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411022\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6597222222222222,\n \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6011560693641619,\n \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.6011560693641619,\n \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-03-21.055340.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T06_03_21.055340", "path": ["results_2023-12-18T06-03-21.055340.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T06-03-21.055340.parquet"]}]}]}	2023-12-18T06:07:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha2 Dataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T06:03:21.055340(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha2\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:03:21.055340(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha2\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:03:21.055340(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 185, 66, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha2\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T06:03:21.055340(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
ee2b1b522c57138467176c5e79b1e120b1efd4cb	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha3](https://huggingface.co/MisterRid/wendigo-14b-alpha3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T06:12:12.392006](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha3/blob/main/results_2023-12-18T06-12-12.392006.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": 0.5945767224954732, "acc_stderr": 0.03360455895710174, "acc_norm": 0.600491328626145, "acc_norm_stderr": 0.03430153130071733, "mc1": 0.3953488372093023, "mc1_stderr": 0.017115815632418194, "mc2": 0.5511700389811293, "mc2_stderr": 0.015590045101276278 }, "harness\|arc:challenge\|25": { "acc": 0.5614334470989761, "acc_stderr": 0.014500682618212864, "acc_norm": 0.5938566552901023, "acc_norm_stderr": 0.014351656690097862 }, "harness\|hellaswag\|10": { "acc": 0.5987851025692094, "acc_stderr": 0.004891426533390627, "acc_norm": 0.7950607448715395, "acc_norm_stderr": 0.0040283226548527475 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.039420826399272135, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.039420826399272135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5234042553191489, "acc_stderr": 0.03265019475033582, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.03265019475033582 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.45517241379310347, "acc_stderr": 0.04149886942192117, "acc_norm": 0.45517241379310347, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7129032258064516, "acc_stderr": 0.02573654274559453, "acc_norm": 0.7129032258064516, "acc_norm_stderr": 0.02573654274559453 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4187192118226601, "acc_stderr": 0.034711928605184676, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.034711928605184676 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7151515151515152, "acc_stderr": 0.03524390844511781, "acc_norm": 0.7151515151515152, "acc_norm_stderr": 0.03524390844511781 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479047, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479047 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8186528497409327, "acc_stderr": 0.02780703236068609, "acc_norm": 0.8186528497409327, "acc_norm_stderr": 0.02780703236068609 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6282051282051282, "acc_stderr": 0.024503472557110936, "acc_norm": 0.6282051282051282, "acc_norm_stderr": 0.024503472557110936 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524575, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524575 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059285, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059285 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763744, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763744 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8128440366972477, "acc_stderr": 0.016722684526200144, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.016722684526200144 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.03019028245350195, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.03019028245350195 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6835443037974683, "acc_stderr": 0.030274974880218977, "acc_norm": 0.6835443037974683, "acc_norm_stderr": 0.030274974880218977 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6793893129770993, "acc_stderr": 0.04093329229834278, "acc_norm": 0.6793893129770993, "acc_norm_stderr": 0.04093329229834278 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.044531975073749834, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.044531975073749834 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.656441717791411, "acc_stderr": 0.03731133519673893, "acc_norm": 0.656441717791411, "acc_norm_stderr": 0.03731133519673893 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6990291262135923, "acc_stderr": 0.04541609446503948, "acc_norm": 0.6990291262135923, "acc_norm_stderr": 0.04541609446503948 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.0225090339370778, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.0225090339370778 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7841634738186463, "acc_stderr": 0.01471168438613995, "acc_norm": 0.7841634738186463, "acc_norm_stderr": 0.01471168438613995 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6098265895953757, "acc_stderr": 0.026261677607806632, "acc_norm": 0.6098265895953757, "acc_norm_stderr": 0.026261677607806632 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3396648044692737, "acc_stderr": 0.015839400406212498, "acc_norm": 0.3396648044692737, "acc_norm_stderr": 0.015839400406212498 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.026992544339297243, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.026992544339297243 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6881028938906752, "acc_stderr": 0.02631185807185416, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.02631185807185416 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6574074074074074, "acc_stderr": 0.026406145973625672, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.026406145973625672 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.425531914893617, "acc_stderr": 0.029494827600144373, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.029494827600144373 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.41134289439374183, "acc_stderr": 0.012567882673803673, "acc_norm": 0.41134289439374183, "acc_norm_stderr": 0.012567882673803673 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6143790849673203, "acc_stderr": 0.019691459052354025, "acc_norm": 0.6143790849673203, "acc_norm_stderr": 0.019691459052354025 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6163265306122448, "acc_stderr": 0.031130880396235936, "acc_norm": 0.6163265306122448, "acc_norm_stderr": 0.031130880396235936 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7412935323383084, "acc_stderr": 0.03096590312357305, "acc_norm": 0.7412935323383084, "acc_norm_stderr": 0.03096590312357305 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3953488372093023, "mc1_stderr": 0.017115815632418194, "mc2": 0.5511700389811293, "mc2_stderr": 0.015590045101276278 }, "harness\|winogrande\|5": { "acc": 0.7474348855564326, "acc_stderr": 0.012211148449394105 }, "harness\|gsm8k\|5": { "acc": 0.3214556482183472, "acc_stderr": 0.012864471384836705 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha3	[ "region:us" ]	2023-12-18T06:15:08+00:00	{"pretty_name": "Evaluation run of MisterRid/wendigo-14b-alpha3", "dataset_summary": "Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha3](https://huggingface.co/MisterRid/wendigo-14b-alpha3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T06:12:12.392006](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha3/blob/main/results_2023-12-18T06-12-12.392006.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\": 0.5945767224954732,\n \"acc_stderr\": 0.03360455895710174,\n \"acc_norm\": 0.600491328626145,\n \"acc_norm_stderr\": 0.03430153130071733,\n \"mc1\": 0.3953488372093023,\n \"mc1_stderr\": 0.017115815632418194,\n \"mc2\": 0.5511700389811293,\n \"mc2_stderr\": 0.015590045101276278\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5614334470989761,\n \"acc_stderr\": 0.014500682618212864,\n \"acc_norm\": 0.5938566552901023,\n \"acc_norm_stderr\": 0.014351656690097862\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5987851025692094,\n \"acc_stderr\": 0.004891426533390627,\n \"acc_norm\": 0.7950607448715395,\n \"acc_norm_stderr\": 0.0040283226548527475\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.039420826399272135,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.039420826399272135\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7129032258064516,\n \"acc_stderr\": 0.02573654274559453,\n \"acc_norm\": 0.7129032258064516,\n \"acc_norm_stderr\": 0.02573654274559453\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4187192118226601,\n \"acc_stderr\": 0.034711928605184676,\n \"acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.034711928605184676\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7151515151515152,\n 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["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-12-12.392006.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T06_12_12.392006", "path": ["results_2023-12-18T06-12-12.392006.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T06-12-12.392006.parquet"]}]}]}	2023-12-18T06:15:53+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha3 Dataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha3 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T06:12:12.392006(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha3\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:12:12.392006(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha3\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:12:12.392006(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 185, 66, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha3\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T06:12:12.392006(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
9ff10f1232c4dfa2b372818150d9fd2ec1fc2b94	# Datasets * small_train: 3000 rows (0: 1500, 1: 1500) (subset of train) * train: 43868 rows (0: 26871, 1: 16997) * test: 1000 rows (0: 500, 1: 500) # label * 0: human * 1: ai	lectura/dagit2-fake-detection	[ "region:us" ]	2023-12-18T06:18:54+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "prompt_name", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "RDizzl3_seven", "dtype": "bool"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "small_train", "num_bytes": 6733440, "num_examples": 3000}, {"name": "train", "num_bytes": 100255071, "num_examples": 43868}, {"name": "test", "num_bytes": 2256359, "num_examples": 1000}], "download_size": 57330696, "dataset_size": 109244870}, "configs": [{"config_name": "default", "data_files": [{"split": "small_train", "path": "data/small_train-"}, {"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-*"}]}]}	2023-12-18T06:25:11+00:00	[]	[]	TAGS #region-us	# Datasets * small_train: 3000 rows (0: 1500, 1: 1500) (subset of train) * train: 43868 rows (0: 26871, 1: 16997) * test: 1000 rows (0: 500, 1: 500) # label * 0: human * 1: ai	[ "# Datasets\n* small_train: 3000 rows (0: 1500, 1: 1500) (subset of train)\n* train: 43868 rows (0: 26871, 1: 16997)\n* test: 1000 rows (0: 500, 1: 500)", "# label\n\n* 0: human\n* 1: ai" ]	[ "TAGS\n#region-us \n", "# Datasets\n* small_train: 3000 rows (0: 1500, 1: 1500) (subset of train)\n* train: 43868 rows (0: 26871, 1: 16997)\n* test: 1000 rows (0: 500, 1: 500)", "# label\n\n* 0: human\n* 1: ai" ]	[ 6, 58, 9 ]	[ "passage: TAGS\n#region-us \n# Datasets\n* small_train: 3000 rows (0: 1500, 1: 1500) (subset of train)\n* train: 43868 rows (0: 26871, 1: 16997)\n* test: 1000 rows (0: 500, 1: 500)# label\n\n* 0: human\n* 1: ai" ]
f89227d920af3de9718f35c35c926beae5308919	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha4 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha4](https://huggingface.co/MisterRid/wendigo-14b-alpha4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T06:46:37.615025](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha4/blob/main/results_2023-12-18T06-46-37.615025.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": 0.5958734764422213, "acc_stderr": 0.033567613925099785, "acc_norm": 0.6017569763815189, "acc_norm_stderr": 0.034261570709298174, "mc1": 0.397796817625459, "mc1_stderr": 0.017133934248559638, "mc2": 0.5497966141695696, "mc2_stderr": 0.01557713395489198 }, "harness\|arc:challenge\|25": { "acc": 0.5571672354948806, "acc_stderr": 0.0145155738733489, "acc_norm": 0.5930034129692833, "acc_norm_stderr": 0.014356399418009121 }, "harness\|hellaswag\|10": { "acc": 0.599681338378809, "acc_stderr": 0.004889615413144195, "acc_norm": 0.7964548894642501, "acc_norm_stderr": 0.004018115765954247 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544057, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544057 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6805555555555556, "acc_stderr": 0.038990736873573344, "acc_norm": 0.6805555555555556, "acc_norm_stderr": 0.038990736873573344 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467383, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467383 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.025010749116137595, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.025010749116137595 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7064516129032258, "acc_stderr": 0.025906087021319295, "acc_norm": 0.7064516129032258, "acc_norm_stderr": 0.025906087021319295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.43842364532019706, "acc_stderr": 0.03491207857486518, "acc_norm": 0.43842364532019706, "acc_norm_stderr": 0.03491207857486518 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091706, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091706 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932026, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932026 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8290155440414507, "acc_stderr": 0.02717121368316453, "acc_norm": 0.8290155440414507, "acc_norm_stderr": 0.02717121368316453 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6333333333333333, "acc_stderr": 0.024433016466052462, "acc_norm": 0.6333333333333333, "acc_norm_stderr": 0.024433016466052462 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32222222222222224, "acc_stderr": 0.02849346509102859, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.02849346509102859 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.030489911417673227, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.030489911417673227 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763744, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763744 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8110091743119267, "acc_stderr": 0.01678548115920363, "acc_norm": 0.8110091743119267, "acc_norm_stderr": 0.01678548115920363 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.030964517926923393, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.030964517926923393 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7046413502109705, "acc_stderr": 0.029696338713422876, "acc_norm": 0.7046413502109705, "acc_norm_stderr": 0.029696338713422876 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6412556053811659, "acc_stderr": 0.03219079200419995, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.03219079200419995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7099236641221374, "acc_stderr": 0.03980066246467765, "acc_norm": 0.7099236641221374, "acc_norm_stderr": 0.03980066246467765 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7129629629629629, "acc_stderr": 0.04373313040914761, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.04373313040914761 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.656441717791411, "acc_stderr": 0.03731133519673893, "acc_norm": 0.656441717791411, "acc_norm_stderr": 0.03731133519673893 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8418803418803419, "acc_stderr": 0.023902325549560403, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.023902325549560403 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7854406130268199, "acc_stderr": 0.014680033956893346, "acc_norm": 0.7854406130268199, "acc_norm_stderr": 0.014680033956893346 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.615606936416185, "acc_stderr": 0.026189666966272035, "acc_norm": 0.615606936416185, "acc_norm_stderr": 0.026189666966272035 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.34972067039106147, "acc_stderr": 0.015949308790233645, "acc_norm": 0.34972067039106147, "acc_norm_stderr": 0.015949308790233645 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6568627450980392, "acc_stderr": 0.02718449890994161, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.02718449890994161 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6728395061728395, "acc_stderr": 0.026105673861409818, "acc_norm": 0.6728395061728395, "acc_norm_stderr": 0.026105673861409818 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.43617021276595747, "acc_stderr": 0.02958345203628407, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.02958345203628407 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.41590612777053454, "acc_stderr": 0.012588323850313627, "acc_norm": 0.41590612777053454, "acc_norm_stderr": 0.012588323850313627 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681393, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681393 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6045751633986928, "acc_stderr": 0.019780465954777515, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.019780465954777515 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6571428571428571, "acc_stderr": 0.030387262919547728, "acc_norm": 0.6571428571428571, "acc_norm_stderr": 0.030387262919547728 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7611940298507462, "acc_stderr": 0.03014777593540922, "acc_norm": 0.7611940298507462, "acc_norm_stderr": 0.03014777593540922 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4759036144578313, "acc_stderr": 0.038879718495972646, "acc_norm": 0.4759036144578313, "acc_norm_stderr": 0.038879718495972646 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7485380116959064, "acc_stderr": 0.033275044238468436, "acc_norm": 0.7485380116959064, "acc_norm_stderr": 0.033275044238468436 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.397796817625459, "mc1_stderr": 0.017133934248559638, "mc2": 0.5497966141695696, "mc2_stderr": 0.01557713395489198 }, "harness\|winogrande\|5": { "acc": 0.7474348855564326, "acc_stderr": 0.012211148449394105 }, "harness\|gsm8k\|5": { "acc": 0.3297952994692949, "acc_stderr": 0.01294995503057115 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha4	[ "region:us" ]	2023-12-18T06:49:32+00:00	{"pretty_name": "Evaluation run of MisterRid/wendigo-14b-alpha4", "dataset_summary": "Dataset automatically created during the evaluation run of model [MisterRid/wendigo-14b-alpha4](https://huggingface.co/MisterRid/wendigo-14b-alpha4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha4\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T06:46:37.615025](https://huggingface.co/datasets/open-llm-leaderboard/details_MisterRid__wendigo-14b-alpha4/blob/main/results_2023-12-18T06-46-37.615025.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\": 0.5958734764422213,\n \"acc_stderr\": 0.033567613925099785,\n \"acc_norm\": 0.6017569763815189,\n \"acc_norm_stderr\": 0.034261570709298174,\n \"mc1\": 0.397796817625459,\n \"mc1_stderr\": 0.017133934248559638,\n \"mc2\": 0.5497966141695696,\n \"mc2_stderr\": 0.01557713395489198\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5571672354948806,\n \"acc_stderr\": 0.0145155738733489,\n \"acc_norm\": 0.5930034129692833,\n \"acc_norm_stderr\": 0.014356399418009121\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.599681338378809,\n \"acc_stderr\": 0.004889615413144195,\n \"acc_norm\": 0.7964548894642501,\n \"acc_norm_stderr\": 0.004018115765954247\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544057,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544057\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6805555555555556,\n \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.6805555555555556,\n \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467383,\n \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467383\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137595,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137595\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7064516129032258,\n \"acc_stderr\": 0.025906087021319295,\n \"acc_norm\": 0.7064516129032258,\n \"acc_norm_stderr\": 0.025906087021319295\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.43842364532019706,\n \"acc_stderr\": 0.03491207857486518,\n \"acc_norm\": 0.43842364532019706,\n \"acc_norm_stderr\": 0.03491207857486518\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091706,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091706\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932026,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8290155440414507,\n \"acc_stderr\": 0.02717121368316453,\n \"acc_norm\": 0.8290155440414507,\n \"acc_norm_stderr\": 0.02717121368316453\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.024433016466052462,\n \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.024433016466052462\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.32222222222222224,\n \"acc_stderr\": 0.02849346509102859,\n \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.02849346509102859\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.030489911417673227,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.030489911417673227\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8110091743119267,\n \"acc_stderr\": 0.01678548115920363,\n \"acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.01678548115920363\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.030964517926923393,\n \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.030964517926923393\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7046413502109705,\n \"acc_stderr\": 0.029696338713422876,\n \"acc_norm\": 0.7046413502109705,\n \"acc_norm_stderr\": 0.029696338713422876\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6412556053811659,\n \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467765,\n \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467765\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.04373313040914761,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.04373313040914761\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.656441717791411,\n \"acc_stderr\": 0.03731133519673893,\n \"acc_norm\": 0.656441717791411,\n \"acc_norm_stderr\": 0.03731133519673893\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8418803418803419,\n \"acc_stderr\": 0.023902325549560403,\n \"acc_norm\": 0.8418803418803419,\n \"acc_norm_stderr\": 0.023902325549560403\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7854406130268199,\n \"acc_stderr\": 0.014680033956893346,\n \"acc_norm\": 0.7854406130268199,\n \"acc_norm_stderr\": 0.014680033956893346\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.615606936416185,\n \"acc_stderr\": 0.026189666966272035,\n \"acc_norm\": 0.615606936416185,\n \"acc_norm_stderr\": 0.026189666966272035\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.34972067039106147,\n \"acc_stderr\": 0.015949308790233645,\n \"acc_norm\": 0.34972067039106147,\n \"acc_norm_stderr\": 0.015949308790233645\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.6568627450980392,\n \"acc_stderr\": 0.02718449890994161,\n \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.02718449890994161\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 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"point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_18T06_46_37.615025", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T06-46-37.615025.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T06-46-37.615025.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_18T06_46_37.615025", "path": ["/details_harness\|gsm8k\|5_2023-12-18T06-46-37.615025.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-18T06-46-37.615025.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_18T06_46_37.615025", "path": ["/details_harness\|hellaswag\|10_2023-12-18T06-46-37.615025.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-18T06-46-37.615025.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_18T06_46_37.615025", "path": 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["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-46-37.615025.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T06-46-37.615025.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T06_46_37.615025", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-46-37.615025.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T06-46-37.615025.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T06_46_37.615025", "path": ["results_2023-12-18T06-46-37.615025.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T06-46-37.615025.parquet"]}]}]}	2023-12-18T06:50:17+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha4 Dataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha4 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T06:46:37.615025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha4\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:46:37.615025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha4\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T06:46:37.615025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 185, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of MisterRid/wendigo-14b-alpha4\n\n\n\nDataset automatically created during the evaluation run of model MisterRid/wendigo-14b-alpha4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T06:46:37.615025(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
66af6039a8be3505dabdd9bf4530e398baeed5ea	# Dataset Card for Evaluation run of migtissera/Synthia-7B-v3.0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [migtissera/Synthia-7B-v3.0](https://huggingface.co/migtissera/Synthia-7B-v3.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_migtissera__Synthia-7B-v3.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T07:10:18.408972](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-7B-v3.0/blob/main/results_2023-12-18T07-10-18.408972.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": 0.636302976950781, "acc_stderr": 0.032290524065091704, "acc_norm": 0.6421638147790109, "acc_norm_stderr": 0.03293460405300201, "mc1": 0.2998776009791922, "mc1_stderr": 0.01604035296671363, "mc2": 0.4384503915554312, "mc2_stderr": 0.014407548299846638 }, "harness\|arc:challenge\|25": { "acc": 0.5742320819112628, "acc_stderr": 0.014449464278868805, "acc_norm": 0.6245733788395904, "acc_norm_stderr": 0.014150631435111728 }, "harness\|hellaswag\|10": { "acc": 0.6357299342760406, "acc_stderr": 0.004802413919932666, "acc_norm": 0.8378809002190799, "acc_norm_stderr": 0.003678067994424467 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926604, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926604 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.041227371113703316, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.041227371113703316 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3941798941798942, "acc_stderr": 0.02516798233389414, "acc_norm": 0.3941798941798942, "acc_norm_stderr": 0.02516798233389414 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.0436031486007746, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.0436031486007746 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7580645161290323, "acc_stderr": 0.024362599693031096, "acc_norm": 0.7580645161290323, "acc_norm_stderr": 0.024362599693031096 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386414, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386414 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402534, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402534 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059288, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059288 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389023, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389023 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8256880733944955, "acc_stderr": 0.016265675632010354, "acc_norm": 0.8256880733944955, "acc_norm_stderr": 0.016265675632010354 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639318, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639318 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.759493670886076, "acc_stderr": 0.02782078198114968, "acc_norm": 0.759493670886076, "acc_norm_stderr": 0.02782078198114968 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057222, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057222 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7914110429447853, "acc_stderr": 0.03192193448934724, "acc_norm": 0.7914110429447853, "acc_norm_stderr": 0.03192193448934724 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.02220930907316562, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.02220930907316562 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8263090676883781, "acc_stderr": 0.013547415658662269, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.013547415658662269 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7023121387283237, "acc_stderr": 0.024617055388677003, "acc_norm": 0.7023121387283237, "acc_norm_stderr": 0.024617055388677003 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3217877094972067, "acc_stderr": 0.015624236160792579, "acc_norm": 0.3217877094972067, "acc_norm_stderr": 0.015624236160792579 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7647058823529411, "acc_stderr": 0.0242886194660461, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.0242886194660461 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.026082700695399665, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.026082700695399665 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460845, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460845 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.439374185136897, "acc_stderr": 0.012676014778580214, "acc_norm": 0.439374185136897, "acc_norm_stderr": 0.012676014778580214 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681404, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681404 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6830065359477124, "acc_stderr": 0.018824219512706214, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.018824219512706214 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7510204081632653, "acc_stderr": 0.027682979522960238, "acc_norm": 0.7510204081632653, "acc_norm_stderr": 0.027682979522960238 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2998776009791922, "mc1_stderr": 0.01604035296671363, "mc2": 0.4384503915554312, "mc2_stderr": 0.014407548299846638 }, "harness\|winogrande\|5": { "acc": 0.7790055248618785, "acc_stderr": 0.01166122363764341 }, "harness\|gsm8k\|5": { "acc": 0.400303260045489, "acc_stderr": 0.01349592643656644 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_migtissera__Synthia-7B-v3.0	[ "region:us" ]	2023-12-18T07:13:12+00:00	{"pretty_name": "Evaluation run of migtissera/Synthia-7B-v3.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/Synthia-7B-v3.0](https://huggingface.co/migtissera/Synthia-7B-v3.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_migtissera__Synthia-7B-v3.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T07:10:18.408972](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-7B-v3.0/blob/main/results_2023-12-18T07-10-18.408972.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\": 0.636302976950781,\n \"acc_stderr\": 0.032290524065091704,\n \"acc_norm\": 0.6421638147790109,\n \"acc_norm_stderr\": 0.03293460405300201,\n \"mc1\": 0.2998776009791922,\n \"mc1_stderr\": 0.01604035296671363,\n \"mc2\": 0.4384503915554312,\n \"mc2_stderr\": 0.014407548299846638\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5742320819112628,\n \"acc_stderr\": 0.014449464278868805,\n \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.014150631435111728\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6357299342760406,\n \"acc_stderr\": 0.004802413919932666,\n \"acc_norm\": 0.8378809002190799,\n \"acc_norm_stderr\": 0.003678067994424467\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926604,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926604\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7361111111111112,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3941798941798942,\n \"acc_stderr\": 0.02516798233389414,\n \"acc_norm\": 0.3941798941798942,\n \"acc_norm_stderr\": 0.02516798233389414\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.0436031486007746,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.0436031486007746\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7580645161290323,\n \"acc_stderr\": 0.024362599693031096,\n \"acc_norm\": 0.7580645161290323,\n \"acc_norm_stderr\": 0.024362599693031096\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059288,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059288\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8256880733944955,\n \"acc_stderr\": 0.016265675632010354,\n \"acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.016265675632010354\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.759493670886076,\n \"acc_stderr\": 0.02782078198114968,\n \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.02782078198114968\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057222,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057222\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934724,\n \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934724\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.02220930907316562,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.02220930907316562\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8263090676883781,\n \"acc_stderr\": 0.013547415658662269,\n \"acc_norm\": 0.8263090676883781,\n \"acc_norm_stderr\": 0.013547415658662269\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7023121387283237,\n \"acc_stderr\": 0.024617055388677003,\n \"acc_norm\": 0.7023121387283237,\n \"acc_norm_stderr\": 0.024617055388677003\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3217877094972067,\n \"acc_stderr\": 0.015624236160792579,\n \"acc_norm\": 0.3217877094972067,\n \"acc_norm_stderr\": 0.015624236160792579\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.0242886194660461,\n \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.0242886194660461\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n \"acc_stderr\": 0.026082700695399665,\n \"acc_norm\": 0.6977491961414791,\n \"acc_norm_stderr\": 0.026082700695399665\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.439374185136897,\n \"acc_stderr\": 0.012676014778580214,\n \"acc_norm\": 0.439374185136897,\n \"acc_norm_stderr\": 0.012676014778580214\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681404,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681404\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706214,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706214\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6272727272727273,\n \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960238,\n \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960238\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2998776009791922,\n \"mc1_stderr\": 0.01604035296671363,\n \"mc2\": 0.4384503915554312,\n \"mc2_stderr\": 0.014407548299846638\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7790055248618785,\n \"acc_stderr\": 0.01166122363764341\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.400303260045489,\n \"acc_stderr\": 0.01349592643656644\n }\n}\n```", "repo_url": "https://huggingface.co/migtissera/Synthia-7B-v3.0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", 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The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T07:10:18.408972(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of migtissera/Synthia-7B-v3.0\n\n\n\nDataset automatically created during the evaluation run of model migtissera/Synthia-7B-v3.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T07:10:18.408972(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of migtissera/Synthia-7B-v3.0\n\n\n\nDataset automatically created during the evaluation run of model migtissera/Synthia-7B-v3.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T07:10:18.408972(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 187, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of migtissera/Synthia-7B-v3.0\n\n\n\nDataset automatically created during the evaluation run of model migtissera/Synthia-7B-v3.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T07:10:18.408972(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
dcd1591ac0bd8464fe68d823a5ffd206d24968ef	## Dataset Details ![image/png](./image.jpg) ## Description The "DataX" dataset is a curated collection combining data generated by large language models (LLMs) and information scraped from Wikipedia. It spans a vast array of topics, providing a rich resource for tasks such as text generation, text-to-text generation, summarization, and conversational models. With over 1.7 million examples, it stands as a significant asset for training robust and diverse machine learning and deep learning models. ## Completeness and Future Work While the dataset currently offers a substantial volume of data, efforts are ongoing to expand its scope and utility. Future updates may include additional splits for validation and testing, broader topic coverage, and enhanced metadata for even richer model training possibilities. ### Intended Use The "datax" dataset is intended for use in academic research and practical applications within the fields of natural language processing (NLP) and machine learning (ML). It is particularly suited for training and evaluating models on a wide range of tasks. Researchers and developers are encouraged to utilize this dataset to explore innovative NLP techniques and to benchmark the performance of models in a variety of contexts. ### Limitations This dataset, while extensive, represents a snapshot of information available up to the year 2023. Users should be aware of the dataset's temporal context when applying it to contemporary models and research. Furthermore, the dataset's language coverage is currently limited to English, which may restrict its applicability for multilingual or non-English projects. ### Ethical Considerations The compilation of this dataset involved collecting data generated by LLMs and scraping content from Wikipedia. While every effort has been made to ensure the dataset adheres to ethical guidelines and respects copyright laws, users are advised to consider the potential for bias and the representation of diverse perspectives within the data. Additionally, users should evaluate the dataset's appropriateness for their specific research or application needs, particularly in sensitive or regulated domains. ## Usage You can use this dataset by loading it using the Hugging Face datasets library or any other relevant method. #### Example Usage ```python from datasets import load_dataset # Load the dataset data = load_dataset('GunA-SD/DataX') ``` ## Citation: Please cite this dataset in your publications if it helps your research: ``` @misc{DataX, title = {DataX: A Mixture of LLM Generated and Wiki Scraped Data}, author = {Gunasekar}, year = {2023}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/GunA-SD/DataX} } ``` ## License This dataset is distributed under the Apache-2.0 License. Full license text is available at [LICENSE](https://apache.org/licenses/LICENSE-2.0).	GunA-SD/DataX	[ "task_categories:text-generation", "task_categories:summarization", "task_categories:conversational", "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "region:us" ]	2023-12-18T07:26:51+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "summarization", "conversational"], "dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "Topic", "dtype": "string"}, {"name": "Content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5397321128, "num_examples": 1720117}], "download_size": 3148810475, "dataset_size": 5397321128}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2024-02-14T17:18:34+00:00	[]	[ "en" ]	TAGS #task_categories-text-generation #task_categories-summarization #task_categories-conversational #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us	## Dataset Details !image/png ## Description The "DataX" dataset is a curated collection combining data generated by large language models (LLMs) and information scraped from Wikipedia. It spans a vast array of topics, providing a rich resource for tasks such as text generation, text-to-text generation, summarization, and conversational models. With over 1.7 million examples, it stands as a significant asset for training robust and diverse machine learning and deep learning models. ## Completeness and Future Work While the dataset currently offers a substantial volume of data, efforts are ongoing to expand its scope and utility. Future updates may include additional splits for validation and testing, broader topic coverage, and enhanced metadata for even richer model training possibilities. ### Intended Use The "datax" dataset is intended for use in academic research and practical applications within the fields of natural language processing (NLP) and machine learning (ML). It is particularly suited for training and evaluating models on a wide range of tasks. Researchers and developers are encouraged to utilize this dataset to explore innovative NLP techniques and to benchmark the performance of models in a variety of contexts. ### Limitations This dataset, while extensive, represents a snapshot of information available up to the year 2023. Users should be aware of the dataset's temporal context when applying it to contemporary models and research. Furthermore, the dataset's language coverage is currently limited to English, which may restrict its applicability for multilingual or non-English projects. ### Ethical Considerations The compilation of this dataset involved collecting data generated by LLMs and scraping content from Wikipedia. While every effort has been made to ensure the dataset adheres to ethical guidelines and respects copyright laws, users are advised to consider the potential for bias and the representation of diverse perspectives within the data. Additionally, users should evaluate the dataset's appropriateness for their specific research or application needs, particularly in sensitive or regulated domains. ## Usage You can use this dataset by loading it using the Hugging Face datasets library or any other relevant method. #### Example Usage : Please cite this dataset in your publications if it helps your research: ## License This dataset is distributed under the Apache-2.0 License. Full license text is available at LICENSE.	[ "## Dataset Details\n\n!image/png", "## Description\n\nThe \"DataX\" dataset is a curated collection combining data generated by large language models (LLMs) and information scraped from Wikipedia. \nIt spans a vast array of topics, providing a rich resource for tasks such as text generation, text-to-text generation, summarization, and conversational models.\nWith over 1.7 million examples, it stands as a significant asset for training robust and diverse machine learning and deep learning models.", "## Completeness and Future Work\n\nWhile the dataset currently offers a substantial volume of data, efforts are ongoing to expand its scope and utility. \nFuture updates may include additional splits for validation and testing, broader topic coverage, and enhanced metadata for even richer model training possibilities.", "### Intended Use\n\nThe \"datax\" dataset is intended for use in academic research and practical applications within the fields of natural language processing (NLP) and machine learning (ML).\nIt is particularly suited for training and evaluating models on a wide range of tasks. Researchers and developers are encouraged to utilize this dataset to explore innovative \nNLP techniques and to benchmark the performance of models in a variety of contexts.", "### Limitations\n\nThis dataset, while extensive, represents a snapshot of information available up to the year 2023. Users should be aware of the dataset's temporal context when applying it to contemporary models and research. \nFurthermore, the dataset's language coverage is currently limited to English, which may restrict its applicability for multilingual or non-English projects.", "### Ethical Considerations\n\nThe compilation of this dataset involved collecting data generated by LLMs and scraping content from Wikipedia. While every effort has been made to ensure the dataset adheres to ethical guidelines and respects copyright laws,\nusers are advised to consider the potential for bias and the representation of diverse perspectives within the data. Additionally, users should evaluate the dataset's appropriateness for their specific research or application needs,\nparticularly in sensitive or regulated domains.", "## Usage\n\nYou can use this dataset by loading it using the Hugging Face datasets library or any other relevant method.", "#### Example Usage\n\n\n\n:\n\nPlease cite this dataset in your publications if it helps your research:", "## License\n\nThis dataset is distributed under the Apache-2.0 License. Full license text is available at LICENSE." ]	[ "TAGS\n#task_categories-text-generation #task_categories-summarization #task_categories-conversational #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n", "## Dataset Details\n\n!image/png", "## Description\n\nThe \"DataX\" dataset is a curated collection combining data generated by large language models (LLMs) and information scraped from Wikipedia. \nIt spans a vast array of topics, providing a rich resource for tasks such as text generation, text-to-text generation, summarization, and conversational models.\nWith over 1.7 million examples, it stands as a significant asset for training robust and diverse machine learning and deep learning models.", "## Completeness and Future Work\n\nWhile the dataset currently offers a substantial volume of data, efforts are ongoing to expand its scope and utility. \nFuture updates may include additional splits for validation and testing, broader topic coverage, and enhanced metadata for even richer model training possibilities.", "### Intended Use\n\nThe \"datax\" dataset is intended for use in academic research and practical applications within the fields of natural language processing (NLP) and machine learning (ML).\nIt is particularly suited for training and evaluating models on a wide range of tasks. Researchers and developers are encouraged to utilize this dataset to explore innovative \nNLP techniques and to benchmark the performance of models in a variety of contexts.", "### Limitations\n\nThis dataset, while extensive, represents a snapshot of information available up to the year 2023. Users should be aware of the dataset's temporal context when applying it to contemporary models and research. \nFurthermore, the dataset's language coverage is currently limited to English, which may restrict its applicability for multilingual or non-English projects.", "### Ethical Considerations\n\nThe compilation of this dataset involved collecting data generated by LLMs and scraping content from Wikipedia. While every effort has been made to ensure the dataset adheres to ethical guidelines and respects copyright laws,\nusers are advised to consider the potential for bias and the representation of diverse perspectives within the data. Additionally, users should evaluate the dataset's appropriateness for their specific research or application needs,\nparticularly in sensitive or regulated domains.", "## Usage\n\nYou can use this dataset by loading it using the Hugging Face datasets library or any other relevant method.", "#### Example Usage\n\n\n\n:\n\nPlease cite this dataset in your publications if it helps your research:", "## License\n\nThis dataset is distributed under the Apache-2.0 License. Full license text is available at LICENSE." ]	[ 61, 8, 102, 62, 94, 85, 112, 29, 22, 26 ]	[ "passage: TAGS\n#task_categories-text-generation #task_categories-summarization #task_categories-conversational #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n## Dataset Details\n\n!image/png## Description\n\nThe \"DataX\" dataset is a curated collection combining data generated by large language models (LLMs) and information scraped from Wikipedia. \nIt spans a vast array of topics, providing a rich resource for tasks such as text generation, text-to-text generation, summarization, and conversational models.\nWith over 1.7 million examples, it stands as a significant asset for training robust and diverse machine learning and deep learning models.## Completeness and Future Work\n\nWhile the dataset currently offers a substantial volume of data, efforts are ongoing to expand its scope and utility. \nFuture updates may include additional splits for validation and testing, broader topic coverage, and enhanced metadata for even richer model training possibilities.### Intended Use\n\nThe \"datax\" dataset is intended for use in academic research and practical applications within the fields of natural language processing (NLP) and machine learning (ML).\nIt is particularly suited for training and evaluating models on a wide range of tasks. Researchers and developers are encouraged to utilize this dataset to explore innovative \nNLP techniques and to benchmark the performance of models in a variety of contexts.### Limitations\n\nThis dataset, while extensive, represents a snapshot of information available up to the year 2023. Users should be aware of the dataset's temporal context when applying it to contemporary models and research. \nFurthermore, the dataset's language coverage is currently limited to English, which may restrict its applicability for multilingual or non-English projects." ]
1f79ad3d711648fc90d5c201880013dab12829b6	# Dataset Card for Ultra Feedback Dutch ## Dataset Description This is a Dutch recreation of [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), which in turn is based on the source dataset [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback). It contains single-turn responses to a prompt by two different systems, intended for the preference optimalisation of language models (such as DPO). The chosen answers is always `gpt-4-1106-preview`, the rejected answer is always [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat). Note that it is a naive assumption that GPT 4 is always better than another language model. For a better balance, some kind of scoring of the answers should be done, with, for instance, Argilla's [`distilabel`](https://distilabel.argilla.io/latest/) but I do not have the funds to run this rating with a state-of-the-art model (like GPT4). If you do, please improve dataset and share the result! ## Dataset Structure The dataset has two splits. The number of examples per split is shown as follows: \| train_prefs \| test_prefs \| \|:-------:\|:-----------:\| \| 48,228 \| 5,359 \| That means that around 11.11% of the data is reserved for testing. One may find that this dataset is smaller than the original Ultra Feedback dataset. This is caused by the OpenAI automatic filtering. More information in the sections below. ### Data Instances The dataset does not have system messages. ```python { 'prompt': 'Wat is de redenering en vervolgens het antwoord? Ja / nee, is het volgende een verifieerbaar feit?\n"Boten worden vaak gebouwd met metselwerk."', 'prompt_id': '1db9a914ba87ef777ff60e185962eeb5257e122c1c408464156e075c324df5ea', 'chosen': [ {'content': 'Wat is de redenering en vervolgens het antwoord? Ja / nee, is het volgende een verifieerbaar feit?\n"Boten worden vaak gebouwd met metselwerk."', 'role': 'user'}, {'content': 'Nee, dit is geen verifieerbaar feit omdat boten doorgaans niet met metselwerk worden gebouwd. De redenering hierachter is dat metselwerk het proces is van het bouwen met bakstenen, stenen, betonblokken, en dergelijke materialen die met mortel (een mengsel van zand, een bindmiddel zoals kalk of cement, en water) worden verbonden. Deze materialen zijn zwaar en niet geschikt voor de constructie van boten, omdat men bij de bouw van boten streeft naar materiaal dat licht, sterk en waterbestendig is.\n\nBoten moeten drijven en bewegen door het water, en massieve, zware materialen zoals die gebruikt in metselwerk zouden een boot doen zinken. De meeste boten worden daarom gebouwd met materialen zoals hout, glasvezel, aluminium, en staal, die sterk en licht zijn, en zijn ontworpen om drijfvermogen te bieden. Concluderend kan worden gesteld dat de uitspraak "Boten worden vaak gebouwd met metselwerk" niet waar is.', 'role': 'assistant'} ], 'rejected': [ {'content': 'Wat is de redenering en vervolgens het antwoord? Ja / nee, is het volgende een verifieerbaar feit?\n"Boten worden vaak gebouwd met metselwerk."', 'role': 'user'}, {'content': 'Ik heb geen toegang tot context om de redenering te bepalen. Uit de verstrekte zin kan niet worden afgeleid wat het perspectief is van de auteur en wat wordt bedoeld met \'meestal\'. \n\nEchter, de verklaring: "Boten worden gebouwd met cement" is niet waar. Hoewel boten worden gebouwd met verschillende materialen, is cement niet een daarvan.', 'role': 'assistant'} ] } ``` ### Data Fields - prompt: the initial user prompt - prompt_id: the unique hash of the prompt - chosen: (gpt-4-1106-preview) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content - rejected: (GEITje-7B-Chat) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content ## Dataset Creation This dataset was created with [this repository](https://github.com/BramVanroy/dutch-instruction-datasets/). The `translate-hf` script for translating the columns, and `answer-hf` to create answers for the translated query. So in a first step, the English first user message was translated with `gpt-4-1106-preview` to Dutch. This prompt is then fed to `gpt-4-1106-preview` and `GEITje-7B-chat` to generate responses, so only the user prompt is translated. The data is therefore single-turn. ### Data filtering On top of the automatic content filtering and parsing issues, additional filtering was also done. All the data is preserved in separate branches if you would like a historical view of the process of how the data was created. - `1-gpt-4-turbo-instruction-translations`: the initial translations of the first user message/prompt - `2-gpt-4-turbo-answers`: adds the answers by GPT4 - `3-geitje-7b-chat-answers`: adds the answers by GEITje-7B-chat - `4-llama-2-13b-chat-dutch-answers`: adds the answers by Llama 2 13B Chat Dutch - but because of the poor quality this data is not used in the final dataset - `5-merged`: merges the instructions and the answers of the previous branches - `6-gpt4-geitje`: removes Llama 2 output - `7-lid`: added [language identification](https://github.com/BramVanroy/dutch-instruction-datasets/blob/main/src/dutch_data/scripts/add_lid.py) to the columns with [fastText](https://huggingface.co/facebook/fasttext-language-identification), which is based on the concatenation of all `content` keys in the `chosen` and `rejected` column. - `8-filtered`: removed samples where the detected text was not Dutch. (Note that this may remove translation-focused samples!). [Other filters](https://github.com/BramVanroy/dutch-instruction-datasets/blob/main/src/dutch_data/scripts/filter_dutch.py) - samples with non-Latin characters are removed (very strict filtering, removes any translation tasks with non-Latin languages) - samples with occurrences of "AI-assistent" or "AI-taalmodel" (and other derivations) are removed because these are often responses in the sense of "As an AI model, I cannot ...", which is not too useful - samples with mentions of ChatGPT, GPT 3/4, OpenAI or ShareGPT are removed - samples with mentions of the typical "knowledge cutoff" are removed - samples with apologies such as "spijt me" are removed, as we are more interested in factual information and content-filled responses - `main`: the main, default branch. Removes all "irrelevant" columns (like English messages, language identification) ### Source Data #### Initial Data Collection and Normalization Initial data binarization by [HuggingFaceH4](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), which in turn started from [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback). #### Who are the source language producers? The initial data Ultra Feedback dataset was created with a variety of open-source models as well as OpenAI models. This new dataset used `gpt-4-1106-preview` and [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat) to create a Dutch version. ## Considerations for Using the Data Note that except for a manual analysis of around 100 random samples, this dataset has not been verified or checked for issues. However, the OpenAI filters blocked requests for translation and generation, which may indicate that some of the original prompts may contained ambiguous or potentially inappropriate content that has now been filtered. During checking, I found that the translations of the prompt sometimes have issues, where parts of the content was not translated. This can happen when the model interprets the input as a command, for instance, instead of translating it. ### Discussion of Biases As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. It is likely that biases remain in the dataset so use with caution. ### Licensing Information This dataset was generated (either in part or in full) with GPT-4 (`gpt-4-1106-preview`), OpenAI’s large-scale language-generation model. Therefore commercial usage is not allowed. If you use this dataset, you must also follow the [Sharing](https://openai.com/policies/sharing-publication-policy) and [Usage](https://openai.com/policies/usage-policies) policies. ### Contributions Thanks to Michiel Buisman of [UWV](https://www.uwv.nl/particulieren/index.aspx) for reaching out and making the creation of this dataset possible with access to Azure's API.	BramVanroy/ultra_feedback_dutch	[ "task_categories:conversational", "task_categories:text-generation", "size_categories:10K<n<100K", "language:nl", "license:cc-by-nc-4.0", "region:us" ]	2023-12-18T07:34:05+00:00	{"language": ["nl"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational", "text-generation"], "pretty_name": "Ultra Feedback Dutch", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_prefs", "num_bytes": 226072999.32356727, "num_examples": 48228}, {"name": "test_prefs", "num_bytes": 25120784.676432718, "num_examples": 5359}], "download_size": 145273062, "dataset_size": 251193784}, "configs": [{"config_name": "default", "data_files": [{"split": "train_prefs", "path": "data/train_prefs-"}, {"split": "test_prefs", "path": "data/test_prefs-"}]}]}	2024-02-01T12:49:40+00:00	[]	[ "nl" ]	TAGS #task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #license-cc-by-nc-4.0 #region-us	Dataset Card for Ultra Feedback Dutch ===================================== Dataset Description ------------------- This is a Dutch recreation of HuggingFaceH4/ultrafeedback\_binarized, which in turn is based on the source dataset UltraFeedback. It contains single-turn responses to a prompt by two different systems, intended for the preference optimalisation of language models (such as DPO). The chosen answers is always 'gpt-4-1106-preview', the rejected answer is always Rijgersberg/GEITje-7B-chat. Note that it is a naive assumption that GPT 4 is always better than another language model. For a better balance, some kind of scoring of the answers should be done, with, for instance, Argilla's 'distilabel' but I do not have the funds to run this rating with a state-of-the-art model (like GPT4). If you do, please improve dataset and share the result! Dataset Structure ----------------- The dataset has two splits. The number of examples per split is shown as follows: That means that around 11.11% of the data is reserved for testing. One may find that this dataset is smaller than the original Ultra Feedback dataset. This is caused by the OpenAI automatic filtering. More information in the sections below. ### Data Instances The dataset does not have system messages. ### Data Fields * prompt: the initial user prompt * prompt\_id: the unique hash of the prompt * chosen: (gpt-4-1106-preview) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content * rejected: (GEITje-7B-Chat) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content Dataset Creation ---------------- This dataset was created with this repository. The 'translate-hf' script for translating the columns, and 'answer-hf' to create answers for the translated query. So in a first step, the English first user message was translated with 'gpt-4-1106-preview' to Dutch. This prompt is then fed to 'gpt-4-1106-preview' and 'GEITje-7B-chat' to generate responses, so only the user prompt is translated. The data is therefore single-turn. ### Data filtering On top of the automatic content filtering and parsing issues, additional filtering was also done. All the data is preserved in separate branches if you would like a historical view of the process of how the data was created. * '1-gpt-4-turbo-instruction-translations': the initial translations of the first user message/prompt * '2-gpt-4-turbo-answers': adds the answers by GPT4 * '3-geitje-7b-chat-answers': adds the answers by GEITje-7B-chat * '4-llama-2-13b-chat-dutch-answers': adds the answers by Llama 2 13B Chat Dutch - but because of the poor quality this data is not used in the final dataset * '5-merged': merges the instructions and the answers of the previous branches * '6-gpt4-geitje': removes Llama 2 output * '7-lid': added language identification to the columns with fastText, which is based on the concatenation of all 'content' keys in the 'chosen' and 'rejected' column. * '8-filtered': removed samples where the detected text was not Dutch. (Note that this may remove translation-focused samples!). Other filters + samples with non-Latin characters are removed (very strict filtering, removes any translation tasks with non-Latin languages) + samples with occurrences of "AI-assistent" or "AI-taalmodel" (and other derivations) are removed because these are often responses in the sense of "As an AI model, I cannot ...", which is not too useful + samples with mentions of ChatGPT, GPT 3/4, OpenAI or ShareGPT are removed + samples with mentions of the typical "knowledge cutoff" are removed + samples with apologies such as "spijt me" are removed, as we are more interested in factual information and content-filled responses * 'main': the main, default branch. Removes all "irrelevant" columns (like English messages, language identification) ### Source Data #### Initial Data Collection and Normalization Initial data binarization by HuggingFaceH4, which in turn started from openbmb/UltraFeedback. #### Who are the source language producers? The initial data Ultra Feedback dataset was created with a variety of open-source models as well as OpenAI models. This new dataset used 'gpt-4-1106-preview' and Rijgersberg/GEITje-7B-chat to create a Dutch version. Considerations for Using the Data --------------------------------- Note that except for a manual analysis of around 100 random samples, this dataset has not been verified or checked for issues. However, the OpenAI filters blocked requests for translation and generation, which may indicate that some of the original prompts may contained ambiguous or potentially inappropriate content that has now been filtered. During checking, I found that the translations of the prompt sometimes have issues, where parts of the content was not translated. This can happen when the model interprets the input as a command, for instance, instead of translating it. ### Discussion of Biases As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. It is likely that biases remain in the dataset so use with caution. ### Licensing Information This dataset was generated (either in part or in full) with GPT-4 ('gpt-4-1106-preview'), OpenAI’s large-scale language-generation model. Therefore commercial usage is not allowed. If you use this dataset, you must also follow the Sharing and Usage policies. ### Contributions Thanks to Michiel Buisman of UWV for reaching out and making the creation of this dataset possible with access to Azure's API.	[ "### Data Instances\n\n\nThe dataset does not have system messages.", "### Data Fields\n\n\n* prompt: the initial user prompt\n* prompt\\_id: the unique hash of the prompt\n* chosen: (gpt-4-1106-preview) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content\n* rejected: (GEITje-7B-Chat) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content\n\n\nDataset Creation\n----------------\n\n\nThis dataset was created with this repository. The 'translate-hf' script for translating the columns, and 'answer-hf' to create answers for the translated query.\n\n\nSo in a first step, the English first user message was translated with 'gpt-4-1106-preview' to Dutch. This prompt is then fed to 'gpt-4-1106-preview' and 'GEITje-7B-chat' to generate responses, so only the user prompt is translated. The data is therefore single-turn.", "### Data filtering\n\n\nOn top of the automatic content filtering and parsing issues, additional filtering was also done. All the data is preserved in separate branches if you would like a historical view of the process of how the data was created.\n\n\n* '1-gpt-4-turbo-instruction-translations': the initial translations of the first user message/prompt\n* '2-gpt-4-turbo-answers': adds the answers by GPT4\n* '3-geitje-7b-chat-answers': adds the answers by GEITje-7B-chat\n* '4-llama-2-13b-chat-dutch-answers': adds the answers by Llama 2 13B Chat Dutch - but because of the poor quality this data is not used in the final dataset\n* '5-merged': merges the instructions and the answers of the previous branches\n* '6-gpt4-geitje': removes Llama 2 output\n* '7-lid': added language identification to the columns with fastText, which is based on the concatenation of all 'content' keys in the 'chosen' and 'rejected' column.\n* '8-filtered': removed samples where the detected text was not Dutch. (Note that this may remove translation-focused samples!). Other filters\n\t+ samples with non-Latin characters are removed (very strict filtering, removes any translation tasks with non-Latin languages)\n\t+ samples with occurrences of \"AI-assistent\" or \"AI-taalmodel\" (and other derivations) are removed because these are often responses in the sense of \"As an AI model, I cannot ...\", which is not too useful\n\t+ samples with mentions of ChatGPT, GPT 3/4, OpenAI or ShareGPT are removed\n\t+ samples with mentions of the typical \"knowledge cutoff\" are removed\n\t+ samples with apologies such as \"spijt me\" are removed, as we are more interested in factual information and content-filled responses\n* 'main': the main, default branch. Removes all \"irrelevant\" columns (like English messages, language identification)", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nInitial data binarization by HuggingFaceH4, which in turn started from openbmb/UltraFeedback.", "#### Who are the source language producers?\n\n\nThe initial data Ultra Feedback dataset was created with a variety of open-source models as well as OpenAI models. This new dataset used 'gpt-4-1106-preview' and Rijgersberg/GEITje-7B-chat to create a Dutch version.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nNote that except for a manual analysis of around 100 random samples, this dataset has not been verified or checked for issues. However, the OpenAI filters blocked requests for translation and generation, which may indicate that some of the original prompts may contained ambiguous or potentially inappropriate content that has now been filtered.\n\n\nDuring checking, I found that the translations of the prompt sometimes have issues, where parts of the content was not translated. This can happen when the model interprets the input as a command, for instance, instead of translating it.", "### Discussion of Biases\n\n\nAs with any machine-generated texts, users should be aware of potential biases that are included in this dataset. It is likely that biases remain in the dataset so use with caution.", "### Licensing Information\n\n\nThis dataset was generated (either in part or in full) with GPT-4 ('gpt-4-1106-preview'), OpenAI’s large-scale language-generation model. Therefore commercial usage is not allowed.\n\n\nIf you use this dataset, you must also follow the Sharing and Usage policies.", "### Contributions\n\n\nThanks to Michiel Buisman of UWV for reaching out and making the creation of this dataset possible with access to Azure's API." ]	[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #license-cc-by-nc-4.0 #region-us \n", "### Data Instances\n\n\nThe dataset does not have system messages.", "### Data Fields\n\n\n* prompt: the initial user prompt\n* prompt\\_id: the unique hash of the prompt\n* chosen: (gpt-4-1106-preview) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content\n* rejected: (GEITje-7B-Chat) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content\n\n\nDataset Creation\n----------------\n\n\nThis dataset was created with this repository. The 'translate-hf' script for translating the columns, and 'answer-hf' to create answers for the translated query.\n\n\nSo in a first step, the English first user message was translated with 'gpt-4-1106-preview' to Dutch. This prompt is then fed to 'gpt-4-1106-preview' and 'GEITje-7B-chat' to generate responses, so only the user prompt is translated. The data is therefore single-turn.", "### Data filtering\n\n\nOn top of the automatic content filtering and parsing issues, additional filtering was also done. All the data is preserved in separate branches if you would like a historical view of the process of how the data was created.\n\n\n* '1-gpt-4-turbo-instruction-translations': the initial translations of the first user message/prompt\n* '2-gpt-4-turbo-answers': adds the answers by GPT4\n* '3-geitje-7b-chat-answers': adds the answers by GEITje-7B-chat\n* '4-llama-2-13b-chat-dutch-answers': adds the answers by Llama 2 13B Chat Dutch - but because of the poor quality this data is not used in the final dataset\n* '5-merged': merges the instructions and the answers of the previous branches\n* '6-gpt4-geitje': removes Llama 2 output\n* '7-lid': added language identification to the columns with fastText, which is based on the concatenation of all 'content' keys in the 'chosen' and 'rejected' column.\n* '8-filtered': removed samples where the detected text was not Dutch. (Note that this may remove translation-focused samples!). Other filters\n\t+ samples with non-Latin characters are removed (very strict filtering, removes any translation tasks with non-Latin languages)\n\t+ samples with occurrences of \"AI-assistent\" or \"AI-taalmodel\" (and other derivations) are removed because these are often responses in the sense of \"As an AI model, I cannot ...\", which is not too useful\n\t+ samples with mentions of ChatGPT, GPT 3/4, OpenAI or ShareGPT are removed\n\t+ samples with mentions of the typical \"knowledge cutoff\" are removed\n\t+ samples with apologies such as \"spijt me\" are removed, as we are more interested in factual information and content-filled responses\n* 'main': the main, default branch. Removes all \"irrelevant\" columns (like English messages, language identification)", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nInitial data binarization by HuggingFaceH4, which in turn started from openbmb/UltraFeedback.", "#### Who are the source language producers?\n\n\nThe initial data Ultra Feedback dataset was created with a variety of open-source models as well as OpenAI models. This new dataset used 'gpt-4-1106-preview' and Rijgersberg/GEITje-7B-chat to create a Dutch version.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nNote that except for a manual analysis of around 100 random samples, this dataset has not been verified or checked for issues. However, the OpenAI filters blocked requests for translation and generation, which may indicate that some of the original prompts may contained ambiguous or potentially inappropriate content that has now been filtered.\n\n\nDuring checking, I found that the translations of the prompt sometimes have issues, where parts of the content was not translated. This can happen when the model interprets the input as a command, for instance, instead of translating it.", "### Discussion of Biases\n\n\nAs with any machine-generated texts, users should be aware of potential biases that are included in this dataset. It is likely that biases remain in the dataset so use with caution.", "### Licensing Information\n\n\nThis dataset was generated (either in part or in full) with GPT-4 ('gpt-4-1106-preview'), OpenAI’s large-scale language-generation model. Therefore commercial usage is not allowed.\n\n\nIf you use this dataset, you must also follow the Sharing and Usage policies.", "### Contributions\n\n\nThanks to Michiel Buisman of UWV for reaching out and making the creation of this dataset possible with access to Azure's API." ]	[ 56, 15, 233, 496, 4, 40, 206, 53, 77, 37 ]	[ "passage: TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #license-cc-by-nc-4.0 #region-us \n### Data Instances\n\n\nThe dataset does not have system messages.### Data Fields\n\n\n* prompt: the initial user prompt\n* prompt\\_id: the unique hash of the prompt\n* chosen: (gpt-4-1106-preview) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content\n* rejected: (GEITje-7B-Chat) list of messages (dictionaries) where each dictionary has a role (user, assistant) and content\n\n\nDataset Creation\n----------------\n\n\nThis dataset was created with this repository. The 'translate-hf' script for translating the columns, and 'answer-hf' to create answers for the translated query.\n\n\nSo in a first step, the English first user message was translated with 'gpt-4-1106-preview' to Dutch. This prompt is then fed to 'gpt-4-1106-preview' and 'GEITje-7B-chat' to generate responses, so only the user prompt is translated. The data is therefore single-turn." ]
9fa4db3f4a22597d093f23b9ce64bb63d9b215e8	Since automatic open source benchmark leaderboard got flooded with incoherent overtrained cheater meme models, I decided to take the matters in my own hands and create my own set of proprietary tests. The aim of these tests is not to see how smart the model is, but to see how good it is at execution of commands and creative writing in a reasonably quantifiable way. All tests are executed with temperature and top P≈0 and rep. penalty=1 in koboldcpp. Model-appropriate format is used, unless it doesn't work. Currently I have the following tests: ## B-test: This test is designed to establish the baseline of the model. It consists of a main task and a bunch of text, which model has to ignore while still executing the task. If the model refuses or fails to comply in a logical way immediately, it fails(0/3). After the initial request question it will get bombarded with text, it gets 1 point for reaching the first checkpoint(1/3). It will get another point for passing the test fully(2/3) and a final point for exiting the test successfully(3/3) ## C-test: Like B-test, but the task is simpler and the distracting text is way more annoying. Since the task is much simpler there are fewer points to gain. Model gets 1 point for passing main distractions and another point for successfully exiting the task. Model gets penalized for writing more than necessary, eg (Note: as an AI language model...). ## D-test: This test is designed around breaking expectations. It consists of a common math trick, but with a twist. The twist is that there is no math involved, just reading. It also has an extensive section at the end to guide the model into breaking the overtrained conditioning. Models will get 1 point for getting the answer right and up to 2 points for the right reasoning. ## P-test: Poems. Model passes each poem test for writing coherently and in rhyme. 1 point for each poem. 6 in total. After seeing Miqu-120b succeed at positive writing and fail miserably at negative, I decided to revise the test a little bit by adjusting the ratios. Assume that all models prior and including Miqu-120b were run on old set, and newer ones will be run on the revised set. ## S-test: Stylized writing. Models are asked to explain a concept in a distinct writing style or as if they are a character. Up to 1 point for each style. Models are penalized for failing to explain the concept or to keep the style all the way through the explaination. 8 in total. Note: not very reliable due to large human factor(±1). Take with a grain of salt. # What does each of the tests measure I dont understand111!!!11! BCD=following commands PS=creative writing # RESULTS ![This table shows the results](llm-results.png) In the table above you can see the results visiualized. You can find pure data in file [LLM-test.csv](LLM-test.csv) What they show is quite interesting: - If a model can't pass any of the BCD tests, it is most likely braindead or very filtered(kinda same lol) - If SP score of the model is very low it's writing style is dry - Creative parent(Euryale) + creative parent(Xwin)=creative child(Goliath) - Creative parent(Euryale) + dry parent(Nous-Hermes) + drier parent(SynthIA)=dry-ish child(Venus) - Dry parent(Nous-Hermes) + creative parent(Xwin) + creative parent(Mythospice)=creative child(lzlv) - Cheater meme model(una-cybertron) was somewhat creative, but braindead - Base model self-merge(Dicephal-123B) increased creativity, but didn't add extra prompt compliance - All my attempts to extend the context of XWin and Llama by using [Yukang's](https://huggingface.co/Yukang) loras have led to drastic decrease in creativity and coherence of the models :( - Miqu is currently the best 32k model according to this benchmark - Miqu-120b is the second model after ChatGPT that has 100% passed S-test! # More tests? Feel free to suggest more models for testing by opening new discussion. Mention model name, size and why do you want to test it. # Limitations - All tests were only done once. - Human factor plays a huge role in SP tests. After redoing some of the tests I noticed ±1 variation for S-test and ±0.5 variation for P-test. (Xwin is likely underrated and Spicyboros is likely overrated in S-test.) - Be critical of my own models! Since I have access to the benchmark, I can game it and rig it all I want and NOBODY can stop me. # Can it be rigged/gamed? Not sure. I've tried to game it by merging, but didn't succeed. You can check out my first attempt [here](https://huggingface.co/ChuckMcSneed/BenchmaxxxerPS-v1-123b). If my questions somehow get leaked and the models are trained on them specifically, then definitely. Update: I made [this RP model](https://huggingface.co/ChuckMcSneed/Gembo-v1-70b) while using this benchmark as a guideline for right/wrong merging. It has a ridiculously high score: 19.75/22! It's not bad, in fact, it is quite interesting in practice, but still far from ChatGPT(or maybe not, I haven't used in a while. Maybe they've lobotomized it to hell).	ChuckMcSneed/NeoEvalPlusN_benchmark	[ "license:wtfpl", "region:us" ]	2023-12-18T07:39:37+00:00	{"license": "wtfpl"}	2024-02-16T02:04:34+00:00	[]	[]	TAGS #license-wtfpl #region-us	Since automatic open source benchmark leaderboard got flooded with incoherent overtrained cheater meme models, I decided to take the matters in my own hands and create my own set of proprietary tests. The aim of these tests is not to see how smart the model is, but to see how good it is at execution of commands and creative writing in a reasonably quantifiable way. All tests are executed with temperature and top P≈0 and rep. penalty=1 in koboldcpp. Model-appropriate format is used, unless it doesn't work. Currently I have the following tests: ## B-test: This test is designed to establish the baseline of the model. It consists of a main task and a bunch of text, which model has to ignore while still executing the task. If the model refuses or fails to comply in a logical way immediately, it fails(0/3). After the initial request question it will get bombarded with text, it gets 1 point for reaching the first checkpoint(1/3). It will get another point for passing the test fully(2/3) and a final point for exiting the test successfully(3/3) ## C-test: Like B-test, but the task is simpler and the distracting text is way more annoying. Since the task is much simpler there are fewer points to gain. Model gets 1 point for passing main distractions and another point for successfully exiting the task. Model gets penalized for writing more than necessary, eg (Note: as an AI language model...). ## D-test: This test is designed around breaking expectations. It consists of a common math trick, but with a twist. The twist is that there is no math involved, just reading. It also has an extensive section at the end to guide the model into breaking the overtrained conditioning. Models will get 1 point for getting the answer right and up to 2 points for the right reasoning. ## P-test: Poems. Model passes each poem test for writing coherently and in rhyme. 1 point for each poem. 6 in total. After seeing Miqu-120b succeed at positive writing and fail miserably at negative, I decided to revise the test a little bit by adjusting the ratios. Assume that all models prior and including Miqu-120b were run on old set, and newer ones will be run on the revised set. ## S-test: Stylized writing. Models are asked to explain a concept in a distinct writing style or as if they are a character. Up to 1 point for each style. Models are penalized for failing to explain the concept or to keep the style all the way through the explaination. 8 in total. Note: not very reliable due to large human factor(±1). Take with a grain of salt. # What does each of the tests measure I dont understand111!!!11! BCD=following commands PS=creative writing # RESULTS !This table shows the results In the table above you can see the results visiualized. You can find pure data in file URL What they show is quite interesting: - If a model can't pass any of the BCD tests, it is most likely braindead or very filtered(kinda same lol) - If SP score of the model is very low it's writing style is dry - Creative parent(Euryale) + creative parent(Xwin)=creative child(Goliath) - Creative parent(Euryale) + dry parent(Nous-Hermes) + drier parent(SynthIA)=dry-ish child(Venus) - Dry parent(Nous-Hermes) + creative parent(Xwin) + creative parent(Mythospice)=creative child(lzlv) - Cheater meme model(una-cybertron) was somewhat creative, but braindead - Base model self-merge(Dicephal-123B) increased creativity, but didn't add extra prompt compliance - All my attempts to extend the context of XWin and Llama by using Yukang's loras have led to drastic decrease in creativity and coherence of the models :( - Miqu is currently the best 32k model according to this benchmark - Miqu-120b is the second model after ChatGPT that has 100% passed S-test! # More tests? Feel free to suggest more models for testing by opening new discussion. Mention model name, size and why do you want to test it. # Limitations - All tests were only done once. - Human factor plays a huge role in SP tests. After redoing some of the tests I noticed ±1 variation for S-test and ±0.5 variation for P-test. (Xwin is likely underrated and Spicyboros is likely overrated in S-test.) - Be critical of my own models! Since I have access to the benchmark, I can game it and rig it all I want and NOBODY can stop me. # Can it be rigged/gamed? Not sure. I've tried to game it by merging, but didn't succeed. You can check out my first attempt here. If my questions somehow get leaked and the models are trained on them specifically, then definitely. Update: I made this RP model while using this benchmark as a guideline for right/wrong merging. It has a ridiculously high score: 19.75/22! It's not bad, in fact, it is quite interesting in practice, but still far from ChatGPT(or maybe not, I haven't used in a while. Maybe they've lobotomized it to hell).	[ "## B-test:\nThis test is designed to establish the baseline of the model. It consists of a main task and a bunch of text, which model has to ignore while still executing the task. If the model refuses or fails to comply in a logical way immediately, it fails(0/3). After the initial request question it will get bombarded with text, it gets 1 point for reaching the first checkpoint(1/3). It will get another point for passing the test fully(2/3) and a final point for exiting the test successfully(3/3)", "## C-test:\nLike B-test, but the task is simpler and the distracting text is way more annoying. Since the task is much simpler there are fewer points to gain. Model gets 1 point for passing main distractions and another point for successfully exiting the task. Model gets penalized for writing more than necessary, eg (Note: as an AI language model...).", "## D-test:\nThis test is designed around breaking expectations. It consists of a common math trick, but with a twist. The twist is that there is no math involved, just reading. It also has an extensive section at the end to guide the model into breaking the overtrained conditioning. Models will get 1 point for getting the answer right and up to 2 points for the right reasoning.", "## P-test:\nPoems. Model passes each poem test for writing coherently and in rhyme. 1 point for each poem. 6 in total.\n\nAfter seeing Miqu-120b succeed at positive writing and fail miserably at negative, I decided to revise the test a little bit by adjusting the ratios. Assume that all models prior and including Miqu-120b were run on old set, and newer ones will be run on the revised set.", "## S-test:\nStylized writing. Models are asked to explain a concept in a distinct writing style or as if they are a character. Up to 1 point for each style. Models are penalized for failing to explain the concept or to keep the style all the way through the explaination. 8 in total. Note: not very reliable due to large human factor(±1). Take with a grain of salt.", "# What does each of the tests measure I dont understand111!!!11!\nBCD=following commands\n\nPS=creative writing", "# RESULTS\n!This table shows the results\n\nIn the table above you can see the results visiualized. You can find pure data in file URL\n\nWhat they show is quite interesting:\n- If a model can't pass any of the BCD tests, it is most likely braindead or very filtered(kinda same lol)\n- If SP score of the model is very low it's writing style is dry\n- Creative parent(Euryale) + creative parent(Xwin)=creative child(Goliath)\n- Creative parent(Euryale) + dry parent(Nous-Hermes) + drier parent(SynthIA)=dry-ish child(Venus)\n- Dry parent(Nous-Hermes) + creative parent(Xwin) + creative parent(Mythospice)=creative child(lzlv)\n- Cheater meme model(una-cybertron) was somewhat creative, but braindead\n- Base model self-merge(Dicephal-123B) increased creativity, but didn't add extra prompt compliance\n- All my attempts to extend the context of XWin and Llama by using Yukang's loras have led to drastic decrease in creativity and coherence of the models :(\n- Miqu is currently the best 32k model according to this benchmark\n- Miqu-120b is the second model after ChatGPT that has 100% passed S-test!", "# More tests?\nFeel free to suggest more models for testing by opening new discussion. Mention model name, size and why do you want to test it.", "# Limitations\n- All tests were only done once. \n- Human factor plays a huge role in SP tests. After redoing some of the tests I noticed ±1 variation for S-test and ±0.5 variation for P-test. (Xwin is likely underrated and Spicyboros is likely overrated in S-test.)\n- Be critical of my own models! Since I have access to the benchmark, I can game it and rig it all I want and NOBODY can stop me.", "# Can it be rigged/gamed?\nNot sure. I've tried to game it by merging, but didn't succeed. You can check out my first attempt here.\n\nIf my questions somehow get leaked and the models are trained on them specifically, then definitely.\n\nUpdate: I made this RP model while using this benchmark as a guideline for right/wrong merging. It has a ridiculously high score: 19.75/22! It's not bad, in fact, it is quite interesting in practice, but still far from ChatGPT(or maybe not, I haven't used in a while. Maybe they've lobotomized it to hell)." ]	[ "TAGS\n#license-wtfpl #region-us \n", "## B-test:\nThis test is designed to establish the baseline of the model. It consists of a main task and a bunch of text, which model has to ignore while still executing the task. If the model refuses or fails to comply in a logical way immediately, it fails(0/3). After the initial request question it will get bombarded with text, it gets 1 point for reaching the first checkpoint(1/3). It will get another point for passing the test fully(2/3) and a final point for exiting the test successfully(3/3)", "## C-test:\nLike B-test, but the task is simpler and the distracting text is way more annoying. Since the task is much simpler there are fewer points to gain. Model gets 1 point for passing main distractions and another point for successfully exiting the task. Model gets penalized for writing more than necessary, eg (Note: as an AI language model...).", "## D-test:\nThis test is designed around breaking expectations. It consists of a common math trick, but with a twist. The twist is that there is no math involved, just reading. It also has an extensive section at the end to guide the model into breaking the overtrained conditioning. Models will get 1 point for getting the answer right and up to 2 points for the right reasoning.", "## P-test:\nPoems. Model passes each poem test for writing coherently and in rhyme. 1 point for each poem. 6 in total.\n\nAfter seeing Miqu-120b succeed at positive writing and fail miserably at negative, I decided to revise the test a little bit by adjusting the ratios. Assume that all models prior and including Miqu-120b were run on old set, and newer ones will be run on the revised set.", "## S-test:\nStylized writing. Models are asked to explain a concept in a distinct writing style or as if they are a character. Up to 1 point for each style. Models are penalized for failing to explain the concept or to keep the style all the way through the explaination. 8 in total. Note: not very reliable due to large human factor(±1). Take with a grain of salt.", "# What does each of the tests measure I dont understand111!!!11!\nBCD=following commands\n\nPS=creative writing", "# RESULTS\n!This table shows the results\n\nIn the table above you can see the results visiualized. You can find pure data in file URL\n\nWhat they show is quite interesting:\n- If a model can't pass any of the BCD tests, it is most likely braindead or very filtered(kinda same lol)\n- If SP score of the model is very low it's writing style is dry\n- Creative parent(Euryale) + creative parent(Xwin)=creative child(Goliath)\n- Creative parent(Euryale) + dry parent(Nous-Hermes) + drier parent(SynthIA)=dry-ish child(Venus)\n- Dry parent(Nous-Hermes) + creative parent(Xwin) + creative parent(Mythospice)=creative child(lzlv)\n- Cheater meme model(una-cybertron) was somewhat creative, but braindead\n- Base model self-merge(Dicephal-123B) increased creativity, but didn't add extra prompt compliance\n- All my attempts to extend the context of XWin and Llama by using Yukang's loras have led to drastic decrease in creativity and coherence of the models :(\n- Miqu is currently the best 32k model according to this benchmark\n- Miqu-120b is the second model after ChatGPT that has 100% passed S-test!", "# More tests?\nFeel free to suggest more models for testing by opening new discussion. Mention model name, size and why do you want to test it.", "# Limitations\n- All tests were only done once. \n- Human factor plays a huge role in SP tests. After redoing some of the tests I noticed ±1 variation for S-test and ±0.5 variation for P-test. (Xwin is likely underrated and Spicyboros is likely overrated in S-test.)\n- Be critical of my own models! Since I have access to the benchmark, I can game it and rig it all I want and NOBODY can stop me.", "# Can it be rigged/gamed?\nNot sure. I've tried to game it by merging, but didn't succeed. You can check out my first attempt here.\n\nIf my questions somehow get leaked and the models are trained on them specifically, then definitely.\n\nUpdate: I made this RP model while using this benchmark as a guideline for right/wrong merging. It has a ridiculously high score: 19.75/22! It's not bad, in fact, it is quite interesting in practice, but still far from ChatGPT(or maybe not, I haven't used in a while. Maybe they've lobotomized it to hell)." ]	[ 14, 123, 83, 86, 102, 88, 27, 305, 32, 108, 144 ]	[ "passage: TAGS\n#license-wtfpl #region-us \n## B-test:\nThis test is designed to establish the baseline of the model. It consists of a main task and a bunch of text, which model has to ignore while still executing the task. If the model refuses or fails to comply in a logical way immediately, it fails(0/3). After the initial request question it will get bombarded with text, it gets 1 point for reaching the first checkpoint(1/3). It will get another point for passing the test fully(2/3) and a final point for exiting the test successfully(3/3)## C-test:\nLike B-test, but the task is simpler and the distracting text is way more annoying. Since the task is much simpler there are fewer points to gain. Model gets 1 point for passing main distractions and another point for successfully exiting the task. Model gets penalized for writing more than necessary, eg (Note: as an AI language model...).## D-test:\nThis test is designed around breaking expectations. It consists of a common math trick, but with a twist. The twist is that there is no math involved, just reading. It also has an extensive section at the end to guide the model into breaking the overtrained conditioning. Models will get 1 point for getting the answer right and up to 2 points for the right reasoning.## P-test:\nPoems. Model passes each poem test for writing coherently and in rhyme. 1 point for each poem. 6 in total.\n\nAfter seeing Miqu-120b succeed at positive writing and fail miserably at negative, I decided to revise the test a little bit by adjusting the ratios. Assume that all models prior and including Miqu-120b were run on old set, and newer ones will be run on the revised set.## S-test:\nStylized writing. Models are asked to explain a concept in a distinct writing style or as if they are a character. Up to 1 point for each style. Models are penalized for failing to explain the concept or to keep the style all the way through the explaination. 8 in total. Note: not very reliable due to large human factor(±1). Take with a grain of salt.", "passage: # What does each of the tests measure I dont understand111!!!11!\nBCD=following commands\n\nPS=creative writing# RESULTS\n!This table shows the results\n\nIn the table above you can see the results visiualized. You can find pure data in file URL\n\nWhat they show is quite interesting:\n- If a model can't pass any of the BCD tests, it is most likely braindead or very filtered(kinda same lol)\n- If SP score of the model is very low it's writing style is dry\n- Creative parent(Euryale) + creative parent(Xwin)=creative child(Goliath)\n- Creative parent(Euryale) + dry parent(Nous-Hermes) + drier parent(SynthIA)=dry-ish child(Venus)\n- Dry parent(Nous-Hermes) + creative parent(Xwin) + creative parent(Mythospice)=creative child(lzlv)\n- Cheater meme model(una-cybertron) was somewhat creative, but braindead\n- Base model self-merge(Dicephal-123B) increased creativity, but didn't add extra prompt compliance\n- All my attempts to extend the context of XWin and Llama by using Yukang's loras have led to drastic decrease in creativity and coherence of the models :(\n- Miqu is currently the best 32k model according to this benchmark\n- Miqu-120b is the second model after ChatGPT that has 100% passed S-test!# More tests?\nFeel free to suggest more models for testing by opening new discussion. Mention model name, size and why do you want to test it.# Limitations\n- All tests were only done once. \n- Human factor plays a huge role in SP tests. After redoing some of the tests I noticed ±1 variation for S-test and ±0.5 variation for P-test. (Xwin is likely underrated and Spicyboros is likely overrated in S-test.)\n- Be critical of my own models! Since I have access to the benchmark, I can game it and rig it all I want and NOBODY can stop me." ]
36b85ac86dccd72443f0732fc1b453743dddceb3	# Dataset Card for "beauty_baby_hpc_grocery" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	nitinbhayana/beauty_baby_hpc_grocery	[ "region:us" ]	2023-12-18T08:15:48+00:00	{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2762574, "num_examples": 20029}, {"name": "test", "num_bytes": 1210499, "num_examples": 8798}], "download_size": 1894299, "dataset_size": 3973073}}	2023-12-18T08:15:57+00:00	[]	[]	TAGS #region-us	# Dataset Card for "beauty_baby_hpc_grocery" More Information needed	[ "# Dataset Card for \"beauty_baby_hpc_grocery\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"beauty_baby_hpc_grocery\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"beauty_baby_hpc_grocery\"\n\nMore Information needed" ]
2a0f22725601e6fc42392864c28539701194cd0f	# SEED-Bench Card ## Benchmark details Benchmark type: SEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations. It spans 27 evaluation dimensions, assessing both text and image generation. Benchmark date: SEED-Bench was collected in November 2023. Paper or resources for more information: https://github.com/AILab-CVC/SEED-Bench License: Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use. Data Sources: - Dimensions 1-9, 23 (In-Context Captioning): Conceptual Captions Dataset (https://ai.google.com/research/ConceptualCaptions/) under its license (https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE). Copyright belongs to the original dataset owner. - Dimension 9 (Text Recognition): ICDAR2003 (http://www.imglab.org/db/index.html), ICDAR2013(https://rrc.cvc.uab.es/?ch=2), IIIT5k(https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset), and SVT(http://vision.ucsd.edu/~kai/svt/). Copyright belongs to the original dataset owner. - Dimension 10 (Celebrity Recognition): MME (https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) and MMBench (https://github.com/open-compass/MMBench) under MMBench license (https://github.com/open-compass/MMBench/blob/main/LICENSE). Copyright belongs to the original dataset owners. - Dimension 11 (Landmark Recognition): Google Landmark Dataset v2 (https://github.com/cvdfoundation/google-landmark) under CC-BY licenses without ND restrictions. - Dimension 12 (Chart Understanding): PlotQA (https://github.com/NiteshMethani/PlotQA) under its license (https://github.com/NiteshMethani/PlotQA/blob/master/LICENSE). - Dimension 13 (Visual Referring Expression): VCR (http://visualcommonsense.com) under its license (http://visualcommonsense.com/license/). - Dimension 14 (Science Knowledge): ScienceQA (https://github.com/lupantech/ScienceQA) under its license (https://github.com/lupantech/ScienceQA/blob/main/LICENSE-DATA). - Dimension 15 (Emotion Recognition): FER2013 (https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/data) under its license (https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/rules#7-competition-data). - Dimension 16 (Visual Mathematics): MME (https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) and data from the internet under CC-BY licenses. - Dimension 17 (Difference Spotting): MIMICIT (https://github.com/Luodian/Otter/blob/main/mimic-it/README.md) under its license (https://github.com/Luodian/Otter/tree/main/mimic-it#eggs). - Dimension 18 (Meme Comprehension): Data from the internet under CC-BY licenses. - Dimension 19 (Global Video Understanding): Charades (https://prior.allenai.org/projects/charades) under its license (https://prior.allenai.org/projects/data/charades/license.txt). SEED-Bench-2 provides 8 frames per video. - Dimensions 20-22 (Action Recognition, Action Prediction, Procedure Understanding): Something-Something v2 (https://developer.qualcomm.com/software/ai-datasets/something-something), Epic-Kitchen 100 (https://epic-kitchens.github.io/2023), and Breakfast (https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/). SEED-Bench-2 provides 8 frames per video. - Dimension 24 (Interleaved Image-Text Analysis): Data from the internet under CC-BY licenses. - Dimension 25 (Text-to-Image Generation): CC-500 (https://github.com/weixi-feng/Structured-Diffusion-Guidance) and ABC-6k (https://github.com/weixi-feng/Structured-Diffusion-Guidance) under their license (https://github.com/weixi-feng/Structured-Diffusion-Guidance/blob/master/LICENSE), with images generated by Stable-Diffusion-XL (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) under its license (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md). - Dimension 26 (Next Image Prediction): Epic-Kitchen 100 (https://epic-kitchens.github.io/2023) under its license (https://creativecommons.org/licenses/by-nc/4.0/). - Dimension 27 (Text-Image Creation): Data from the internet under CC-BY licenses. Please contact us if you believe any data infringes upon your rights, and we will remove it. Where to send questions or comments about the benchmark: https://github.com/AILab-CVC/SEED-Bench/issues ## Intended use Primary intended uses: SEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks. Primary intended users: Researchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark.	AILab-CVC/SEED-Bench-2	[ "task_categories:visual-question-answering", "size_categories:10K<n<100K", "language:en", "license:cc-by-nc-4.0", "region:us" ]	2023-12-18T08:15:53+00:00	{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["visual-question-answering"], "pretty_name": "SEED-Bench-2"}	2023-12-18T08:37:14+00:00	[]	[ "en" ]	TAGS #task_categories-visual-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #region-us	# SEED-Bench Card ## Benchmark details Benchmark type: SEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations. It spans 27 evaluation dimensions, assessing both text and image generation. Benchmark date: SEED-Bench was collected in November 2023. Paper or resources for more information: URL License: Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: URL Data Sources: - Dimensions 1-9, 23 (In-Context Captioning): Conceptual Captions Dataset (URL under its license (URL Copyright belongs to the original dataset owner. - Dimension 9 (Text Recognition): ICDAR2003 (URL ICDAR2013(URL IIIT5k(URL and SVT(URL Copyright belongs to the original dataset owner. - Dimension 10 (Celebrity Recognition): MME (URL and MMBench (URL under MMBench license (URL Copyright belongs to the original dataset owners. - Dimension 11 (Landmark Recognition): Google Landmark Dataset v2 (URL under CC-BY licenses without ND restrictions. - Dimension 12 (Chart Understanding): PlotQA (URL under its license (URL - Dimension 13 (Visual Referring Expression): VCR (URL) under its license (URL - Dimension 14 (Science Knowledge): ScienceQA (URL under its license (URL - Dimension 15 (Emotion Recognition): FER2013 (URL under its license (URL - Dimension 16 (Visual Mathematics): MME (URL and data from the internet under CC-BY licenses. - Dimension 17 (Difference Spotting): MIMICIT (URL under its license (URL - Dimension 18 (Meme Comprehension): Data from the internet under CC-BY licenses. - Dimension 19 (Global Video Understanding): Charades (URL under its license (URL SEED-Bench-2 provides 8 frames per video. - Dimensions 20-22 (Action Recognition, Action Prediction, Procedure Understanding): Something-Something v2 (URL Epic-Kitchen 100 (URL and Breakfast (URL SEED-Bench-2 provides 8 frames per video. - Dimension 24 (Interleaved Image-Text Analysis): Data from the internet under CC-BY licenses. - Dimension 25 (Text-to-Image Generation): CC-500 (URL and ABC-6k (URL under their license (URL with images generated by Stable-Diffusion-XL (URL under its license (URL - Dimension 26 (Next Image Prediction): Epic-Kitchen 100 (URL under its license (URL - Dimension 27 (Text-Image Creation): Data from the internet under CC-BY licenses. Please contact us if you believe any data infringes upon your rights, and we will remove it. Where to send questions or comments about the benchmark: URL ## Intended use Primary intended uses: SEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks. Primary intended users: Researchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark.	[ "# SEED-Bench Card", "## Benchmark details\n\nBenchmark type:\nSEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations. \nIt spans 27 evaluation dimensions, assessing both text and image generation.\n\n\nBenchmark date:\nSEED-Bench was collected in November 2023.\n\nPaper or resources for more information:\nURL\n\nLicense:\nAttribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: URL\n\n\nData Sources:\n- Dimensions 1-9, 23 (In-Context Captioning): Conceptual Captions Dataset (URL under its license (URL Copyright belongs to the original dataset owner.\n- Dimension 9 (Text Recognition): ICDAR2003 (URL ICDAR2013(URL IIIT5k(URL and SVT(URL Copyright belongs to the original dataset owner.\n- Dimension 10 (Celebrity Recognition): MME (URL and MMBench (URL under MMBench license (URL Copyright belongs to the original dataset owners.\n- Dimension 11 (Landmark Recognition): Google Landmark Dataset v2 (URL under CC-BY licenses without ND restrictions.\n- Dimension 12 (Chart Understanding): PlotQA (URL under its license (URL\n- Dimension 13 (Visual Referring Expression): VCR (URL) under its license (URL\n- Dimension 14 (Science Knowledge): ScienceQA (URL under its license (URL\n- Dimension 15 (Emotion Recognition): FER2013 (URL under its license (URL\n- Dimension 16 (Visual Mathematics): MME (URL and data from the internet under CC-BY licenses.\n- Dimension 17 (Difference Spotting): MIMICIT (URL under its license (URL\n- Dimension 18 (Meme Comprehension): Data from the internet under CC-BY licenses.\n- Dimension 19 (Global Video Understanding): Charades (URL under its license (URL SEED-Bench-2 provides 8 frames per video.\n- Dimensions 20-22 (Action Recognition, Action Prediction, Procedure Understanding): Something-Something v2 (URL Epic-Kitchen 100 (URL and Breakfast (URL SEED-Bench-2 provides 8 frames per video.\n- Dimension 24 (Interleaved Image-Text Analysis): Data from the internet under CC-BY licenses.\n- Dimension 25 (Text-to-Image Generation): CC-500 (URL and ABC-6k (URL under their license (URL with images generated by Stable-Diffusion-XL (URL under its license (URL\n- Dimension 26 (Next Image Prediction): Epic-Kitchen 100 (URL under its license (URL\n- Dimension 27 (Text-Image Creation): Data from the internet under CC-BY licenses.\n\nPlease contact us if you believe any data infringes upon your rights, and we will remove it.\n\nWhere to send questions or comments about the benchmark:\nURL", "## Intended use\nPrimary intended uses:\nSEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks.\n\nPrimary intended users:\nResearchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark." ]	[ "TAGS\n#task_categories-visual-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #region-us \n", "# SEED-Bench Card", "## Benchmark details\n\nBenchmark type:\nSEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations. \nIt spans 27 evaluation dimensions, assessing both text and image generation.\n\n\nBenchmark date:\nSEED-Bench was collected in November 2023.\n\nPaper or resources for more information:\nURL\n\nLicense:\nAttribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: URL\n\n\nData Sources:\n- Dimensions 1-9, 23 (In-Context Captioning): Conceptual Captions Dataset (URL under its license (URL Copyright belongs to the original dataset owner.\n- Dimension 9 (Text Recognition): ICDAR2003 (URL ICDAR2013(URL IIIT5k(URL and SVT(URL Copyright belongs to the original dataset owner.\n- Dimension 10 (Celebrity Recognition): MME (URL and MMBench (URL under MMBench license (URL Copyright belongs to the original dataset owners.\n- Dimension 11 (Landmark Recognition): Google Landmark Dataset v2 (URL under CC-BY licenses without ND restrictions.\n- Dimension 12 (Chart Understanding): PlotQA (URL under its license (URL\n- Dimension 13 (Visual Referring Expression): VCR (URL) under its license (URL\n- Dimension 14 (Science Knowledge): ScienceQA (URL under its license (URL\n- Dimension 15 (Emotion Recognition): FER2013 (URL under its license (URL\n- Dimension 16 (Visual Mathematics): MME (URL and data from the internet under CC-BY licenses.\n- Dimension 17 (Difference Spotting): MIMICIT (URL under its license (URL\n- Dimension 18 (Meme Comprehension): Data from the internet under CC-BY licenses.\n- Dimension 19 (Global Video Understanding): Charades (URL under its license (URL SEED-Bench-2 provides 8 frames per video.\n- Dimensions 20-22 (Action Recognition, Action Prediction, Procedure Understanding): Something-Something v2 (URL Epic-Kitchen 100 (URL and Breakfast (URL SEED-Bench-2 provides 8 frames per video.\n- Dimension 24 (Interleaved Image-Text Analysis): Data from the internet under CC-BY licenses.\n- Dimension 25 (Text-to-Image Generation): CC-500 (URL and ABC-6k (URL under their license (URL with images generated by Stable-Diffusion-XL (URL under its license (URL\n- Dimension 26 (Next Image Prediction): Epic-Kitchen 100 (URL under its license (URL\n- Dimension 27 (Text-Image Creation): Data from the internet under CC-BY licenses.\n\nPlease contact us if you believe any data infringes upon your rights, and we will remove it.\n\nWhere to send questions or comments about the benchmark:\nURL", "## Intended use\nPrimary intended uses:\nSEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks.\n\nPrimary intended users:\nResearchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark." ]	[ 48, 7, 645, 76 ]	[ "passage: TAGS\n#task_categories-visual-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #region-us \n# SEED-Bench Card" ]
3a773ea59c9d3e797dff742f6c337f7702243f51	# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [luffycodes/vicuna-mmlu-val-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-mcq-7b-ep2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:19:40.489086](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2/blob/main/results_2023-12-18T08-19-40.489086.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": 0.4687909606959751, "acc_stderr": 0.034482691498229606, "acc_norm": 0.474821731384865, "acc_norm_stderr": 0.03528101342729721, "mc1": 0.2913096695226438, "mc1_stderr": 0.01590598704818483, "mc2": 0.4386596963219029, "mc2_stderr": 0.014931837062941003 }, "harness\|arc:challenge\|25": { "acc": 0.4931740614334471, "acc_stderr": 0.014610029151379813, "acc_norm": 0.5332764505119454, "acc_norm_stderr": 0.01457899585960581 }, "harness\|hellaswag\|10": { "acc": 0.5841465843457478, "acc_stderr": 0.004918612098944032, "acc_norm": 0.7773351921927902, "acc_norm_stderr": 0.00415184825793471 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45185185185185184, "acc_stderr": 0.04299268905480864, "acc_norm": 0.45185185185185184, "acc_norm_stderr": 0.04299268905480864 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4605263157894737, "acc_stderr": 0.04056242252249034, "acc_norm": 0.4605263157894737, "acc_norm_stderr": 0.04056242252249034 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4830188679245283, "acc_stderr": 0.030755120364119898, "acc_norm": 0.4830188679245283, "acc_norm_stderr": 0.030755120364119898 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5138888888888888, "acc_stderr": 0.04179596617581, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.04179596617581 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.41040462427745666, "acc_stderr": 0.03750757044895537, "acc_norm": 0.41040462427745666, "acc_norm_stderr": 0.03750757044895537 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.1568627450980392, "acc_stderr": 0.03618664819936246, "acc_norm": 0.1568627450980392, "acc_norm_stderr": 0.03618664819936246 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.425531914893617, "acc_stderr": 0.03232146916224468, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.03232146916224468 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278008, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278008 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.32275132275132273, "acc_stderr": 0.024078943243597016, "acc_norm": 0.32275132275132273, "acc_norm_stderr": 0.024078943243597016 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04216370213557835, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04216370213557835 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5193548387096775, "acc_stderr": 0.028422687404312107, "acc_norm": 0.5193548387096775, "acc_norm_stderr": 0.028422687404312107 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3694581280788177, "acc_stderr": 0.03395970381998574, "acc_norm": 0.3694581280788177, "acc_norm_stderr": 0.03395970381998574 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6303030303030303, "acc_stderr": 0.03769430314512567, "acc_norm": 0.6303030303030303, "acc_norm_stderr": 0.03769430314512567 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6060606060606061, "acc_stderr": 0.034812853382329624, "acc_norm": 0.6060606060606061, "acc_norm_stderr": 0.034812853382329624 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6424870466321243, "acc_stderr": 0.034588160421810114, "acc_norm": 0.6424870466321243, "acc_norm_stderr": 0.034588160421810114 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.40512820512820513, "acc_stderr": 0.024890471769938145, "acc_norm": 0.40512820512820513, "acc_norm_stderr": 0.024890471769938145 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.0263357394040558 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.3907563025210084, "acc_stderr": 0.031693802357129965, "acc_norm": 0.3907563025210084, "acc_norm_stderr": 0.031693802357129965 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.03802039760107903, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.03802039760107903 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6018348623853211, "acc_stderr": 0.020987989422654268, "acc_norm": 0.6018348623853211, "acc_norm_stderr": 0.020987989422654268 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.30092592592592593, "acc_stderr": 0.03128039084329881, "acc_norm": 0.30092592592592593, "acc_norm_stderr": 0.03128039084329881 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.03454236585380609, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.03454236585380609 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6413502109704642, "acc_stderr": 0.031219569445301847, "acc_norm": 0.6413502109704642, "acc_norm_stderr": 0.031219569445301847 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5605381165919282, "acc_stderr": 0.03331092511038179, "acc_norm": 0.5605381165919282, "acc_norm_stderr": 0.03331092511038179 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5419847328244275, "acc_stderr": 0.04369802690578756, "acc_norm": 0.5419847328244275, "acc_norm_stderr": 0.04369802690578756 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5537190082644629, "acc_stderr": 0.0453793517794788, "acc_norm": 0.5537190082644629, "acc_norm_stderr": 0.0453793517794788 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.0471282125742677, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.0471282125742677 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.4662576687116564, "acc_stderr": 0.03919415545048411, "acc_norm": 0.4662576687116564, "acc_norm_stderr": 0.03919415545048411 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5242718446601942, "acc_stderr": 0.049449010929737795, "acc_norm": 0.5242718446601942, "acc_norm_stderr": 0.049449010929737795 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7264957264957265, "acc_stderr": 0.029202540153431173, "acc_norm": 0.7264957264957265, "acc_norm_stderr": 0.029202540153431173 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6360153256704981, "acc_stderr": 0.01720568480903223, "acc_norm": 0.6360153256704981, "acc_norm_stderr": 0.01720568480903223 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5317919075144508, "acc_stderr": 0.02686462436675665, "acc_norm": 0.5317919075144508, "acc_norm_stderr": 0.02686462436675665 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2536312849162011, "acc_stderr": 0.014551553659369922, "acc_norm": 0.2536312849162011, "acc_norm_stderr": 0.014551553659369922 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5163398692810458, "acc_stderr": 0.02861462475280544, "acc_norm": 0.5163398692810458, "acc_norm_stderr": 0.02861462475280544 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5755627009646302, "acc_stderr": 0.028071928247946205, "acc_norm": 0.5755627009646302, "acc_norm_stderr": 0.028071928247946205 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.49691358024691357, "acc_stderr": 0.02782021415859437, "acc_norm": 0.49691358024691357, "acc_norm_stderr": 0.02782021415859437 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35815602836879434, "acc_stderr": 0.028602085862759415, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.028602085862759415 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3428943937418514, "acc_stderr": 0.0121234632715859, "acc_norm": 0.3428943937418514, "acc_norm_stderr": 0.0121234632715859 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4522058823529412, "acc_stderr": 0.03023375855159645, "acc_norm": 0.4522058823529412, "acc_norm_stderr": 0.03023375855159645 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.44281045751633985, "acc_stderr": 0.02009508315457735, "acc_norm": 0.44281045751633985, "acc_norm_stderr": 0.02009508315457735 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5363636363636364, "acc_stderr": 0.04776449162396197, "acc_norm": 0.5363636363636364, "acc_norm_stderr": 0.04776449162396197 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5551020408163265, "acc_stderr": 0.031814251181977865, "acc_norm": 0.5551020408163265, "acc_norm_stderr": 0.031814251181977865 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5970149253731343, "acc_stderr": 0.034683432951111266, "acc_norm": 0.5970149253731343, "acc_norm_stderr": 0.034683432951111266 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.43373493975903615, "acc_stderr": 0.03858158940685516, "acc_norm": 0.43373493975903615, "acc_norm_stderr": 0.03858158940685516 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.03565079670708311, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.03565079670708311 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2913096695226438, "mc1_stderr": 0.01590598704818483, "mc2": 0.4386596963219029, "mc2_stderr": 0.014931837062941003 }, "harness\|winogrande\|5": { "acc": 0.712707182320442, "acc_stderr": 0.01271748105247803 }, "harness\|gsm8k\|5": { "acc": 0.1023502653525398, "acc_stderr": 0.008349110996208834 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2	[ "region:us" ]	2023-12-18T08:22:35+00:00	{"pretty_name": "Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2", "dataset_summary": "Dataset automatically created during the evaluation run of model [luffycodes/vicuna-mmlu-val-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-mcq-7b-ep2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:19:40.489086](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-mcq-7b-ep2/blob/main/results_2023-12-18T08-19-40.489086.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\": 0.4687909606959751,\n \"acc_stderr\": 0.034482691498229606,\n \"acc_norm\": 0.474821731384865,\n \"acc_norm_stderr\": 0.03528101342729721,\n \"mc1\": 0.2913096695226438,\n \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.4386596963219029,\n \"mc2_stderr\": 0.014931837062941003\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.4931740614334471,\n \"acc_stderr\": 0.014610029151379813,\n \"acc_norm\": 0.5332764505119454,\n \"acc_norm_stderr\": 0.01457899585960581\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5841465843457478,\n \"acc_stderr\": 0.004918612098944032,\n \"acc_norm\": 0.7773351921927902,\n \"acc_norm_stderr\": 0.00415184825793471\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.4605263157894737,\n \"acc_stderr\": 0.04056242252249034,\n \"acc_norm\": 0.4605263157894737,\n \"acc_norm_stderr\": 0.04056242252249034\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.4830188679245283,\n \"acc_stderr\": 0.030755120364119898,\n \"acc_norm\": 0.4830188679245283,\n \"acc_norm_stderr\": 0.030755120364119898\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5138888888888888,\n \"acc_stderr\": 0.04179596617581,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.04179596617581\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.41040462427745666,\n \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.41040462427745666,\n \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.1568627450980392,\n \"acc_stderr\": 0.03618664819936246,\n \"acc_norm\": 0.1568627450980392,\n \"acc_norm_stderr\": 0.03618664819936246\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.03232146916224468,\n \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.03232146916224468\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.04372748290278008,\n \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.04372748290278008\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.32275132275132273,\n \"acc_stderr\": 0.024078943243597016,\n \"acc_norm\": 0.32275132275132273,\n \"acc_norm_stderr\": 0.024078943243597016\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5193548387096775,\n \"acc_stderr\": 0.028422687404312107,\n \"acc_norm\": 0.5193548387096775,\n \"acc_norm_stderr\": 0.028422687404312107\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.3694581280788177,\n \"acc_stderr\": 0.03395970381998574,\n \"acc_norm\": 0.3694581280788177,\n \"acc_norm_stderr\": 0.03395970381998574\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6303030303030303,\n \"acc_stderr\": 0.03769430314512567,\n \"acc_norm\": 0.6303030303030303,\n \"acc_norm_stderr\": 0.03769430314512567\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.034812853382329624,\n \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.034812853382329624\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.6424870466321243,\n \"acc_stderr\": 0.034588160421810114,\n \"acc_norm\": 0.6424870466321243,\n \"acc_norm_stderr\": 0.034588160421810114\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.40512820512820513,\n \"acc_stderr\": 0.024890471769938145,\n \"acc_norm\": 0.40512820512820513,\n \"acc_norm_stderr\": 0.024890471769938145\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-19-40.489086.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_19_40.489086", "path": ["results_2023-12-18T08-19-40.489086.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-19-40.489086.parquet"]}]}]}	2023-12-18T08:23:21+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2 Dataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-mcq-7b-ep2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:19:40.489086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2\n\n\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-mcq-7b-ep2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:19:40.489086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2\n\n\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-mcq-7b-ep2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:19:40.489086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 203, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-mcq-7b-ep2\n\n\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-mcq-7b-ep2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:19:40.489086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
8b87e0f709f2719d41ce98b07caba4adcab998b2	# Dataset Card for Evaluation run of openchat/openchat-3.5-1210 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_openchat__openchat-3.5-1210", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:20:01.389363](https://huggingface.co/datasets/open-llm-leaderboard/details_openchat__openchat-3.5-1210/blob/main/results_2023-12-18T08-20-01.389363.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": 0.6488027943708179, "acc_stderr": 0.03210806718394323, "acc_norm": 0.6497098585915643, "acc_norm_stderr": 0.03276295616957028, "mc1": 0.35128518971848227, "mc1_stderr": 0.016711358163544403, "mc2": 0.5214604840999885, "mc2_stderr": 0.015414031217543209 }, "harness\|arc:challenge\|25": { "acc": 0.6109215017064846, "acc_stderr": 0.014247309976045607, "acc_norm": 0.6493174061433447, "acc_norm_stderr": 0.013944635930726097 }, "harness\|hellaswag\|10": { "acc": 0.664708225453097, "acc_stderr": 0.004711275408138422, "acc_norm": 0.8492332204740092, "acc_norm_stderr": 0.0035709011883580674 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.67, "acc_stderr": 0.047258156262526094, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526094 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.0358687928008034, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400351, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.041227371113703316, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.041227371113703316 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.025107425481137282, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.025107425481137282 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.02985751567338641, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.02985751567338641 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.021995311963644234, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.021995311963644234 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253262, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253262 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6974789915966386, "acc_stderr": 0.02983796238829194, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.02983796238829194 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590167, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590167 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8431372549019608, "acc_stderr": 0.025524722324553353, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.025524722324553353 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601446, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601446 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229143, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229143 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.037601780060266196, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.037601780060266196 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281382, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281382 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8135376756066411, "acc_stderr": 0.013927751372001503, "acc_norm": 0.8135376756066411, "acc_norm_stderr": 0.013927751372001503 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7312138728323699, "acc_stderr": 0.02386800326250011, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.02386800326250011 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3553072625698324, "acc_stderr": 0.01600698993480318, "acc_norm": 0.3553072625698324, "acc_norm_stderr": 0.01600698993480318 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7191358024691358, "acc_stderr": 0.02500646975579921, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.02500646975579921 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4771838331160365, "acc_stderr": 0.0127569333828237, "acc_norm": 0.4771838331160365, "acc_norm_stderr": 0.0127569333828237 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7169117647058824, "acc_stderr": 0.027365861131513812, "acc_norm": 0.7169117647058824, "acc_norm_stderr": 0.027365861131513812 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675606, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675606 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061452, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061452 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35128518971848227, "mc1_stderr": 0.016711358163544403, "mc2": 0.5214604840999885, "mc2_stderr": 0.015414031217543209 }, "harness\|winogrande\|5": { "acc": 0.8074191002367798, "acc_stderr": 0.011082538847491897 }, "harness\|gsm8k\|5": { "acc": 0.6595905989385898, "acc_stderr": 0.013052097103299104 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_openchat__openchat-3.5-1210	[ "region:us" ]	2023-12-18T08:22:50+00:00	{"pretty_name": "Evaluation run of openchat/openchat-3.5-1210", "dataset_summary": "Dataset automatically created during the evaluation run of model [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openchat__openchat-3.5-1210\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:20:01.389363](https://huggingface.co/datasets/open-llm-leaderboard/details_openchat__openchat-3.5-1210/blob/main/results_2023-12-18T08-20-01.389363.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\": 0.6488027943708179,\n \"acc_stderr\": 0.03210806718394323,\n \"acc_norm\": 0.6497098585915643,\n \"acc_norm_stderr\": 0.03276295616957028,\n \"mc1\": 0.35128518971848227,\n \"mc1_stderr\": 0.016711358163544403,\n \"mc2\": 0.5214604840999885,\n \"mc2_stderr\": 0.015414031217543209\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6109215017064846,\n \"acc_stderr\": 0.014247309976045607,\n \"acc_norm\": 0.6493174061433447,\n \"acc_norm_stderr\": 0.013944635930726097\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.664708225453097,\n \"acc_stderr\": 0.004711275408138422,\n \"acc_norm\": 0.8492332204740092,\n \"acc_norm_stderr\": 0.0035709011883580674\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137282,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137282\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338641,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338641\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644234,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644234\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253262,\n \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253262\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829194,\n \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829194\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590167,\n \"acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590167\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553353,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553353\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.037601780060266196,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.037601780060266196\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.021586494001281382,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.021586494001281382\n },\n 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["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-20-01.389363.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-20-01.389363.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_20_01.389363", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-20-01.389363.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-20-01.389363.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_20_01.389363", "path": ["results_2023-12-18T08-20-01.389363.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-20-01.389363.parquet"]}]}]}	2023-12-18T08:23:33+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of openchat/openchat-3.5-1210 Dataset automatically created during the evaluation run of model openchat/openchat-3.5-1210 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:20:01.389363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of openchat/openchat-3.5-1210\n\n\n\nDataset automatically created during the evaluation run of model openchat/openchat-3.5-1210 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:20:01.389363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of openchat/openchat-3.5-1210\n\n\n\nDataset automatically created during the evaluation run of model openchat/openchat-3.5-1210 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:20:01.389363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 179, 66, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of openchat/openchat-3.5-1210\n\n\n\nDataset automatically created during the evaluation run of model openchat/openchat-3.5-1210 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:20:01.389363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
952e6585df04ba198feac3aecada6a5e89502d5b	# Dataset Card for Evaluation run of NExtNewChattingAI/shark_tank_ai_7_b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [NExtNewChattingAI/shark_tank_ai_7_b](https://huggingface.co/NExtNewChattingAI/shark_tank_ai_7_b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_NExtNewChattingAI__shark_tank_ai_7_b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:22:45.276136](https://huggingface.co/datasets/open-llm-leaderboard/details_NExtNewChattingAI__shark_tank_ai_7_b/blob/main/results_2023-12-18T08-22-45.276136.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": 0.65556501715169, "acc_stderr": 0.031845412531981275, "acc_norm": 0.6565753714833046, "acc_norm_stderr": 0.0324927894891608, "mc1": 0.4259485924112607, "mc1_stderr": 0.01731047190407654, "mc2": 0.6019434535176705, "mc2_stderr": 0.015061482204205485 }, "harness\|arc:challenge\|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042194, "acc_norm": 0.6689419795221843, "acc_norm_stderr": 0.013752062419817836 }, "harness\|hellaswag\|10": { "acc": 0.678550089623581, "acc_stderr": 0.004660785616933751, "acc_norm": 0.8660625373431587, "acc_norm_stderr": 0.0033988905252297008 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247078, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247078 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726367, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726367 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6042553191489362, "acc_stderr": 0.031967586978353627, "acc_norm": 0.6042553191489362, "acc_norm_stderr": 0.031967586978353627 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6206896551724138, "acc_stderr": 0.040434618619167466, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.040434618619167466 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4417989417989418, "acc_stderr": 0.025576257061253833, "acc_norm": 0.4417989417989418, "acc_norm_stderr": 0.025576257061253833 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083525, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586825, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586825 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289733, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289733 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6923076923076923, "acc_stderr": 0.02340092891831049, "acc_norm": 0.6923076923076923, "acc_norm_stderr": 0.02340092891831049 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251972, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251972 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7184873949579832, "acc_stderr": 0.029213549414372177, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.029213549414372177 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8385321100917431, "acc_stderr": 0.015776239256163248, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.015776239256163248 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5694444444444444, "acc_stderr": 0.03376922151252335, "acc_norm": 0.5694444444444444, "acc_norm_stderr": 0.03376922151252335 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8186274509803921, "acc_stderr": 0.027044621719474082, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.027044621719474082 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621115, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621115 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229146, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229146 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8320610687022901, "acc_stderr": 0.032785485373431386, "acc_norm": 0.8320610687022901, "acc_norm_stderr": 0.032785485373431386 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098825, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098825 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615624, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615624 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8390804597701149, "acc_stderr": 0.013140225515611724, "acc_norm": 0.8390804597701149, "acc_norm_stderr": 0.013140225515611724 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500107, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500107 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.44581005586592176, "acc_stderr": 0.01662399851333311, "acc_norm": 0.44581005586592176, "acc_norm_stderr": 0.01662399851333311 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7516339869281046, "acc_stderr": 0.02473998135511359, "acc_norm": 0.7516339869281046, "acc_norm_stderr": 0.02473998135511359 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.45827900912646674, "acc_stderr": 0.01272570165695364, "acc_norm": 0.45827900912646674, "acc_norm_stderr": 0.01272570165695364 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.027778298701545443, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.027778298701545443 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.01863559403442397, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.01863559403442397 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8656716417910447, "acc_stderr": 0.02411267824090083, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.02411267824090083 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4259485924112607, "mc1_stderr": 0.01731047190407654, "mc2": 0.6019434535176705, "mc2_stderr": 0.015061482204205485 }, "harness\|winogrande\|5": { "acc": 0.819258089976322, "acc_stderr": 0.010814911009613983 }, "harness\|gsm8k\|5": { "acc": 0.6573161485974223, "acc_stderr": 0.01307303023082791 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_NExtNewChattingAI__shark_tank_ai_7_b	[ "region:us" ]	2023-12-18T08:25:36+00:00	{"pretty_name": "Evaluation run of NExtNewChattingAI/shark_tank_ai_7_b", "dataset_summary": "Dataset automatically created during the evaluation run of model [NExtNewChattingAI/shark_tank_ai_7_b](https://huggingface.co/NExtNewChattingAI/shark_tank_ai_7_b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NExtNewChattingAI__shark_tank_ai_7_b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:22:45.276136](https://huggingface.co/datasets/open-llm-leaderboard/details_NExtNewChattingAI__shark_tank_ai_7_b/blob/main/results_2023-12-18T08-22-45.276136.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\": 0.65556501715169,\n \"acc_stderr\": 0.031845412531981275,\n \"acc_norm\": 0.6565753714833046,\n \"acc_norm_stderr\": 0.0324927894891608,\n \"mc1\": 0.4259485924112607,\n \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.6019434535176705,\n \"mc2_stderr\": 0.015061482204205485\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042194,\n \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.013752062419817836\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.678550089623581,\n \"acc_stderr\": 0.004660785616933751,\n \"acc_norm\": 0.8660625373431587,\n \"acc_norm_stderr\": 0.0033988905252297008\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247078,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247078\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726367,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726367\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.6042553191489362,\n \"acc_stderr\": 0.031967586978353627,\n \"acc_norm\": 0.6042553191489362,\n \"acc_norm_stderr\": 0.031967586978353627\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.040434618619167466,\n \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.040434618619167466\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083525,\n \"acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083525\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586825,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586825\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6923076923076923,\n \"acc_stderr\": 0.02340092891831049,\n \"acc_norm\": 0.6923076923076923,\n \"acc_norm_stderr\": 0.02340092891831049\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.029213549414372177,\n \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.029213549414372177\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163248,\n \"acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163248\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5694444444444444,\n \"acc_stderr\": 0.03376922151252335,\n \"acc_norm\": 0.5694444444444444,\n \"acc_norm_stderr\": 0.03376922151252335\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.030769352008229146,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.030769352008229146\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8320610687022901,\n \"acc_stderr\": 0.032785485373431386,\n \"acc_norm\": 0.8320610687022901,\n \"acc_norm_stderr\": 0.032785485373431386\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098825,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098825\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8390804597701149,\n \"acc_stderr\": 0.013140225515611724,\n \"acc_norm\": 0.8390804597701149,\n \"acc_norm_stderr\": 0.013140225515611724\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500107,\n \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500107\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.44581005586592176,\n \"acc_stderr\": 0.01662399851333311,\n \"acc_norm\": 0.44581005586592176,\n \"acc_norm_stderr\": 0.01662399851333311\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.45827900912646674,\n \"acc_stderr\": 0.01272570165695364,\n \"acc_norm\": 0.45827900912646674,\n \"acc_norm_stderr\": 0.01272570165695364\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545443,\n \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545443\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.01863559403442397,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.01863559403442397\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8656716417910447,\n \"acc_stderr\": 0.02411267824090083,\n \"acc_norm\": 0.8656716417910447,\n \"acc_norm_stderr\": 0.02411267824090083\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4259485924112607,\n \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.6019434535176705,\n \"mc2_stderr\": 0.015061482204205485\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.819258089976322,\n \"acc_stderr\": 0.010814911009613983\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6573161485974223,\n \"acc_stderr\": 0.01307303023082791\n }\n}\n```", "repo_url": "https://huggingface.co/NExtNewChattingAI/shark_tank_ai_7_b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", 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The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:22:45.276136(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of NExtNewChattingAI/shark_tank_ai_7_b\n\n\n\nDataset automatically created during the evaluation run of model NExtNewChattingAI/shark_tank_ai_7_b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:22:45.276136(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NExtNewChattingAI/shark_tank_ai_7_b\n\n\n\nDataset automatically created during the evaluation run of model NExtNewChattingAI/shark_tank_ai_7_b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:22:45.276136(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 197, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NExtNewChattingAI/shark_tank_ai_7_b\n\n\n\nDataset automatically created during the evaluation run of model NExtNewChattingAI/shark_tank_ai_7_b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:22:45.276136(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
a6746a9f67e4a363bfc3d4e00e28e89a504f3fc3	# Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mncai/mistral-7b-dpo-merge-v1.1](https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:24:07.649575](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1/blob/main/results_2023-12-18T08-24-07.649575.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": 0.653910016295201, "acc_stderr": 0.03202751255847507, "acc_norm": 0.6535699044300431, "acc_norm_stderr": 0.03269009606304505, "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6847598329649042, "mc2_stderr": 0.015228501222479747 }, "harness\|arc:challenge\|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002503, "acc_norm": 0.7252559726962458, "acc_norm_stderr": 0.013044617212771227 }, "harness\|hellaswag\|10": { "acc": 0.7080262895837482, "acc_stderr": 0.004537410615572944, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897138 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.037385206761196686, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.037385206761196686 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7245283018867924, "acc_stderr": 0.027495663683724053, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.027495663683724053 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.035676037996391706, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.035676037996391706 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566545, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566545 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8244274809160306, "acc_stderr": 0.03336820338476074, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.03336820338476074 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.41964285714285715, "acc_stderr": 0.04684099321077106, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.04684099321077106 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4770949720670391, "acc_stderr": 0.016704945740326188, "acc_norm": 0.4770949720670391, "acc_norm_stderr": 0.016704945740326188 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4716312056737589, "acc_stderr": 0.02977945095730307, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.02977945095730307 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4706649282920469, "acc_stderr": 0.012748238397365549, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.012748238397365549 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6699346405228758, "acc_stderr": 0.019023726160724553, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.019023726160724553 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6847598329649042, "mc2_stderr": 0.015228501222479747 }, "harness\|winogrande\|5": { "acc": 0.8232044198895028, "acc_stderr": 0.010721923287918753 }, "harness\|gsm8k\|5": { "acc": 0.7088703563305534, "acc_stderr": 0.012513215297888463 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1	[ "region:us" ]	2023-12-18T08:26:58+00:00	{"pretty_name": "Evaluation run of mncai/mistral-7b-dpo-merge-v1.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [mncai/mistral-7b-dpo-merge-v1.1](https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:24:07.649575](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1/blob/main/results_2023-12-18T08-24-07.649575.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\": 0.653910016295201,\n \"acc_stderr\": 0.03202751255847507,\n \"acc_norm\": 0.6535699044300431,\n \"acc_norm_stderr\": 0.03269009606304505,\n \"mc1\": 0.5520195838433293,\n \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6847598329649042,\n \"mc2_stderr\": 0.015228501222479747\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6945392491467577,\n \"acc_stderr\": 0.013460080478002503,\n \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7080262895837482,\n \"acc_stderr\": 0.004537410615572944,\n \"acc_norm\": 0.8814977096195977,\n \"acc_norm_stderr\": 0.0032254141192897138\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724053,\n \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724053\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566545,\n \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566545\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.41964285714285715,\n \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4770949720670391,\n \"acc_stderr\": 0.016704945740326188,\n \"acc_norm\": 0.4770949720670391,\n \"acc_norm_stderr\": 0.016704945740326188\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4716312056737589,\n \"acc_stderr\": 0.02977945095730307,\n \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.02977945095730307\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4706649282920469,\n \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.02826388994378459,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.02826388994378459\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5520195838433293,\n \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6847598329649042,\n \"mc2_stderr\": 0.015228501222479747\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8232044198895028,\n \"acc_stderr\": 0.010721923287918753\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7088703563305534,\n \"acc_stderr\": 0.012513215297888463\n }\n}\n```", "repo_url": "https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|gsm8k\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hellaswag\|10_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T08-24-07.649575.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-18T08-24-07.649575.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-24-07.649575.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_24_07.649575", "path": ["results_2023-12-18T08-24-07.649575.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-24-07.649575.parquet"]}]}]}	2023-12-18T08:27:44+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1 Dataset automatically created during the evaluation run of model mncai/mistral-7b-dpo-merge-v1.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:24:07.649575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1\n\n\n\nDataset automatically created during the evaluation run of model mncai/mistral-7b-dpo-merge-v1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:24:07.649575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1\n\n\n\nDataset automatically created during the evaluation run of model mncai/mistral-7b-dpo-merge-v1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:24:07.649575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 195, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1\n\n\n\nDataset automatically created during the evaluation run of model mncai/mistral-7b-dpo-merge-v1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:24:07.649575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
06bad19e580c4977a3b1daf51eef33f2b424e7be	# Dataset Card for Evaluation run of quantumaikr/quantum-dpo-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [quantumaikr/quantum-dpo-v0.1](https://huggingface.co/quantumaikr/quantum-dpo-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_quantumaikr__quantum-dpo-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:25:35.133410](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__quantum-dpo-v0.1/blob/main/results_2023-12-18T08-25-35.133410.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": 0.6583441513997987, "acc_stderr": 0.032026908469625553, "acc_norm": 0.6580775139828253, "acc_norm_stderr": 0.0326896820470086, "mc1": 0.5581395348837209, "mc1_stderr": 0.01738476747898621, "mc2": 0.6991731296636601, "mc2_stderr": 0.014996767395410802 }, "harness\|arc:challenge\|25": { "acc": 0.6962457337883959, "acc_stderr": 0.01343890918477876, "acc_norm": 0.7252559726962458, "acc_norm_stderr": 0.013044617212771227 }, "harness\|hellaswag\|10": { "acc": 0.713802031467835, "acc_stderr": 0.004510593395289895, "acc_norm": 0.8836885082652858, "acc_norm_stderr": 0.003199428675985866 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720386, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720386 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.037385206761196686, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.037385206761196686 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083525, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.02874204090394848, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.02874204090394848 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660836, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660836 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5462962962962963, "acc_stderr": 0.03395322726375797, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.03395322726375797 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078962, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078962 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290916, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290916 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.031024411740572213, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.031024411740572213 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8365261813537676, "acc_stderr": 0.013223928616741622, "acc_norm": 0.8365261813537676, "acc_norm_stderr": 0.013223928616741622 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.48044692737430167, "acc_stderr": 0.016709709877661995, "acc_norm": 0.48044692737430167, "acc_norm_stderr": 0.016709709877661995 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826528, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826528 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694912, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694912 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093092, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093092 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5581395348837209, "mc1_stderr": 0.01738476747898621, "mc2": 0.6991731296636601, "mc2_stderr": 0.014996767395410802 }, "harness\|winogrande\|5": { "acc": 0.8232044198895028, "acc_stderr": 0.010721923287918753 }, "harness\|gsm8k\|5": { "acc": 0.7081122062168309, "acc_stderr": 0.012522795894420867 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_quantumaikr__quantum-dpo-v0.1	[ "region:us" ]	2023-12-18T08:28:27+00:00	{"pretty_name": "Evaluation run of quantumaikr/quantum-dpo-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [quantumaikr/quantum-dpo-v0.1](https://huggingface.co/quantumaikr/quantum-dpo-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_quantumaikr__quantum-dpo-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:25:35.133410](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__quantum-dpo-v0.1/blob/main/results_2023-12-18T08-25-35.133410.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\": 0.6583441513997987,\n \"acc_stderr\": 0.032026908469625553,\n \"acc_norm\": 0.6580775139828253,\n \"acc_norm_stderr\": 0.0326896820470086,\n \"mc1\": 0.5581395348837209,\n \"mc1_stderr\": 0.01738476747898621,\n \"mc2\": 0.6991731296636601,\n \"mc2_stderr\": 0.014996767395410802\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6962457337883959,\n \"acc_stderr\": 0.01343890918477876,\n \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.713802031467835,\n \"acc_stderr\": 0.004510593395289895,\n \"acc_norm\": 0.8836885082652858,\n \"acc_norm_stderr\": 0.003199428675985866\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-25-35.133410.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_25_35.133410", "path": ["results_2023-12-18T08-25-35.133410.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-25-35.133410.parquet"]}]}]}	2023-12-18T08:29:12+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of quantumaikr/quantum-dpo-v0.1 Dataset automatically created during the evaluation run of model quantumaikr/quantum-dpo-v0.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:25:35.133410(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of quantumaikr/quantum-dpo-v0.1\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-dpo-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:25:35.133410(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of quantumaikr/quantum-dpo-v0.1\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-dpo-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:25:35.133410(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 185, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of quantumaikr/quantum-dpo-v0.1\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-dpo-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:25:35.133410(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
76d364a10e8e3c2d1a9d47cdb5f86eb5ccffb581	# Dataset Card for Evaluation run of cookinai/BruinHermes <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cookinai/BruinHermes](https://huggingface.co/cookinai/BruinHermes) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cookinai__BruinHermes", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:28:15.533319](https://huggingface.co/datasets/open-llm-leaderboard/details_cookinai__BruinHermes/blob/main/results_2023-12-18T08-28-15.533319.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": 0.6567218786558917, "acc_stderr": 0.03193493899655974, "acc_norm": 0.65676353979433, "acc_norm_stderr": 0.032592352059711976, "mc1": 0.5018359853121175, "mc1_stderr": 0.017503383046877048, "mc2": 0.6559538306507776, "mc2_stderr": 0.015048223056540525 }, "harness\|arc:challenge\|25": { "acc": 0.6689419795221843, "acc_stderr": 0.013752062419817836, "acc_norm": 0.7013651877133106, "acc_norm_stderr": 0.01337407861506874 }, "harness\|hellaswag\|10": { "acc": 0.6893049193387771, "acc_stderr": 0.004618323959513038, "acc_norm": 0.8707428799044015, "acc_norm_stderr": 0.0033479866695653154 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952928, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952928 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933714, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933714 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5957446808510638, "acc_stderr": 0.03208115750788684, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.03208115750788684 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42592592592592593, "acc_stderr": 0.02546714904546955, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.02546714904546955 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723292, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723292 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267045, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267045 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328972, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328972 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.023854795680971128, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.023854795680971128 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.02882088466625326, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.02882088466625326 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8550458715596331, "acc_stderr": 0.015094215699700481, "acc_norm": 0.8550458715596331, "acc_norm_stderr": 0.015094215699700481 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078966, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078966 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290913, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290913 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980981, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980981 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7914110429447853, "acc_stderr": 0.03192193448934724, "acc_norm": 0.7914110429447853, "acc_norm_stderr": 0.03192193448934724 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8326947637292464, "acc_stderr": 0.013347327202920332, "acc_norm": 0.8326947637292464, "acc_norm_stderr": 0.013347327202920332 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.42793296089385474, "acc_stderr": 0.01654788799741611, "acc_norm": 0.42793296089385474, "acc_norm_stderr": 0.01654788799741611 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242553, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242553 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904211, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904211 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4661016949152542, "acc_stderr": 0.012740853872949829, "acc_norm": 0.4661016949152542, "acc_norm_stderr": 0.012740853872949829 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6813725490196079, "acc_stderr": 0.01885008469646872, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.01885008469646872 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644286, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644286 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8557213930348259, "acc_stderr": 0.02484575321230604, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.02484575321230604 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5018359853121175, "mc1_stderr": 0.017503383046877048, "mc2": 0.6559538306507776, "mc2_stderr": 0.015048223056540525 }, "harness\|winogrande\|5": { "acc": 0.8129439621152328, "acc_stderr": 0.010959716435242912 }, "harness\|gsm8k\|5": { "acc": 0.7119029567854435, "acc_stderr": 0.012474469737197917 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_cookinai__BruinHermes	[ "region:us" ]	2023-12-18T08:31:05+00:00	{"pretty_name": "Evaluation run of cookinai/BruinHermes", "dataset_summary": "Dataset automatically created during the evaluation run of model [cookinai/BruinHermes](https://huggingface.co/cookinai/BruinHermes) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cookinai__BruinHermes\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:28:15.533319](https://huggingface.co/datasets/open-llm-leaderboard/details_cookinai__BruinHermes/blob/main/results_2023-12-18T08-28-15.533319.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\": 0.6567218786558917,\n \"acc_stderr\": 0.03193493899655974,\n \"acc_norm\": 0.65676353979433,\n \"acc_norm_stderr\": 0.032592352059711976,\n \"mc1\": 0.5018359853121175,\n \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6559538306507776,\n \"mc2_stderr\": 0.015048223056540525\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6689419795221843,\n \"acc_stderr\": 0.013752062419817836,\n \"acc_norm\": 0.7013651877133106,\n \"acc_norm_stderr\": 0.01337407861506874\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6893049193387771,\n \"acc_stderr\": 0.004618323959513038,\n \"acc_norm\": 0.8707428799044015,\n \"acc_norm_stderr\": 0.0033479866695653154\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700481,\n \"acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700481\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 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"latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-28-15.533319.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_28_15.533319", "path": ["results_2023-12-18T08-28-15.533319.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-28-15.533319.parquet"]}]}]}	2023-12-18T08:31:51+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of cookinai/BruinHermes Dataset automatically created during the evaluation run of model cookinai/BruinHermes on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:28:15.533319(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of cookinai/BruinHermes\n\n\n\nDataset automatically created during the evaluation run of model cookinai/BruinHermes on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:28:15.533319(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of cookinai/BruinHermes\n\n\n\nDataset automatically created during the evaluation run of model cookinai/BruinHermes on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:28:15.533319(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 175, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of cookinai/BruinHermes\n\n\n\nDataset automatically created during the evaluation run of model cookinai/BruinHermes on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:28:15.533319(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
ffe6e335f533955310ba5dfb66b869ff68a994d4	# Dataset Card for Evaluation run of rishiraj/CatPPT <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [rishiraj/CatPPT](https://huggingface.co/rishiraj/CatPPT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_rishiraj__CatPPT", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:30:09.872009](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__CatPPT/blob/main/results_2023-12-18T08-30-09.872009.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": 0.6555637262571667, "acc_stderr": 0.0320682215857209, "acc_norm": 0.6557700093546615, "acc_norm_stderr": 0.03273113968108238, "mc1": 0.43329253365973075, "mc1_stderr": 0.017347024450107495, "mc2": 0.6154509110626448, "mc2_stderr": 0.01503233577240663 }, "harness\|arc:challenge\|25": { "acc": 0.6493174061433447, "acc_stderr": 0.013944635930726097, "acc_norm": 0.6808873720136519, "acc_norm_stderr": 0.013621696119173311 }, "harness\|hellaswag\|10": { "acc": 0.6731726747659829, "acc_stderr": 0.004680949283855315, "acc_norm": 0.8668591913961362, "acc_norm_stderr": 0.00339032545802026 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287533, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287533 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305526, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305526 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4729064039408867, "acc_stderr": 0.03512819077876106, "acc_norm": 0.4729064039408867, "acc_norm_stderr": 0.03512819077876106 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8232323232323232, "acc_stderr": 0.027178752639044915, "acc_norm": 0.8232323232323232, "acc_norm_stderr": 0.027178752639044915 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289726, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289726 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.029443169323031537, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.029443169323031537 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590167, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590167 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639318, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639318 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8143459915611815, "acc_stderr": 0.025310495376944863, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.025310495376944863 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.02158649400128136, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.02158649400128136 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993457, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993457 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069363, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069363 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.43910614525139663, "acc_stderr": 0.016598022120580428, "acc_norm": 0.43910614525139663, "acc_norm_stderr": 0.016598022120580428 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818737, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818737 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.02447722285613511, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.02447722285613511 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46153846153846156, "acc_stderr": 0.012732398286190444, "acc_norm": 0.46153846153846156, "acc_norm_stderr": 0.012732398286190444 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.02841820861940676, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.02841820861940676 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0190709855896875, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0190709855896875 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.0287951855742913, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.0287951855742913 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.43329253365973075, "mc1_stderr": 0.017347024450107495, "mc2": 0.6154509110626448, "mc2_stderr": 0.01503233577240663 }, "harness\|winogrande\|5": { "acc": 0.8161010260457774, "acc_stderr": 0.01088791601330589 }, "harness\|gsm8k\|5": { "acc": 0.7081122062168309, "acc_stderr": 0.012522795894420869 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_rishiraj__CatPPT	[ "region:us" ]	2023-12-18T08:33:01+00:00	{"pretty_name": "Evaluation run of rishiraj/CatPPT", "dataset_summary": "Dataset automatically created during the evaluation run of model [rishiraj/CatPPT](https://huggingface.co/rishiraj/CatPPT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_rishiraj__CatPPT\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:30:09.872009](https://huggingface.co/datasets/open-llm-leaderboard/details_rishiraj__CatPPT/blob/main/results_2023-12-18T08-30-09.872009.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\": 0.6555637262571667,\n \"acc_stderr\": 0.0320682215857209,\n \"acc_norm\": 0.6557700093546615,\n \"acc_norm_stderr\": 0.03273113968108238,\n \"mc1\": 0.43329253365973075,\n \"mc1_stderr\": 0.017347024450107495,\n \"mc2\": 0.6154509110626448,\n \"mc2_stderr\": 0.01503233577240663\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6493174061433447,\n \"acc_stderr\": 0.013944635930726097,\n \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173311\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6731726747659829,\n \"acc_stderr\": 0.004680949283855315,\n \"acc_norm\": 0.8668591913961362,\n \"acc_norm_stderr\": 0.00339032545802026\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287533,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287533\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305526,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305526\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289726,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289726\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.37037037037037035,\n \"acc_stderr\": 0.029443169323031537,\n \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.029443169323031537\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590167,\n \"acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590167\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.02158649400128136,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.02158649400128136\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8237547892720306,\n \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.43910614525139663,\n \"acc_stderr\": 0.016598022120580428,\n \"acc_norm\": 0.43910614525139663,\n \"acc_norm_stderr\": 0.016598022120580428\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n \"acc_stderr\": 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["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": 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["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-30-09.872009.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_30_09.872009", "path": ["results_2023-12-18T08-30-09.872009.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-30-09.872009.parquet"]}]}]}	2023-12-18T08:33:44+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of rishiraj/CatPPT Dataset automatically created during the evaluation run of model rishiraj/CatPPT on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:30:09.872009(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of rishiraj/CatPPT\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/CatPPT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:30:09.872009(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of rishiraj/CatPPT\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/CatPPT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:30:09.872009(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 173, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of rishiraj/CatPPT\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/CatPPT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:30:09.872009(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
1d0f19e2e42dc1ad272e4aa13110b9ed01237155	# Dataset Card for Evaluation run of perlthoughts/Marcoroni-8x7B-v3-MoE <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [perlthoughts/Marcoroni-8x7B-v3-MoE](https://huggingface.co/perlthoughts/Marcoroni-8x7B-v3-MoE) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_perlthoughts__Marcoroni-8x7B-v3-MoE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:51:31.365964](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Marcoroni-8x7B-v3-MoE/blob/main/results_2023-12-18T08-51-31.365964.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": 0.6545821748587465, "acc_stderr": 0.032036628493726085, "acc_norm": 0.654496818028716, "acc_norm_stderr": 0.03269985600714658, "mc1": 0.4467564259485924, "mc1_stderr": 0.017403977522557144, "mc2": 0.6039866216126479, "mc2_stderr": 0.015317466968177975 }, "harness\|arc:challenge\|25": { "acc": 0.6629692832764505, "acc_stderr": 0.013813476652902272, "acc_norm": 0.6936860068259386, "acc_norm_stderr": 0.013470584417276513 }, "harness\|hellaswag\|10": { "acc": 0.6809400517825135, "acc_stderr": 0.004651597209993092, "acc_norm": 0.867755427205736, "acc_norm_stderr": 0.0033806414709899196 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6592592592592592, "acc_stderr": 0.04094376269996792, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.04094376269996792 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7245283018867924, "acc_stderr": 0.027495663683724057, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.027495663683724057 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.43915343915343913, "acc_stderr": 0.025559920550531003, "acc_norm": 0.43915343915343913, "acc_norm_stderr": 0.025559920550531003 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083004, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083004 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.03006676158297793, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.03006676158297793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660834, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660834 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8284313725490197, "acc_stderr": 0.02646056956124064, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.02646056956124064 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8143459915611815, "acc_stderr": 0.025310495376944863, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.025310495376944863 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.023445826276545543, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.023445826276545543 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4100558659217877, "acc_stderr": 0.01644970820902608, "acc_norm": 0.4100558659217877, "acc_norm_stderr": 0.01644970820902608 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7654320987654321, "acc_stderr": 0.023576881744005723, "acc_norm": 0.7654320987654321, "acc_norm_stderr": 0.023576881744005723 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4654498044328553, "acc_stderr": 0.012739711554045704, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045704 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6830065359477124, "acc_stderr": 0.018824219512706207, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.018824219512706207 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4467564259485924, "mc1_stderr": 0.017403977522557144, "mc2": 0.6039866216126479, "mc2_stderr": 0.015317466968177975 }, "harness\|winogrande\|5": { "acc": 0.8145224940805051, "acc_stderr": 0.010923965303140505 }, "harness\|gsm8k\|5": { "acc": 0.7172100075815011, "acc_stderr": 0.012405020417873619 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_perlthoughts__Marcoroni-8x7B-v3-MoE	[ "region:us" ]	2023-12-18T08:54:27+00:00	{"pretty_name": "Evaluation run of perlthoughts/Marcoroni-8x7B-v3-MoE", "dataset_summary": "Dataset automatically created during the evaluation run of model [perlthoughts/Marcoroni-8x7B-v3-MoE](https://huggingface.co/perlthoughts/Marcoroni-8x7B-v3-MoE) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_perlthoughts__Marcoroni-8x7B-v3-MoE\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:51:31.365964](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Marcoroni-8x7B-v3-MoE/blob/main/results_2023-12-18T08-51-31.365964.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\": 0.6545821748587465,\n \"acc_stderr\": 0.032036628493726085,\n \"acc_norm\": 0.654496818028716,\n \"acc_norm_stderr\": 0.03269985600714658,\n \"mc1\": 0.4467564259485924,\n \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6039866216126479,\n \"mc2_stderr\": 0.015317466968177975\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6629692832764505,\n \"acc_stderr\": 0.013813476652902272,\n \"acc_norm\": 0.6936860068259386,\n \"acc_norm_stderr\": 0.013470584417276513\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6809400517825135,\n \"acc_stderr\": 0.004651597209993092,\n \"acc_norm\": 0.867755427205736,\n \"acc_norm_stderr\": 0.0033806414709899196\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6592592592592592,\n \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724057,\n \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724057\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531003,\n \"acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531003\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7709677419354839,\n \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083004,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083004\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.02646056956124064,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.02646056956124064\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8314176245210728,\n \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4100558659217877,\n \"acc_stderr\": 0.01644970820902608,\n \"acc_norm\": 0.4100558659217877,\n \"acc_norm_stderr\": 0.01644970820902608\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7106109324758842,\n \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7654320987654321,\n \"acc_stderr\": 0.023576881744005723,\n \"acc_norm\": 0.7654320987654321,\n \"acc_norm_stderr\": 0.023576881744005723\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4654498044328553,\n \"acc_stderr\": 0.012739711554045704,\n \"acc_norm\": 0.4654498044328553,\n \"acc_norm_stderr\": 0.012739711554045704\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4467564259485924,\n \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6039866216126479,\n \"mc2_stderr\": 0.015317466968177975\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8145224940805051,\n \"acc_stderr\": 0.010923965303140505\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7172100075815011,\n \"acc_stderr\": 0.012405020417873619\n }\n}\n```", "repo_url": "https://huggingface.co/perlthoughts/Marcoroni-8x7B-v3-MoE", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_18T08_51_31.365964", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T08-51-31.365964.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T08-51-31.365964.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_18T08_51_31.365964", "path": ["/details_harness\|gsm8k\|5_2023-12-18T08-51-31.365964.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-18T08-51-31.365964.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_18T08_51_31.365964", "path": ["/details_harness\|hellaswag\|10_2023-12-18T08-51-31.365964.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-18T08-51-31.365964.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", 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["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-51-31.365964.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-51-31.365964.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_51_31.365964", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-51-31.365964.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-51-31.365964.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_51_31.365964", "path": ["results_2023-12-18T08-51-31.365964.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-51-31.365964.parquet"]}]}]}	2023-12-18T08:55:11+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of perlthoughts/Marcoroni-8x7B-v3-MoE Dataset automatically created during the evaluation run of model perlthoughts/Marcoroni-8x7B-v3-MoE on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:51:31.365964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of perlthoughts/Marcoroni-8x7B-v3-MoE\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/Marcoroni-8x7B-v3-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:51:31.365964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of perlthoughts/Marcoroni-8x7B-v3-MoE\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/Marcoroni-8x7B-v3-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:51:31.365964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 195, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of perlthoughts/Marcoroni-8x7B-v3-MoE\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/Marcoroni-8x7B-v3-MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:51:31.365964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
5e7545fd0cb869ef6666cb651164801e7cd2b613	# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:54:03.410174](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2/blob/main/results_2023-12-18T08-54-03.410174.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": 0.4771667109240717, "acc_stderr": 0.034233110377777674, "acc_norm": 0.485115350864936, "acc_norm_stderr": 0.035153131866548476, "mc1": 0.2827417380660955, "mc1_stderr": 0.015764770836777305, "mc2": 0.4317214117939303, "mc2_stderr": 0.01510139555175088 }, "harness\|arc:challenge\|25": { "acc": 0.492320819112628, "acc_stderr": 0.01460966744089257, "acc_norm": 0.5298634812286689, "acc_norm_stderr": 0.014585305840007102 }, "harness\|hellaswag\|10": { "acc": 0.5798645688109938, "acc_stderr": 0.004925717008099711, "acc_norm": 0.7767377016530571, "acc_norm_stderr": 0.004155816900505157 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750574, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750574 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5131578947368421, "acc_stderr": 0.04067533136309174, "acc_norm": 0.5131578947368421, "acc_norm_stderr": 0.04067533136309174 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4641509433962264, "acc_stderr": 0.030693675018458006, "acc_norm": 0.4641509433962264, "acc_norm_stderr": 0.030693675018458006 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4930555555555556, "acc_stderr": 0.041808067502949374, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.041808067502949374 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.45664739884393063, "acc_stderr": 0.03798106566014498, "acc_norm": 0.45664739884393063, "acc_norm_stderr": 0.03798106566014498 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.1568627450980392, "acc_stderr": 0.036186648199362466, "acc_norm": 0.1568627450980392, "acc_norm_stderr": 0.036186648199362466 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.40425531914893614, "acc_stderr": 0.032081157507886836, "acc_norm": 0.40425531914893614, "acc_norm_stderr": 0.032081157507886836 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3508771929824561, "acc_stderr": 0.04489539350270699, "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.04489539350270699 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.30423280423280424, "acc_stderr": 0.023695415009463087, "acc_norm": 0.30423280423280424, "acc_norm_stderr": 0.023695415009463087 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147125, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147125 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.532258064516129, "acc_stderr": 0.02838474778881333, "acc_norm": 0.532258064516129, "acc_norm_stderr": 0.02838474778881333 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4039408866995074, "acc_stderr": 0.0345245390382204, "acc_norm": 0.4039408866995074, "acc_norm_stderr": 0.0345245390382204 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6242424242424243, "acc_stderr": 0.03781887353205982, "acc_norm": 0.6242424242424243, "acc_norm_stderr": 0.03781887353205982 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.601010101010101, "acc_stderr": 0.03488901616852731, "acc_norm": 0.601010101010101, "acc_norm_stderr": 0.03488901616852731 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6683937823834197, "acc_stderr": 0.03397636541089118, "acc_norm": 0.6683937823834197, "acc_norm_stderr": 0.03397636541089118 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.41025641025641024, "acc_stderr": 0.024939313906940788, "acc_norm": 0.41025641025641024, "acc_norm_stderr": 0.024939313906940788 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815635, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815635 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.39915966386554624, "acc_stderr": 0.03181110032413925, "acc_norm": 0.39915966386554624, "acc_norm_stderr": 0.03181110032413925 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.634862385321101, "acc_stderr": 0.020642801454384005, "acc_norm": 0.634862385321101, "acc_norm_stderr": 0.020642801454384005 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.39351851851851855, "acc_stderr": 0.03331747876370312, "acc_norm": 0.39351851851851855, "acc_norm_stderr": 0.03331747876370312 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6862745098039216, "acc_stderr": 0.032566854844603886, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.032566854844603886 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6708860759493671, "acc_stderr": 0.030587326294702365, "acc_norm": 0.6708860759493671, "acc_norm_stderr": 0.030587326294702365 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5246636771300448, "acc_stderr": 0.03351695167652628, "acc_norm": 0.5246636771300448, "acc_norm_stderr": 0.03351695167652628 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5648854961832062, "acc_stderr": 0.04348208051644858, "acc_norm": 0.5648854961832062, "acc_norm_stderr": 0.04348208051644858 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6115702479338843, "acc_stderr": 0.04449270350068382, "acc_norm": 0.6115702479338843, "acc_norm_stderr": 0.04449270350068382 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.04826217294139894, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.04826217294139894 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.49079754601226994, "acc_stderr": 0.03927705600787443, "acc_norm": 0.49079754601226994, "acc_norm_stderr": 0.03927705600787443 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.33035714285714285, "acc_stderr": 0.04464285714285712, "acc_norm": 0.33035714285714285, "acc_norm_stderr": 0.04464285714285712 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6601941747572816, "acc_stderr": 0.04689765937278135, "acc_norm": 0.6601941747572816, "acc_norm_stderr": 0.04689765937278135 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6623931623931624, "acc_stderr": 0.030980296992618558, "acc_norm": 0.6623931623931624, "acc_norm_stderr": 0.030980296992618558 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.01685739124747255, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.01685739124747255 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5028901734104047, "acc_stderr": 0.026918645383239004, "acc_norm": 0.5028901734104047, "acc_norm_stderr": 0.026918645383239004 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2536312849162011, "acc_stderr": 0.014551553659369923, "acc_norm": 0.2536312849162011, "acc_norm_stderr": 0.014551553659369923 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5261437908496732, "acc_stderr": 0.028590752958852394, "acc_norm": 0.5261437908496732, "acc_norm_stderr": 0.028590752958852394 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5369774919614148, "acc_stderr": 0.028320325830105915, "acc_norm": 0.5369774919614148, "acc_norm_stderr": 0.028320325830105915 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5061728395061729, "acc_stderr": 0.02781862396258329, "acc_norm": 0.5061728395061729, "acc_norm_stderr": 0.02781862396258329 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3475177304964539, "acc_stderr": 0.028406627809590954, "acc_norm": 0.3475177304964539, "acc_norm_stderr": 0.028406627809590954 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.33702737940026073, "acc_stderr": 0.012072836273691323, "acc_norm": 0.33702737940026073, "acc_norm_stderr": 0.012072836273691323 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.49264705882352944, "acc_stderr": 0.030369552523902173, "acc_norm": 0.49264705882352944, "acc_norm_stderr": 0.030369552523902173 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4526143790849673, "acc_stderr": 0.02013679091849254, "acc_norm": 0.4526143790849673, "acc_norm_stderr": 0.02013679091849254 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5727272727272728, "acc_stderr": 0.047381987035454834, "acc_norm": 0.5727272727272728, "acc_norm_stderr": 0.047381987035454834 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6244897959183674, "acc_stderr": 0.03100120903989484, "acc_norm": 0.6244897959183674, "acc_norm_stderr": 0.03100120903989484 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6616915422885572, "acc_stderr": 0.03345563070339193, "acc_norm": 0.6616915422885572, "acc_norm_stderr": 0.03345563070339193 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.41566265060240964, "acc_stderr": 0.03836722176598052, "acc_norm": 0.41566265060240964, "acc_norm_stderr": 0.03836722176598052 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.03565079670708312, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.03565079670708312 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2827417380660955, "mc1_stderr": 0.015764770836777305, "mc2": 0.4317214117939303, "mc2_stderr": 0.01510139555175088 }, "harness\|winogrande\|5": { "acc": 0.7190213101815311, "acc_stderr": 0.012632541095875824 }, "harness\|gsm8k\|5": { "acc": 0.000758150113722517, "acc_stderr": 0.0007581501137225296 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2	[ "region:us" ]	2023-12-18T08:57:00+00:00	{"pretty_name": "Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2", "dataset_summary": "Dataset automatically created during the evaluation run of model [luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T08:54:03.410174](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2/blob/main/results_2023-12-18T08-54-03.410174.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\": 0.4771667109240717,\n \"acc_stderr\": 0.034233110377777674,\n \"acc_norm\": 0.485115350864936,\n \"acc_norm_stderr\": 0.035153131866548476,\n \"mc1\": 0.2827417380660955,\n \"mc1_stderr\": 0.015764770836777305,\n \"mc2\": 0.4317214117939303,\n \"mc2_stderr\": 0.01510139555175088\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.492320819112628,\n \"acc_stderr\": 0.01460966744089257,\n \"acc_norm\": 0.5298634812286689,\n \"acc_norm_stderr\": 0.014585305840007102\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5798645688109938,\n \"acc_stderr\": 0.004925717008099711,\n \"acc_norm\": 0.7767377016530571,\n \"acc_norm_stderr\": 0.004155816900505157\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4740740740740741,\n \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309174,\n \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309174\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.4641509433962264,\n \"acc_stderr\": 0.030693675018458006,\n \"acc_norm\": 0.4641509433962264,\n \"acc_norm_stderr\": 0.030693675018458006\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4930555555555556,\n \"acc_stderr\": 0.041808067502949374,\n \"acc_norm\": 0.4930555555555556,\n \"acc_norm_stderr\": 0.041808067502949374\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.45664739884393063,\n \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.45664739884393063,\n \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.1568627450980392,\n \"acc_stderr\": 0.036186648199362466,\n \"acc_norm\": 0.1568627450980392,\n \"acc_norm_stderr\": 0.036186648199362466\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T08-54-03.410174.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T08_54_03.410174", "path": ["results_2023-12-18T08-54-03.410174.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T08-54-03.410174.parquet"]}]}]}	2023-12-18T08:57:41+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2 Dataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T08:54:03.410174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2\n\n\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:54:03.410174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2\n\n\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T08:54:03.410174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 215, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2\n\n\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T08:54:03.410174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:" ]
02366bc66bd43aa69e707747487ec8f3c22cac84	# Geo Reviews Dataset 2023 Yandex is making available the largest Russian-language dataset of reviews about organizations published on Yandex Maps. Use it for academic and research purposes, share your results with us in Issues. ## Dataset Description - Repository: https://github.com/yandex/geo-reviews-dataset-2023 - Paper: [Яндекс Карты открывают крупнейший русскоязычный датасет отзывов на организации](https://habr.com/ru/companies/yandex/articles/763832/) - Point of Contact: [[email protected]](mailto:[email protected]) ## Description - 500,000 unique reviews - Only reviews about organizations in Russia - Available on Yandex Maps - Published from January to July 2023 - The dataset does not contain short one-word reviews - Reviews have been cleared of personal data (phone numbers, email addresses) ## Dataset Fields The dataset contains the following attributes: - Organization address (`address`) - Organization name (`name_ru`) - List of categories to which the organization belongs (`rubrics`) - User rating from 0 to 5 (`rating`) - Review text (`text`) ## License Distributed under MIT license. ## Contacts For any inquiries or questions regarding the dataset, please contact us at [[email protected]](mailto:[email protected]).	d0rj/geo-reviews-dataset-2023	[ "task_categories:text-classification", "task_categories:sentence-similarity", "task_categories:token-classification", "task_categories:text2text-generation", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ru", "license:mit", "reviews", "yandex", "region:us" ]	2023-12-18T09:11:11+00:00	{"language": "ru", "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification", "sentence-similarity", "token-classification", "text2text-generation"], "pretty_name": "Geo Reviews Dataset 2023", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "address", "dtype": "string"}, {"name": "name_ru", "dtype": "string"}, {"name": "rating", "dtype": "int64"}, {"name": "rubrics", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 369301294, "num_examples": 500000}], "download_size": 170880716, "dataset_size": 369301294}, "tags": ["reviews", "yandex"]}	2023-12-18T09:31:28+00:00	[]	[ "ru" ]	TAGS #task_categories-text-classification #task_categories-sentence-similarity #task_categories-token-classification #task_categories-text2text-generation #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Russian #license-mit #reviews #yandex #region-us	# Geo Reviews Dataset 2023 Yandex is making available the largest Russian-language dataset of reviews about organizations published on Yandex Maps. Use it for academic and research purposes, share your results with us in Issues. ## Dataset Description - Repository: URL - Paper: Яндекс Карты открывают крупнейший русскоязычный датасет отзывов на организации - Point of Contact: opensource@URL ## Description - 500,000 unique reviews - Only reviews about organizations in Russia - Available on Yandex Maps - Published from January to July 2023 - The dataset does not contain short one-word reviews - Reviews have been cleared of personal data (phone numbers, email addresses) ## Dataset Fields The dataset contains the following attributes: - Organization address ('address') - Organization name ('name_ru') - List of categories to which the organization belongs ('rubrics') - User rating from 0 to 5 ('rating') - Review text ('text') ## License Distributed under MIT license. ## Contacts For any inquiries or questions regarding the dataset, please contact us at opensource@URL.	[ "# Geo Reviews Dataset 2023\n\nYandex is making available the largest Russian-language dataset of reviews about organizations published on Yandex Maps.\n\nUse it for academic and research purposes, share your results with us in Issues.", "## Dataset Description\n\n- Repository: URL\n- Paper: Яндекс Карты открывают крупнейший русскоязычный датасет отзывов на организации\n- Point of Contact: opensource@URL", "## Description\n\n- 500,000 unique reviews\n- Only reviews about organizations in Russia\n- Available on Yandex Maps\n- Published from January to July 2023\n- The dataset does not contain short one-word reviews\n- Reviews have been cleared of personal data (phone numbers, email addresses)", "## Dataset Fields\n\nThe dataset contains the following attributes:\n\n- Organization address ('address')\n- Organization name ('name_ru')\n- List of categories to which the organization belongs ('rubrics')\n- User rating from 0 to 5 ('rating')\n- Review text ('text')", "## License\n\nDistributed under MIT license.", "## Contacts\n\nFor any inquiries or questions regarding the dataset, please contact us at opensource@URL." ]	[ "TAGS\n#task_categories-text-classification #task_categories-sentence-similarity #task_categories-token-classification #task_categories-text2text-generation #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Russian #license-mit #reviews #yandex #region-us \n", "# Geo Reviews Dataset 2023\n\nYandex is making available the largest Russian-language dataset of reviews about organizations published on Yandex Maps.\n\nUse it for academic and research purposes, share your results with us in Issues.", "## Dataset Description\n\n- Repository: URL\n- Paper: Яндекс Карты открывают крупнейший русскоязычный датасет отзывов на организации\n- Point of Contact: opensource@URL", "## Description\n\n- 500,000 unique reviews\n- Only reviews about organizations in Russia\n- Available on Yandex Maps\n- Published from January to July 2023\n- The dataset does not contain short one-word reviews\n- Reviews have been cleared of personal data (phone numbers, email addresses)", "## Dataset Fields\n\nThe dataset contains the following attributes:\n\n- Organization address ('address')\n- Organization name ('name_ru')\n- List of categories to which the organization belongs ('rubrics')\n- User rating from 0 to 5 ('rating')\n- Review text ('text')", "## License\n\nDistributed under MIT license.", "## Contacts\n\nFor any inquiries or questions regarding the dataset, please contact us at opensource@URL." ]	[ 99, 48, 40, 58, 73, 8, 24 ]	[ "passage: TAGS\n#task_categories-text-classification #task_categories-sentence-similarity #task_categories-token-classification #task_categories-text2text-generation #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Russian #license-mit #reviews #yandex #region-us \n# Geo Reviews Dataset 2023\n\nYandex is making available the largest Russian-language dataset of reviews about organizations published on Yandex Maps.\n\nUse it for academic and research purposes, share your results with us in Issues.## Dataset Description\n\n- Repository: URL\n- Paper: Яндекс Карты открывают крупнейший русскоязычный датасет отзывов на организации\n- Point of Contact: opensource@URL## Description\n\n- 500,000 unique reviews\n- Only reviews about organizations in Russia\n- Available on Yandex Maps\n- Published from January to July 2023\n- The dataset does not contain short one-word reviews\n- Reviews have been cleared of personal data (phone numbers, email addresses)## Dataset Fields\n\nThe dataset contains the following attributes:\n\n- Organization address ('address')\n- Organization name ('name_ru')\n- List of categories to which the organization belongs ('rubrics')\n- User rating from 0 to 5 ('rating')\n- Review text ('text')## License\n\nDistributed under MIT license.## Contacts\n\nFor any inquiries or questions regarding the dataset, please contact us at opensource@URL." ]
8689425db42bca49d7f1e81c078cae4ff85dcb3d	# Dataset Card for Evaluation run of martyn/llama2-megamerge-dare-13b-v2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [martyn/llama2-megamerge-dare-13b-v2](https://huggingface.co/martyn/llama2-megamerge-dare-13b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_martyn__llama2-megamerge-dare-13b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T09:10:01.856812](https://huggingface.co/datasets/open-llm-leaderboard/details_martyn__llama2-megamerge-dare-13b-v2/blob/main/results_2023-12-18T09-10-01.856812.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": 0.5527610511045514, "acc_stderr": 0.03393471599188749, "acc_norm": 0.5576353360299099, "acc_norm_stderr": 0.03464924134012296, "mc1": 0.32802937576499386, "mc1_stderr": 0.016435632932815025, "mc2": 0.47271389220607046, "mc2_stderr": 0.01511731653639185 }, "harness\|arc:challenge\|25": { "acc": 0.5563139931740614, "acc_stderr": 0.014518421825670444, "acc_norm": 0.5938566552901023, "acc_norm_stderr": 0.014351656690097858 }, "harness\|hellaswag\|10": { "acc": 0.6168094005178252, "acc_stderr": 0.004851705504790439, "acc_norm": 0.8093009360685123, "acc_norm_stderr": 0.003920494224883792 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5259259259259259, "acc_stderr": 0.04313531696750575, "acc_norm": 0.5259259259259259, "acc_norm_stderr": 0.04313531696750575 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.618421052631579, "acc_stderr": 0.03953173377749194, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.03953173377749194 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.569811320754717, "acc_stderr": 0.030471445867183238, "acc_norm": 0.569811320754717, "acc_norm_stderr": 0.030471445867183238 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5763888888888888, "acc_stderr": 0.041321250197233685, "acc_norm": 0.5763888888888888, "acc_norm_stderr": 0.041321250197233685 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4508670520231214, "acc_stderr": 0.037940126746970296, "acc_norm": 0.4508670520231214, "acc_norm_stderr": 0.037940126746970296 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.27450980392156865, "acc_stderr": 0.04440521906179328, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.04440521906179328 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.451063829787234, "acc_stderr": 0.032529096196131965, "acc_norm": 0.451063829787234, "acc_norm_stderr": 0.032529096196131965 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537315, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537315 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.34656084656084657, "acc_stderr": 0.024508777521028424, "acc_norm": 0.34656084656084657, "acc_norm_stderr": 0.024508777521028424 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.373015873015873, "acc_stderr": 0.04325506042017086, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.04325506042017086 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6387096774193548, "acc_stderr": 0.027327548447957543, "acc_norm": 0.6387096774193548, "acc_norm_stderr": 0.027327548447957543 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.39901477832512317, "acc_stderr": 0.03445487686264716, "acc_norm": 0.39901477832512317, "acc_norm_stderr": 0.03445487686264716 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001974, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6484848484848484, "acc_stderr": 0.037282069986826503, "acc_norm": 0.6484848484848484, "acc_norm_stderr": 0.037282069986826503 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7121212121212122, "acc_stderr": 0.03225883512300992, "acc_norm": 0.7121212121212122, "acc_norm_stderr": 0.03225883512300992 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7512953367875648, "acc_stderr": 0.031195840877700286, "acc_norm": 0.7512953367875648, "acc_norm_stderr": 0.031195840877700286 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5487179487179488, "acc_stderr": 0.025230381238934833, "acc_norm": 0.5487179487179488, "acc_norm_stderr": 0.025230381238934833 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815642, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815642 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5588235294117647, "acc_stderr": 0.032252942323996406, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.032252942323996406 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.710091743119266, "acc_stderr": 0.019453066609201597, "acc_norm": 0.710091743119266, "acc_norm_stderr": 0.019453066609201597 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538271, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538271 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.75, "acc_stderr": 0.03039153369274154, "acc_norm": 0.75, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7257383966244726, "acc_stderr": 0.029041333510598028, "acc_norm": 0.7257383966244726, "acc_norm_stderr": 0.029041333510598028 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928276, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928276 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6793893129770993, "acc_stderr": 0.04093329229834278, "acc_norm": 0.6793893129770993, "acc_norm_stderr": 0.04093329229834278 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7107438016528925, "acc_stderr": 0.041391127276354626, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.041391127276354626 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6380368098159509, "acc_stderr": 0.037757007291414416, "acc_norm": 0.6380368098159509, "acc_norm_stderr": 0.037757007291414416 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.33035714285714285, "acc_stderr": 0.044642857142857116, "acc_norm": 0.33035714285714285, "acc_norm_stderr": 0.044642857142857116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.782051282051282, "acc_stderr": 0.027046857630716695, "acc_norm": 0.782051282051282, "acc_norm_stderr": 0.027046857630716695 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.015671006009339586, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.015671006009339586 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5982658959537572, "acc_stderr": 0.026394104177643634, "acc_norm": 0.5982658959537572, "acc_norm_stderr": 0.026394104177643634 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.45363128491620114, "acc_stderr": 0.016650437588269073, "acc_norm": 0.45363128491620114, "acc_norm_stderr": 0.016650437588269073 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.028180596328259283, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.028180596328259283 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6302250803858521, "acc_stderr": 0.027417996705630998, "acc_norm": 0.6302250803858521, "acc_norm_stderr": 0.027417996705630998 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5771604938271605, "acc_stderr": 0.027487472980871588, "acc_norm": 0.5771604938271605, "acc_norm_stderr": 0.027487472980871588 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.37943262411347517, "acc_stderr": 0.028947338851614105, "acc_norm": 0.37943262411347517, "acc_norm_stderr": 0.028947338851614105 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4230769230769231, "acc_stderr": 0.012618204066588392, "acc_norm": 0.4230769230769231, "acc_norm_stderr": 0.012618204066588392 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904612, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904612 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.020196594933541194, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.020196594933541194 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6489795918367347, "acc_stderr": 0.03055531675557364, "acc_norm": 0.6489795918367347, "acc_norm_stderr": 0.03055531675557364 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7064676616915423, "acc_stderr": 0.03220024104534205, "acc_norm": 0.7064676616915423, "acc_norm_stderr": 0.03220024104534205 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7543859649122807, "acc_stderr": 0.03301405946987249, "acc_norm": 0.7543859649122807, "acc_norm_stderr": 0.03301405946987249 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.32802937576499386, "mc1_stderr": 0.016435632932815025, "mc2": 0.47271389220607046, "mc2_stderr": 0.01511731653639185 }, "harness\|winogrande\|5": { "acc": 0.755327545382794, "acc_stderr": 0.012082125654159738 }, "harness\|gsm8k\|5": { "acc": 0.2926459438968916, "acc_stderr": 0.012532334368242883 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_martyn__llama2-megamerge-dare-13b-v2	[ "region:us" ]	2023-12-18T09:12:59+00:00	{"pretty_name": "Evaluation run of martyn/llama2-megamerge-dare-13b-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [martyn/llama2-megamerge-dare-13b-v2](https://huggingface.co/martyn/llama2-megamerge-dare-13b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_martyn__llama2-megamerge-dare-13b-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T09:10:01.856812](https://huggingface.co/datasets/open-llm-leaderboard/details_martyn__llama2-megamerge-dare-13b-v2/blob/main/results_2023-12-18T09-10-01.856812.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\": 0.5527610511045514,\n \"acc_stderr\": 0.03393471599188749,\n \"acc_norm\": 0.5576353360299099,\n \"acc_norm_stderr\": 0.03464924134012296,\n \"mc1\": 0.32802937576499386,\n \"mc1_stderr\": 0.016435632932815025,\n \"mc2\": 0.47271389220607046,\n \"mc2_stderr\": 0.01511731653639185\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5563139931740614,\n \"acc_stderr\": 0.014518421825670444,\n \"acc_norm\": 0.5938566552901023,\n \"acc_norm_stderr\": 0.014351656690097858\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6168094005178252,\n \"acc_stderr\": 0.004851705504790439,\n \"acc_norm\": 0.8093009360685123,\n \"acc_norm_stderr\": 0.003920494224883792\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5259259259259259,\n \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.5259259259259259,\n \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.569811320754717,\n \"acc_stderr\": 0.030471445867183238,\n \"acc_norm\": 0.569811320754717,\n \"acc_norm_stderr\": 0.030471445867183238\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5763888888888888,\n \"acc_stderr\": 0.041321250197233685,\n \"acc_norm\": 0.5763888888888888,\n \"acc_norm_stderr\": 0.041321250197233685\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4508670520231214,\n \"acc_stderr\": 0.037940126746970296,\n \"acc_norm\": 0.4508670520231214,\n \"acc_norm_stderr\": 0.037940126746970296\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.04440521906179328,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.04440521906179328\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.451063829787234,\n \"acc_stderr\": 0.032529096196131965,\n \"acc_norm\": 0.451063829787234,\n \"acc_norm_stderr\": 0.032529096196131965\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.34656084656084657,\n \"acc_stderr\": 0.024508777521028424,\n \"acc_norm\": 0.34656084656084657,\n \"acc_norm_stderr\": 0.024508777521028424\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6387096774193548,\n \"acc_stderr\": 0.027327548447957543,\n \"acc_norm\": 0.6387096774193548,\n \"acc_norm_stderr\": 0.027327548447957543\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.39901477832512317,\n \"acc_stderr\": 0.03445487686264716,\n \"acc_norm\": 0.39901477832512317,\n \"acc_norm_stderr\": 0.03445487686264716\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7121212121212122,\n \"acc_stderr\": 0.03225883512300992,\n \"acc_norm\": 0.7121212121212122,\n \"acc_norm_stderr\": 0.03225883512300992\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.7512953367875648,\n \"acc_stderr\": 0.031195840877700286,\n \"acc_norm\": 0.7512953367875648,\n \"acc_norm_stderr\": 0.031195840877700286\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5487179487179488,\n \"acc_stderr\": 0.025230381238934833,\n \"acc_norm\": 0.5487179487179488,\n \"acc_norm_stderr\": 0.025230381238934833\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815642,\n \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815642\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.032252942323996406,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.032252942323996406\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.710091743119266,\n \"acc_stderr\": 0.019453066609201597,\n \"acc_norm\": 0.710091743119266,\n \"acc_norm_stderr\": 0.019453066609201597\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598028,\n \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598028\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6322869955156951,\n \"acc_stderr\": 0.03236198350928276,\n \"acc_norm\": 0.6322869955156951,\n \"acc_norm_stderr\": 0.03236198350928276\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7107438016528925,\n \"acc_stderr\": 0.041391127276354626,\n \"acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.041391127276354626\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.6380368098159509,\n \"acc_stderr\": 0.037757007291414416,\n \"acc_norm\": 0.6380368098159509,\n \"acc_norm_stderr\": 0.037757007291414416\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.33035714285714285,\n \"acc_stderr\": 0.044642857142857116,\n \"acc_norm\": 0.33035714285714285,\n \"acc_norm_stderr\": 0.044642857142857116\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.782051282051282,\n \"acc_stderr\": 0.027046857630716695,\n \"acc_norm\": 0.782051282051282,\n \"acc_norm_stderr\": 0.027046857630716695\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.015671006009339586,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.015671006009339586\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.5982658959537572,\n \"acc_stderr\": 0.026394104177643634,\n \"acc_norm\": 0.5982658959537572,\n \"acc_norm_stderr\": 0.026394104177643634\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.45363128491620114,\n \"acc_stderr\": 0.016650437588269073,\n \"acc_norm\": 0.45363128491620114,\n \"acc_norm_stderr\": 0.016650437588269073\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.028180596328259283,\n \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.028180596328259283\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6302250803858521,\n \"acc_stderr\": 0.027417996705630998,\n \"acc_norm\": 0.6302250803858521,\n \"acc_norm_stderr\": 0.027417996705630998\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.5771604938271605,\n \"acc_stderr\": 0.027487472980871588,\n \"acc_norm\": 0.5771604938271605,\n \"acc_norm_stderr\": 0.027487472980871588\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.37943262411347517,\n \"acc_stderr\": 0.028947338851614105,\n \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.028947338851614105\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4230769230769231,\n \"acc_stderr\": 0.012618204066588392,\n \"acc_norm\": 0.4230769230769231,\n \"acc_norm_stderr\": 0.012618204066588392\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.020196594933541194,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.020196594933541194\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7064676616915423,\n \"acc_stderr\": 0.03220024104534205,\n \"acc_norm\": 0.7064676616915423,\n \"acc_norm_stderr\": 0.03220024104534205\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.03892494720807614\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.7543859649122807,\n \"acc_stderr\": 0.03301405946987249,\n \"acc_norm\": 0.7543859649122807,\n \"acc_norm_stderr\": 0.03301405946987249\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.32802937576499386,\n \"mc1_stderr\": 0.016435632932815025,\n \"mc2\": 0.47271389220607046,\n \"mc2_stderr\": 0.01511731653639185\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.755327545382794,\n \"acc_stderr\": 0.012082125654159738\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2926459438968916,\n \"acc_stderr\": 0.012532334368242883\n }\n}\n```", "repo_url": "https://huggingface.co/martyn/llama2-megamerge-dare-13b-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_18T09_10_01.856812", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T09-10-01.856812.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T09-10-01.856812.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_18T09_10_01.856812", "path": ["/details_harness\|gsm8k\|5_2023-12-18T09-10-01.856812.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-18T09-10-01.856812.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_18T09_10_01.856812", "path": ["/details_harness\|hellaswag\|10_2023-12-18T09-10-01.856812.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-18T09-10-01.856812.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_18T09_10_01.856812", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T09-10-01.856812.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T09-10-01.856812.parquet", 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["/details_harness\|truthfulqa:mc\|0_2023-12-18T09-10-01.856812.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T09-10-01.856812.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T09_10_01.856812", "path": ["/details_harness\|winogrande\|5_2023-12-18T09-10-01.856812.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T09-10-01.856812.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T09_10_01.856812", "path": ["results_2023-12-18T09-10-01.856812.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T09-10-01.856812.parquet"]}]}]}	2023-12-18T09:13:44+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of martyn/llama2-megamerge-dare-13b-v2 Dataset automatically created during the evaluation run of model martyn/llama2-megamerge-dare-13b-v2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T09:10:01.856812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of martyn/llama2-megamerge-dare-13b-v2\n\n\n\nDataset automatically created during the evaluation run of model martyn/llama2-megamerge-dare-13b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T09:10:01.856812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of martyn/llama2-megamerge-dare-13b-v2\n\n\n\nDataset automatically created during the evaluation run of model martyn/llama2-megamerge-dare-13b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T09:10:01.856812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 193, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of martyn/llama2-megamerge-dare-13b-v2\n\n\n\nDataset automatically created during the evaluation run of model martyn/llama2-megamerge-dare-13b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T09:10:01.856812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]" ]
d070d1113ce365df05903763a2017ec2d0d56118	# JEEBench(EMNLP 2023) Repository for the code and dataset for the paper: "Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models" accepted in EMNLP 2023 as a Main conference paper. https://aclanthology.org/2023.emnlp-main.468/ ## Citation If you use our dataset in your research, please cite it using the following ```latex @inproceedings{arora-etal-2023-llms, title = "Have {LLM}s Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models", author = "Arora, Daman and Singh, Himanshu and {Mausam}", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.468", doi = "10.18653/v1/2023.emnlp-main.468", pages = "7527--7543", abstract = "The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs. We curate 515 challenging pre-engineering mathematics, physics and chemistry problems from the highly competitive IIT JEE-Advanced exam. Long-horizon reasoning on top of deep in-domain knowledge is essential for solving problems in this benchmark. Our evaluation on various open-source and proprietary models reveals that the highest performance, even after using techniques like self-consistency, self-refinement and chain-of-thought prompting, is less than 40{\%}. The typical failure modes of GPT-4, the best model, are errors in algebraic manipulation, difficulty in grounding abstract concepts into mathematical equations accurately and failure in retrieving relevant domain-specific concepts. We also observe that by mere prompting, GPT-4 is unable to assess risk introduced by negative marking for incorrect answers. For this, we develop a post-hoc confidence-thresholding method over self-consistency, which enables effective response selection. We hope that our challenging benchmark will guide future re-search in problem-solving using LLMs.", } ```	daman1209arora/jeebench	[ "task_categories:question-answering", "size_categories:n<1K", "language:en", "license:mit", "chemistry", "physics", "mathematics", "region:us" ]	2023-12-18T09:24:26+00:00	{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "jeebench", "tags": ["chemistry", "physics", "mathematics"]}	2023-12-18T09:31:37+00:00	[]	[ "en" ]	TAGS #task_categories-question-answering #size_categories-n<1K #language-English #license-mit #chemistry #physics #mathematics #region-us	# JEEBench(EMNLP 2023) Repository for the code and dataset for the paper: "Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models" accepted in EMNLP 2023 as a Main conference paper. URL If you use our dataset in your research, please cite it using the following	[ "# JEEBench(EMNLP 2023)\nRepository for the code and dataset for the paper: \"Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models\" accepted in EMNLP 2023 as a Main conference paper. \nURL\n\n\nIf you use our dataset in your research, please cite it using the following" ]	[ "TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #chemistry #physics #mathematics #region-us \n", "# JEEBench(EMNLP 2023)\nRepository for the code and dataset for the paper: \"Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models\" accepted in EMNLP 2023 as a Main conference paper. \nURL\n\n\nIf you use our dataset in your research, please cite it using the following" ]	[ 49, 79 ]	[ "passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #chemistry #physics #mathematics #region-us \n# JEEBench(EMNLP 2023)\nRepository for the code and dataset for the paper: \"Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models\" accepted in EMNLP 2023 as a Main conference paper. \nURL\n\n\nIf you use our dataset in your research, please cite it using the following" ]
f2a58ad2cd777d5426b4bf9f7f7af826180f9d6c	# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T09:26:04.672254](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0/blob/main/results_2023-12-18T09-26-04.672254.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": 0.6495541508127465, "acc_stderr": 0.03195905121079232, "acc_norm": 0.6503791395121084, "acc_norm_stderr": 0.03260613037981816, "mc1": 0.41003671970624234, "mc1_stderr": 0.017217844717449325, "mc2": 0.5858188205527958, "mc2_stderr": 0.015472440902902757 }, "harness\|arc:challenge\|25": { "acc": 0.6271331058020477, "acc_stderr": 0.014131176760131169, "acc_norm": 0.658703071672355, "acc_norm_stderr": 0.013855831287497728 }, "harness\|hellaswag\|10": { "acc": 0.6736705835490938, "acc_stderr": 0.004679111783653906, "acc_norm": 0.8552081258713403, "acc_norm_stderr": 0.0035117170854519824 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7171052631578947, "acc_stderr": 0.03665349695640767, "acc_norm": 0.7171052631578947, "acc_norm_stderr": 0.03665349695640767 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4021164021164021, "acc_stderr": 0.025253032554997695, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.025253032554997695 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8064516129032258, "acc_stderr": 0.022475258525536057, "acc_norm": 0.8064516129032258, "acc_norm_stderr": 0.022475258525536057 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009181, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009181 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.02399150050031304, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.02399150050031304 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.02889774874113115, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.02889774874113115 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233483, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233483 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596914, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596914 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.03989139859531771, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.03989139859531771 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8339719029374202, "acc_stderr": 0.0133064782430663, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.0133064782430663 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7369942196531792, "acc_stderr": 0.02370309952525817, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.02370309952525817 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.38100558659217876, "acc_stderr": 0.016242028834053616, "acc_norm": 0.38100558659217876, "acc_norm_stderr": 0.016242028834053616 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7438271604938271, "acc_stderr": 0.024288533637726095, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.024288533637726095 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.475177304964539, "acc_stderr": 0.029790719243829727, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.029790719243829727 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4654498044328553, "acc_stderr": 0.012739711554045706, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045706 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7061224489795919, "acc_stderr": 0.02916273841024977, "acc_norm": 0.7061224489795919, "acc_norm_stderr": 0.02916273841024977 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578337, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578337 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41003671970624234, "mc1_stderr": 0.017217844717449325, "mc2": 0.5858188205527958, "mc2_stderr": 0.015472440902902757 }, "harness\|winogrande\|5": { "acc": 0.7963693764798737, "acc_stderr": 0.011317798781626918 }, "harness\|gsm8k\|5": { "acc": 0.6671721000758151, "acc_stderr": 0.012979892496598281 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0	[ "region:us" ]	2023-12-18T09:28:55+00:00	{"pretty_name": "Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0", "dataset_summary": "Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T09:26:04.672254](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0/blob/main/results_2023-12-18T09-26-04.672254.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\": 0.6495541508127465,\n \"acc_stderr\": 0.03195905121079232,\n \"acc_norm\": 0.6503791395121084,\n \"acc_norm_stderr\": 0.03260613037981816,\n \"mc1\": 0.41003671970624234,\n \"mc1_stderr\": 0.017217844717449325,\n \"mc2\": 0.5858188205527958,\n \"mc2_stderr\": 0.015472440902902757\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131169,\n \"acc_norm\": 0.658703071672355,\n \"acc_norm_stderr\": 0.013855831287497728\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6736705835490938,\n \"acc_stderr\": 0.004679111783653906,\n \"acc_norm\": 0.8552081258713403,\n \"acc_norm_stderr\": 0.0035117170854519824\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997695,\n \"acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997695\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8064516129032258,\n \"acc_stderr\": 0.022475258525536057,\n \"acc_norm\": 0.8064516129032258,\n \"acc_norm_stderr\": 0.022475258525536057\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.02399150050031304,\n \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.02399150050031304\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113115,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113115\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233483,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233483\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8339719029374202,\n \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.02370309952525817,\n \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.02370309952525817\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.38100558659217876,\n \"acc_stderr\": 0.016242028834053616,\n \"acc_norm\": 0.38100558659217876,\n \"acc_norm_stderr\": 0.016242028834053616\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4654498044328553,\n \"acc_stderr\": 0.012739711554045706,\n \"acc_norm\": 0.4654498044328553,\n \"acc_norm_stderr\": 0.012739711554045706\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.02916273841024977,\n \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.02916273841024977\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5301204819277109,\n \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.41003671970624234,\n \"mc1_stderr\": 0.017217844717449325,\n \"mc2\": 0.5858188205527958,\n \"mc2_stderr\": 0.015472440902902757\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626918\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6671721000758151,\n \"acc_stderr\": 0.012979892496598281\n }\n}\n```", "repo_url": "https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|gsm8k\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hellaswag\|10_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T09-26-04.672254.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T09-26-04.672254.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-international_law\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-management\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-marketing\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["/details_harness\|winogrande\|5_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T09-26-04.672254.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T09_26_04.672254", "path": ["results_2023-12-18T09-26-04.672254.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T09-26-04.672254.parquet"]}]}]}	2023-12-18T09:29:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0 Dataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T09:26:04.672254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T09:26:04.672254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T09:26:04.672254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 195, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T09:26:04.672254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]
f8ae6495b3a275382c51de1bd3b2a1f8666df363	# Dataset Card for "r_j5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	jksheth/r_j5	[ "region:us" ]	2023-12-18T10:20:57+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "price", "dtype": "float64"}, {"name": "category_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "file_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1841538, "num_examples": 12441}], "download_size": 688852, "dataset_size": 1841538}}	2023-12-18T10:21:02+00:00	[]	[]	TAGS #region-us	# Dataset Card for "r_j5" More Information needed	[ "# Dataset Card for \"r_j5\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"r_j5\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"r_j5\"\n\nMore Information needed" ]
f82d69126cf25fb80e4e5eb37275c118a5babee2	# Dataset Card for Evaluation run of Mihaiii/Metis-0.3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Mihaiii/Metis-0.3](https://huggingface.co/Mihaiii/Metis-0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Mihaiii__Metis-0.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T10:29:51.346737](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Metis-0.3/blob/main/results_2023-12-18T10-29-51.346737.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": 0.6087596961823534, "acc_stderr": 0.033143419693783545, "acc_norm": 0.6135679004202929, "acc_norm_stderr": 0.03381506918300307, "mc1": 0.5263157894736842, "mc1_stderr": 0.017479241161975453, "mc2": 0.6755936296533276, "mc2_stderr": 0.015113334433722326 }, "harness\|arc:challenge\|25": { "acc": 0.5819112627986348, "acc_stderr": 0.01441398839699608, "acc_norm": 0.6271331058020477, "acc_norm_stderr": 0.014131176760131169 }, "harness\|hellaswag\|10": { "acc": 0.6609241187014538, "acc_stderr": 0.004724281487819376, "acc_norm": 0.8480382393945429, "acc_norm_stderr": 0.0035825015965645452 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5703703703703704, "acc_stderr": 0.042763494943765995, "acc_norm": 0.5703703703703704, "acc_norm_stderr": 0.042763494943765995 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.618421052631579, "acc_stderr": 0.039531733777491945, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.039531733777491945 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6736111111111112, "acc_stderr": 0.03921067198982266, "acc_norm": 0.6736111111111112, "acc_norm_stderr": 0.03921067198982266 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956913, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956913 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.41228070175438597, "acc_stderr": 0.046306532033665956, "acc_norm": 0.41228070175438597, "acc_norm_stderr": 0.046306532033665956 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419035, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419035 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6548387096774193, "acc_stderr": 0.02704574657353433, "acc_norm": 0.6548387096774193, "acc_norm_stderr": 0.02704574657353433 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386417, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386417 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8393782383419689, "acc_stderr": 0.026499057701397443, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.026499057701397443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5794871794871795, "acc_stderr": 0.025028610276710862, "acc_norm": 0.5794871794871795, "acc_norm_stderr": 0.025028610276710862 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815632, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815632 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6554621848739496, "acc_stderr": 0.030868682604121626, "acc_norm": 0.6554621848739496, "acc_norm_stderr": 0.030868682604121626 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8018348623853211, "acc_stderr": 0.017090573804217902, "acc_norm": 0.8018348623853211, "acc_norm_stderr": 0.017090573804217902 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538271, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538271 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501954, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501954 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416827, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416827 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.732824427480916, "acc_stderr": 0.038808483010823944, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.038808483010823944 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8264462809917356, "acc_stderr": 0.0345727283691767, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.0345727283691767 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7300613496932515, "acc_stderr": 0.03487825168497892, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.03487825168497892 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.02220930907316561, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.02220930907316561 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.014866821664709588, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.014866821664709588 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6907514450867052, "acc_stderr": 0.024883140570071762, "acc_norm": 0.6907514450867052, "acc_norm_stderr": 0.024883140570071762 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3139664804469274, "acc_stderr": 0.015521923933523646, "acc_norm": 0.3139664804469274, "acc_norm_stderr": 0.015521923933523646 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7098765432098766, "acc_stderr": 0.025251173936495033, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495033 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4574468085106383, "acc_stderr": 0.029719281272236844, "acc_norm": 0.4574468085106383, "acc_norm_stderr": 0.029719281272236844 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4322033898305085, "acc_stderr": 0.012652297777114968, "acc_norm": 0.4322033898305085, "acc_norm_stderr": 0.012652297777114968 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6066176470588235, "acc_stderr": 0.029674288281311155, "acc_norm": 0.6066176470588235, "acc_norm_stderr": 0.029674288281311155 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6290849673202614, "acc_stderr": 0.019542101564854128, "acc_norm": 0.6290849673202614, "acc_norm_stderr": 0.019542101564854128 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675596, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675596 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7412935323383084, "acc_stderr": 0.03096590312357303, "acc_norm": 0.7412935323383084, "acc_norm_stderr": 0.03096590312357303 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653693, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653693 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5263157894736842, "mc1_stderr": 0.017479241161975453, "mc2": 0.6755936296533276, "mc2_stderr": 0.015113334433722326 }, "harness\|winogrande\|5": { "acc": 0.7726913970007893, "acc_stderr": 0.011778612167091088 }, "harness\|gsm8k\|5": { "acc": 0.3934799090219864, "acc_stderr": 0.01345631582840459 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_Mihaiii__Metis-0.3	[ "region:us" ]	2023-12-18T10:32:43+00:00	{"pretty_name": "Evaluation run of Mihaiii/Metis-0.3", "dataset_summary": "Dataset automatically created during the evaluation run of model [Mihaiii/Metis-0.3](https://huggingface.co/Mihaiii/Metis-0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Mihaiii__Metis-0.3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T10:29:51.346737](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Metis-0.3/blob/main/results_2023-12-18T10-29-51.346737.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\": 0.6087596961823534,\n \"acc_stderr\": 0.033143419693783545,\n \"acc_norm\": 0.6135679004202929,\n \"acc_norm_stderr\": 0.03381506918300307,\n \"mc1\": 0.5263157894736842,\n \"mc1_stderr\": 0.017479241161975453,\n \"mc2\": 0.6755936296533276,\n \"mc2_stderr\": 0.015113334433722326\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5819112627986348,\n \"acc_stderr\": 0.01441398839699608,\n \"acc_norm\": 0.6271331058020477,\n \"acc_norm_stderr\": 0.014131176760131169\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6609241187014538,\n \"acc_stderr\": 0.004724281487819376,\n \"acc_norm\": 0.8480382393945429,\n \"acc_norm_stderr\": 0.0035825015965645452\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.039531733777491945,\n \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.039531733777491945\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6736111111111112,\n \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.6736111111111112,\n \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.41228070175438597,\n \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.41228070175438597,\n \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419035,\n \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419035\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 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"path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": 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["/details_harness\|truthfulqa:mc\|0_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["/details_harness\|winogrande\|5_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T10-29-51.346737.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T10_29_51.346737", "path": ["results_2023-12-18T10-29-51.346737.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T10-29-51.346737.parquet"]}]}]}	2023-12-18T10:33:28+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Mihaiii/Metis-0.3 Dataset automatically created during the evaluation run of model Mihaiii/Metis-0.3 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T10:29:51.346737(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of Mihaiii/Metis-0.3\n\n\n\nDataset automatically created during the evaluation run of model Mihaiii/Metis-0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T10:29:51.346737(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Mihaiii/Metis-0.3\n\n\n\nDataset automatically created during the evaluation run of model Mihaiii/Metis-0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T10:29:51.346737(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 175, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Mihaiii/Metis-0.3\n\n\n\nDataset automatically created during the evaluation run of model Mihaiii/Metis-0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T10:29:51.346737(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
fdab3e38c3e551f3103b4405097b92372179806d	# CBIS-DDSM Dataset ## Overview The CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset is a collection of medical images for breast cancer research. It is a subset of the Digital Database for Screening Mammography (DDSM) and has been carefully curated to facilitate research in computer-aided diagnosis and medical image analysis. ## Dataset Description - Total Instances: - Image Modalities: Mammography, Breast Ultrasound - Annotation Types: Masses, Calcifications, Benign, Malignant - Resolution: Varies across modalities and instances ## Contents - Images: The dataset includes mammography and breast ultrasound images in DICOM format. - Annotations: Ground truth annotations for masses and calcifications, along with additional metadata. ## Example Usage ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("Reverb/CBIS-DDSM", split="train") ```	Reverb/CBIS-DDSM	[ "task_categories:image-classification", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "medical", "region:us" ]	2023-12-18T11:17:38+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "tags": ["medical"]}	2023-12-18T14:50:49+00:00	[]	[ "en" ]	TAGS #task_categories-image-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #medical #region-us	# CBIS-DDSM Dataset ## Overview The CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset is a collection of medical images for breast cancer research. It is a subset of the Digital Database for Screening Mammography (DDSM) and has been carefully curated to facilitate research in computer-aided diagnosis and medical image analysis. ## Dataset Description - Total Instances: - Image Modalities: Mammography, Breast Ultrasound - Annotation Types: Masses, Calcifications, Benign, Malignant - Resolution: Varies across modalities and instances ## Contents - Images: The dataset includes mammography and breast ultrasound images in DICOM format. - Annotations: Ground truth annotations for masses and calcifications, along with additional metadata. ## Example Usage	[ "# CBIS-DDSM Dataset", "## Overview\n\nThe CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset is a collection of medical images for breast cancer research. It is a subset of the Digital Database for Screening Mammography (DDSM) and has been carefully curated to facilitate research in computer-aided diagnosis and medical image analysis.", "## Dataset Description\n\n- Total Instances: \n- Image Modalities: Mammography, Breast Ultrasound\n- Annotation Types: Masses, Calcifications, Benign, Malignant\n- Resolution: Varies across modalities and instances", "## Contents\n\n- Images: The dataset includes mammography and breast ultrasound images in DICOM format.\n- Annotations: Ground truth annotations for masses and calcifications, along with additional metadata.", "## Example Usage" ]	[ "TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #medical #region-us \n", "# CBIS-DDSM Dataset", "## Overview\n\nThe CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset is a collection of medical images for breast cancer research. It is a subset of the Digital Database for Screening Mammography (DDSM) and has been carefully curated to facilitate research in computer-aided diagnosis and medical image analysis.", "## Dataset Description\n\n- Total Instances: \n- Image Modalities: Mammography, Breast Ultrasound\n- Annotation Types: Masses, Calcifications, Benign, Malignant\n- Resolution: Varies across modalities and instances", "## Contents\n\n- Images: The dataset includes mammography and breast ultrasound images in DICOM format.\n- Annotations: Ground truth annotations for masses and calcifications, along with additional metadata.", "## Example Usage" ]	[ 44, 8, 78, 54, 47, 5 ]	[ "passage: TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #medical #region-us \n# CBIS-DDSM Dataset## Overview\n\nThe CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset is a collection of medical images for breast cancer research. It is a subset of the Digital Database for Screening Mammography (DDSM) and has been carefully curated to facilitate research in computer-aided diagnosis and medical image analysis.## Dataset Description\n\n- Total Instances: \n- Image Modalities: Mammography, Breast Ultrasound\n- Annotation Types: Masses, Calcifications, Benign, Malignant\n- Resolution: Varies across modalities and instances## Contents\n\n- Images: The dataset includes mammography and breast ultrasound images in DICOM format.\n- Annotations: Ground truth annotations for masses and calcifications, along with additional metadata.## Example Usage" ]
198a370507acefd61404fbbda43ccecb372874dc	# Bulletin officiel des finances publiques - impôts, non-instruct (11-12-2023) This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice. Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach. Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks. Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways: - Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions. - Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs. - Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more. - Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs. - Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text. ## Dataset generation This JSON file is a list of dictionaries, each dictionary contains the following fields: - `instruction`: `string`, presenting the instruction linked to the element. - `input`: `string`, signifying the input details for the element. - `output`: `string`, indicating the output information for the element. We used the following list of instructions for generating the dataset: ```python instructions = [ "Compose l'intégralité de la publication sous forme écrite.", "Écris la totalité du contenu de la publication.", "Formule la totalité du texte présent dans la publication.", "Produis l'intégralité de la publication en écriture.", "Développe la publication dans son ensemble par écrit.", "Génère l'ensemble du texte contenu dans la publication.", "Formule le contenu intégral de la publication en entier.", "Rédige la totalité du texte de la publication en entier.", "Compose l'intégralité du contenu textuel de la publication.", "Rédige l'ensemble du texte qui constitue la publication.", "Composez l'intégralité de la publication sous forme écrite.", "Écrivez la totalité du contenu de la publication.", "Formulez la totalité du texte présent dans la publication.", "Développez la publication dans son ensemble par écrit.", "Générez l'ensemble du texte contenu dans la publication.", "Formulez le contenu intégral de la publication en entier.", "Rédigez la totalité du texte de la publication en entier.", "Composez l'intégralité du contenu textuel de la publication.", "Écrivez la publication dans son intégralité en termes de texte.", "Rédigez l'ensemble du texte qui constitue la publication.", "Composer l'intégralité de la publication sous forme écrite.", "Écrire la totalité du contenu de la publication.", "Formuler la totalité du texte présent dans la publication.", "Produire l'intégralité de la publication en écriture.", "Développer la publication dans son ensemble par écrit.", "Générer l'ensemble du texte contenu dans la publication.", "Formuler le contenu intégral de la publication en entier.", "Rédiger la totalité du texte de la publication en entier.", "Composer l'intégralité du contenu textuel de la publication.", "Rédiger l'ensemble du texte qui constitue la publication.", "Quelles sont les dispositions de la publication ?", "Quelles dispositions sont incluses dans la publication ?", "Quelles sont les dispositions énoncées dans la publication ?", "Quel est le texte intégral de la publication ?", "Quelle est la lettre de la publication ?" ] ``` ## Citing this project If you use this code in your research, please use the following BibTeX entry. ```BibTeX @misc{louisbrulenaudet2023, author = {Louis Brulé Naudet}, title = {Bulletin officiel des finances publiques - impôts, non-instruct (11-12-2023)}, howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/bofip}}, year = {2023} } ``` ## Feedback If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).	louisbrulenaudet/bofip	[ "task_categories:text-generation", "task_categories:table-question-answering", "task_categories:summarization", "task_categories:conversational", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:fr", "license:apache-2.0", "finetuning", "legal", "french law", "droit français", "Bofip", "region:us" ]	2023-12-18T11:36:17+00:00	{"language": ["fr"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation", "table-question-answering", "summarization", "conversational"], "pretty_name": "Bulletin officiel des finances publiques - imp\u00f4ts", "tags": ["finetuning", "legal", "french law", "droit fran\u00e7ais", "Bofip"]}	2023-12-18T13:21:46+00:00	[]	[ "fr" ]	TAGS #task_categories-text-generation #task_categories-table-question-answering #task_categories-summarization #task_categories-conversational #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-French #license-apache-2.0 #finetuning #legal #french law #droit français #Bofip #region-us	# Bulletin officiel des finances publiques - impôts, non-instruct (11-12-2023) This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice. Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach. Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks. Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways: - Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions. - Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs. - Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more. - Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs. - Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text. ## Dataset generation This JSON file is a list of dictionaries, each dictionary contains the following fields: - 'instruction': 'string', presenting the instruction linked to the element. - 'input': 'string', signifying the input details for the element. - 'output': 'string', indicating the output information for the element. We used the following list of instructions for generating the dataset: ## Citing this project If you use this code in your research, please use the following BibTeX entry. ## Feedback If you have any feedback, please reach out at louisbrulenaudet@URL.	[ "# Bulletin officiel des finances publiques - impôts, non-instruct (11-12-2023)\n\nThis project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice. \n\nFine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.\n\nInstruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.\n\nInstruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:\n\n- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.\n- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.\n- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.\n- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.\n- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.", "## Dataset generation\n\nThis JSON file is a list of dictionaries, each dictionary contains the following fields:\n\n- 'instruction': 'string', presenting the instruction linked to the element.\n- 'input': 'string', signifying the input details for the element.\n- 'output': 'string', indicating the output information for the element.\n\nWe used the following list of instructions for generating the dataset:", "## Citing this project\n\nIf you use this code in your research, please use the following BibTeX entry.", "## Feedback\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL." ]	[ "TAGS\n#task_categories-text-generation #task_categories-table-question-answering #task_categories-summarization #task_categories-conversational #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-French #license-apache-2.0 #finetuning #legal #french law #droit français #Bofip #region-us \n", "# Bulletin officiel des finances publiques - impôts, non-instruct (11-12-2023)\n\nThis project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice. \n\nFine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.\n\nInstruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.\n\nInstruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:\n\n- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.\n- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.\n- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.\n- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.\n- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.", "## Dataset generation\n\nThis JSON file is a list of dictionaries, each dictionary contains the following fields:\n\n- 'instruction': 'string', presenting the instruction linked to the element.\n- 'input': 'string', signifying the input details for the element.\n- 'output': 'string', indicating the output information for the element.\n\nWe used the following list of instructions for generating the dataset:", "## Citing this project\n\nIf you use this code in your research, please use the following BibTeX entry.", "## Feedback\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL." ]	[ 112, 510, 100, 24, 21 ]	[ "passage: TAGS\n#task_categories-text-generation #task_categories-table-question-answering #task_categories-summarization #task_categories-conversational #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-French #license-apache-2.0 #finetuning #legal #french law #droit français #Bofip #region-us \n" ]
b16fa32116b2bdd11d6253181ecd76e1d94ec0db	# Dataset Card for "gu-course-syllabus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	anordkvist/gu-course-syllabus	[ "region:us" ]	2023-12-18T12:08:23+00:00	{"dataset_info": {"features": [{"name": "course_code", "dtype": "string"}, {"name": "Confirmation", "dtype": "string"}, {"name": "Position in the educational system", "dtype": "string"}, {"name": "Entry requirements", "dtype": "string"}, {"name": "Learning outcomes", "dtype": "string"}, {"name": "Course content", "dtype": "string"}, {"name": "Form of teaching", "dtype": "string"}, {"name": "Assessment", "dtype": "string"}, {"name": "Grades", "dtype": "string"}, {"name": "Course evaluation", "dtype": "string"}, {"name": "Additional information", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 19007262, "num_examples": 2971}], "download_size": 7025801, "dataset_size": 19007262}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-12-22T11:16:50+00:00	[]	[]	TAGS #region-us	# Dataset Card for "gu-course-syllabus" More Information needed	[ "# Dataset Card for \"gu-course-syllabus\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"gu-course-syllabus\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"gu-course-syllabus\"\n\nMore Information needed" ]
a4454347d4f34dddd839a090d4ddd4fa7cae1ff7	### Dataset Card for "Bias Detection Counterfactuals" Summary - Description: This dataset is designed to assess the fairness of language models by providing sentences that systematically vary by attributes such as gender, race, and religion. It allows for bias measurement, response consistency evaluation, and counterfactual fairness testing. - Purpose: To provide a tool for researchers and practitioners to identify and mitigate biases in language models, ensuring more equitable and inclusive outcomes. - Supported Tasks: Bias detection, fairness assessment, counterfactual analysis, sentiment analysis. - Languages: English Composition - Size of Dataset: 520 - Variability: Attributes varied include gender, race, religion. - Structure: Each record is a sentence with placeholders for attributes that are systematically varied. Source Data - Initial Data Collection and Normalization: Real-world expereinces by annotators. Annotations - Annotation process: Human Please cite us if you use this data/	newsmediabias/Bias-Detection-Counterfactuals	[ "license:cc-by-nc-4.0", "region:us" ]	2023-12-18T12:37:31+00:00	{"license": "cc-by-nc-4.0"}	2023-12-18T12:43:35+00:00	[]	[]	TAGS #license-cc-by-nc-4.0 #region-us	### Dataset Card for "Bias Detection Counterfactuals" Summary - Description: This dataset is designed to assess the fairness of language models by providing sentences that systematically vary by attributes such as gender, race, and religion. It allows for bias measurement, response consistency evaluation, and counterfactual fairness testing. - Purpose: To provide a tool for researchers and practitioners to identify and mitigate biases in language models, ensuring more equitable and inclusive outcomes. - Supported Tasks: Bias detection, fairness assessment, counterfactual analysis, sentiment analysis. - Languages: English Composition - Size of Dataset: 520 - Variability: Attributes varied include gender, race, religion. - Structure: Each record is a sentence with placeholders for attributes that are systematically varied. Source Data - Initial Data Collection and Normalization: Real-world expereinces by annotators. Annotations - Annotation process: Human Please cite us if you use this data/	[ "### Dataset Card for \"Bias Detection Counterfactuals\"\n\nSummary\n\n- Description: This dataset is designed to assess the fairness of language models by providing sentences that systematically vary by attributes such as gender, race, and religion. It allows for bias measurement, response consistency evaluation, and counterfactual fairness testing.\n- Purpose: To provide a tool for researchers and practitioners to identify and mitigate biases in language models, ensuring more equitable and inclusive outcomes.\n- Supported Tasks: Bias detection, fairness assessment, counterfactual analysis, sentiment analysis.\n- Languages: English\n\nComposition\n\n- Size of Dataset: 520\n- Variability: Attributes varied include gender, race, religion.\n- Structure: Each record is a sentence with placeholders for attributes that are systematically varied.\n\nSource Data\n\n- Initial Data Collection and Normalization: Real-world expereinces by annotators.\n\nAnnotations\n\n- Annotation process: Human\n\nPlease cite us if you use this data/" ]	[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "### Dataset Card for \"Bias Detection Counterfactuals\"\n\nSummary\n\n- Description: This dataset is designed to assess the fairness of language models by providing sentences that systematically vary by attributes such as gender, race, and religion. It allows for bias measurement, response consistency evaluation, and counterfactual fairness testing.\n- Purpose: To provide a tool for researchers and practitioners to identify and mitigate biases in language models, ensuring more equitable and inclusive outcomes.\n- Supported Tasks: Bias detection, fairness assessment, counterfactual analysis, sentiment analysis.\n- Languages: English\n\nComposition\n\n- Size of Dataset: 520\n- Variability: Attributes varied include gender, race, religion.\n- Structure: Each record is a sentence with placeholders for attributes that are systematically varied.\n\nSource Data\n\n- Initial Data Collection and Normalization: Real-world expereinces by annotators.\n\nAnnotations\n\n- Annotation process: Human\n\nPlease cite us if you use this data/" ]	[ 17, 235 ]	[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n### Dataset Card for \"Bias Detection Counterfactuals\"\n\nSummary\n\n- Description: This dataset is designed to assess the fairness of language models by providing sentences that systematically vary by attributes such as gender, race, and religion. It allows for bias measurement, response consistency evaluation, and counterfactual fairness testing.\n- Purpose: To provide a tool for researchers and practitioners to identify and mitigate biases in language models, ensuring more equitable and inclusive outcomes.\n- Supported Tasks: Bias detection, fairness assessment, counterfactual analysis, sentiment analysis.\n- Languages: English\n\nComposition\n\n- Size of Dataset: 520\n- Variability: Attributes varied include gender, race, religion.\n- Structure: Each record is a sentence with placeholders for attributes that are systematically varied.\n\nSource Data\n\n- Initial Data Collection and Normalization: Real-world expereinces by annotators.\n\nAnnotations\n\n- Annotation process: Human\n\nPlease cite us if you use this data/" ]
4424f18d83c6b0828fc1e640055284bf9ee6c9da	A dataset of parallel translation-based instructions for Indonesian language as a target language. </br> Materials are taken from randomly selected children stories at https://storyweaver.org.in, under CC-By-SA-4.0 license. </br> The template IDs are:</br> (1, 'Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Inggris ke teks dalam Bahasa Indonesia:', 'Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:'),</br> (2, 'Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Jawa ke teks dalam Bahasa Indonesia:', 'Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:'),</br> (3, 'Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Sunda ke teks dalam Bahasa Indonesia:', 'Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:'),</br></br> Data is mainly composed of three parallel language samples as prompt inputs and target completions:</br> 1. <b>en_id</b></br> Prompt/instruction language: <i>Indonesia</i></br> Source/input language:<i>English</i></br> Target/output language:<i>Indonesia</i></br> Size: 1065 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Inggris ke teks dalam Bahasa Indonesia:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:\n\n{output}</br> 2. <b>javanese_id</b></br> Prompt/instruction language: <i>Indonesia</i></br> Source/input language: <i>Javanese</i></br> Target/output language:<i>Indonesia</i></br> Size: 403 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Jawa ke teks dalam Bahasa Indonesia:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:\n\n{output}</br> 3. <b>sunda_id</b></br> Prompt/instruction language: <i>Indonesia</i></br> Source/input language: <i>Sundanese</i></br> Target/output language: <i>Indonesia</i></br> Size: 94 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Sunda ke teks dalam Bahasa Indonesia:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:\n\n{output}</br> Data was originally prepared for enriching multilingual resources in Open Science AYA Project (2023).	Iftitahu/indonesian_instruct_stories	[ "task_categories:translation", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:1K<n<10K", "language:id", "language:jv", "language:su", "language:en", "license:cc-by-4.0", "region:us" ]	2023-12-18T13:28:32+00:00	{"language": ["id", "jv", "su", "en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["translation", "text-generation", "text2text-generation"]}	2024-01-25T07:05:14+00:00	[]	[ "id", "jv", "su", "en" ]	TAGS #task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-1K<n<10K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us	A dataset of parallel translation-based instructions for Indonesian language as a target language. </br> Materials are taken from randomly selected children stories at URL, under CC-By-SA-4.0 license. </br> The template IDs are:</br> (1, 'Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Inggris ke teks dalam Bahasa Indonesia:', 'Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:'),</br> (2, 'Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Jawa ke teks dalam Bahasa Indonesia:', 'Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:'),</br> (3, 'Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Sunda ke teks dalam Bahasa Indonesia:', 'Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:'),</br></br> Data is mainly composed of three parallel language samples as prompt inputs and target completions:</br> 1. <b>en_id</b></br> Prompt/instruction language: <i>Indonesia</i></br> Source/input language:<i>English</i></br> Target/output language:<i>Indonesia</i></br> Size: 1065 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Inggris ke teks dalam Bahasa Indonesia:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:\n\n{output}</br> 2. <b>javanese_id</b></br> Prompt/instruction language: <i>Indonesia</i></br> Source/input language: <i>Javanese</i></br> Target/output language:<i>Indonesia</i></br> Size: 403 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Jawa ke teks dalam Bahasa Indonesia:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:\n\n{output}</br> 3. <b>sunda_id</b></br> Prompt/instruction language: <i>Indonesia</i></br> Source/input language: <i>Sundanese</i></br> Target/output language: <i>Indonesia</i></br> Size: 94 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahkanlah penggalan teks cerita anak berikut dari teks berbahasa Sunda ke teks dalam Bahasa Indonesia:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahan atau padanan teks tersebut dalam Bahasa Indonesia adalah:\n\n{output}</br> Data was originally prepared for enriching multilingual resources in Open Science AYA Project (2023).	[]	[ "TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-1K<n<10K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us \n" ]	[ 80 ]	[ "passage: TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-1K<n<10K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us \n" ]
71f3faa6574e072188fedc0cb30275644b99d37e	## Dataset Description - Point of Contact: [email protected], [email protected] ### Dataset Summary Tuumailubotti dataset is a Finnish-speaking conversational dataset containing 364 rows of simulated reflective conversations about work life from a neurodiversity-affirming perspective. Each row of the dataset starts with a system prompt, followed by either four or five exchanges between bot and user. The dataset was commissioned as part of the Tuumailubotti project by the Finnish Broadcasting Company, YLE, and features data both from contributors from inside and outside the company. ### Supported Tasks - `conversational`: The dataset can be used to train a model for Conversational tasks, such as chatbots. We trained it for this purpose using TurkuNLP/gpt3-finnish-8B (with 8-bit quantization). https://huggingface.co/TurkuNLP/gpt3-finnish-8B For the most part, the dataset can also be utilized in a question answering context, but please note that compared to traditional q&a datasets the roles are reversed. ### Languages - `fi` The dataset has a mix of colloquial and formal Finnish. ## Dataset Structure ### Data Instances ``` { 'System': ..., 'Bot1': ..., 'User1': ..., 'Bot2': ..., 'User2':..., 'Bot3':... ... } ``` We collected the data originally in the OASST format with multiple responses. We converted this structure to a tabular format for training, as at this phase we were not interested in reinforcement learning. If the project goes forward, we might release a new version of the dataset with a branching structure. ### Data Fields - `System`: String, The system prompt for the current conversation; a very brief description of the purpose of the bot and its task. - `Bot1`: String, First turn of dialogue. - `User1`: String, Second turn of dialogue. - `Bot2`: String, Third turn of dialogue. - `User2`: String, Fourth turn of dialogue. - `Bot3`: String, Fifth turn of dialogue, sometimes left empty. There are no splits in the dataset. ## Dataset Creation ### Curation Rationale This dataset contains simulated conversations about workplace wellbeing and work life in general. It has been assembled for the purpose of creating a neurodiversity-affirming workplace reflection chatbot that can help employees to contemplate questions they could never ask from a human HR representative. ### Source Data The data contributors were instructed to imagine what kind of conversations they personally would like to have with the finished bot, and then write down these fictitious conversations without mentioning any real-world personal data. We provided three topics, based on the "TAOS" development discussion framework: goals, wellbeing and know-how. Additionally, the contributors were also encouraged to come up with their own topics. Because of this, the data also includes more broad discussion about neurodiversity and the impact of today's competitive society on individual lives. As we found out that the base Finnish GPT models contain either outdated or outright harmful information about neurodiversity-related topics, we also included some information that utilized the vocabulary created by Autistic Spectrum Finland (ASY). #### Initial Data Collection and Normalization While obvious typos have been fixed from the responses of the bot and the system prompts, the dataset has not otherwise been filtered or altered. This phase was omitted on purpose in order to research the potential diversity ingrained in this dataset. #### Who are the source language producers? The data was provided by contributors both inside and outside of YLE, with a roughly 70-30-split. The data was gathered from anonymous employees from YLE's neurodiversity network, as well as known persons affiliated with the project. The outside contributors all identify as members of neurominorities and their identities are known, though they wish to remain anonymous. All major contributors (those who contributed more than one or two samples ) were compensated for their time. ### Personal and Sensitive Information Because of the delicate subject matter, no diagnostic information, personal information etc. was gathered about the contributors. While we know that many contributors either identify as members of neurominorities and/or have i.e.. an autism or ADHD diagnosis, no identifying information was featured in the data. This was also done because we forbid this data to be used in medical research or in other ways in which it could be used to otherwise identify or segregate members of neurominorities. The contributors were instructed to pretend to be fictitious users while giving responses, and the dataset has also been manually checked in case of personal or company data. ## Considerations for Using the Data ### Social Impact of Dataset We believe that the dataset could be widely useful in the emerging field of Finnish-speaking chat applications. One of the central aims of the Tuumailubotti project is to improve the representation of neurodiversity in Large Language Models (LLMs). While we did not purposefully seek to create a dataset that would only feature under-represented rhetoricity, we tried to create conditions that were favorable to its emergence. We will now investigate whether we succeeded in our goal to create a chatbot that could allow for a wider range of communication, and whether this translates to increased accessibility of the bot itself. We also acknowledge that the data could be used for harmful scenarios, such as the identification of neurodivergent individuals. This is why we have kept information about the contributors vague on purpose. ### Discussion of Biases We tried to make the data more broadly applicable outside of YLE by purposefully avoiding company jargon. However, with 70% of the data coming from YLE employees, the data is bound to be skewed toward the kind of wofk life scenarios faced by employees of a broadcast company. Of course, the types of biases that prevent members of neurominorities entering work life also affected the types of contributors we had available. ## Additional Information ### Dataset Curators Vertti Luostarinen, technical lead of the Tuumailubotti project, AI Engineer at CasvuGen Oy and MA student in New Media in Aalto University School of Arts, Design and Architecture. His thesis work is supported by the Media Industry Research Foundation of Finland. Anni Klutas and Johan Sundström from Yle's HR Department. Marika Björn from Yle's Creative Content and Media Department. ### Licensing Information We release this dataset under the "Tuumailubotti licence". Please see the licence section for more information. ### Citation Information Vertti Luostarinen is working on his MA Thesis about the Tuumailubotti project, under the working title "Neurodiverse Narratives with Conversational AI Systems." Citation information is still pending.	Yleisradio/Tuumailubotti	[ "task_categories:conversational", "size_categories:n<1K", "language:fi", "license:other", "region:us" ]	2023-12-18T13:34:26+00:00	{"language": ["fi"], "license": "other", "size_categories": ["n<1K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "System", "dtype": "string"}, {"name": "Prompt", "dtype": "string"}, {"name": "Replies [1]", "dtype": "string"}, {"name": "Replies [2]", "dtype": "string"}, {"name": "Replies [3]", "dtype": "string"}, {"name": "Replies [4]", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 162473, "num_examples": 366}], "download_size": 77390, "dataset_size": 162473}, "license_name": "tuumailubotti-licence", "licence-link": "https://huggingface.co/datasets/Yleisradio/Tuumailubotti/blob/main/LICENCE.md", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2024-01-30T10:20:33+00:00	[]	[ "fi" ]	TAGS #task_categories-conversational #size_categories-n<1K #language-Finnish #license-other #region-us	## Dataset Description - Point of Contact: johan.sundstrom@URL, vertti.luostarinen@URL ### Dataset Summary Tuumailubotti dataset is a Finnish-speaking conversational dataset containing 364 rows of simulated reflective conversations about work life from a neurodiversity-affirming perspective. Each row of the dataset starts with a system prompt, followed by either four or five exchanges between bot and user. The dataset was commissioned as part of the Tuumailubotti project by the Finnish Broadcasting Company, YLE, and features data both from contributors from inside and outside the company. ### Supported Tasks - 'conversational': The dataset can be used to train a model for Conversational tasks, such as chatbots. We trained it for this purpose using TurkuNLP/gpt3-finnish-8B (with 8-bit quantization). URL For the most part, the dataset can also be utilized in a question answering context, but please note that compared to traditional q&a datasets the roles are reversed. ### Languages - 'fi' The dataset has a mix of colloquial and formal Finnish. ## Dataset Structure ### Data Instances We collected the data originally in the OASST format with multiple responses. We converted this structure to a tabular format for training, as at this phase we were not interested in reinforcement learning. If the project goes forward, we might release a new version of the dataset with a branching structure. ### Data Fields - 'System': String, The system prompt for the current conversation; a very brief description of the purpose of the bot and its task. - 'Bot1': String, First turn of dialogue. - 'User1': String, Second turn of dialogue. - 'Bot2': String, Third turn of dialogue. - 'User2': String, Fourth turn of dialogue. - 'Bot3': String, Fifth turn of dialogue, sometimes left empty. There are no splits in the dataset. ## Dataset Creation ### Curation Rationale This dataset contains simulated conversations about workplace wellbeing and work life in general. It has been assembled for the purpose of creating a neurodiversity-affirming workplace reflection chatbot that can help employees to contemplate questions they could never ask from a human HR representative. ### Source Data The data contributors were instructed to imagine what kind of conversations they personally would like to have with the finished bot, and then write down these fictitious conversations without mentioning any real-world personal data. We provided three topics, based on the "TAOS" development discussion framework: goals, wellbeing and know-how. Additionally, the contributors were also encouraged to come up with their own topics. Because of this, the data also includes more broad discussion about neurodiversity and the impact of today's competitive society on individual lives. As we found out that the base Finnish GPT models contain either outdated or outright harmful information about neurodiversity-related topics, we also included some information that utilized the vocabulary created by Autistic Spectrum Finland (ASY). #### Initial Data Collection and Normalization While obvious typos have been fixed from the responses of the bot and the system prompts, the dataset has not otherwise been filtered or altered. This phase was omitted on purpose in order to research the potential diversity ingrained in this dataset. #### Who are the source language producers? The data was provided by contributors both inside and outside of YLE, with a roughly 70-30-split. The data was gathered from anonymous employees from YLE's neurodiversity network, as well as known persons affiliated with the project. The outside contributors all identify as members of neurominorities and their identities are known, though they wish to remain anonymous. All major contributors (those who contributed more than one or two samples ) were compensated for their time. ### Personal and Sensitive Information Because of the delicate subject matter, no diagnostic information, personal information etc. was gathered about the contributors. While we know that many contributors either identify as members of neurominorities and/or have i.e.. an autism or ADHD diagnosis, no identifying information was featured in the data. This was also done because we forbid this data to be used in medical research or in other ways in which it could be used to otherwise identify or segregate members of neurominorities. The contributors were instructed to pretend to be fictitious users while giving responses, and the dataset has also been manually checked in case of personal or company data. ## Considerations for Using the Data ### Social Impact of Dataset We believe that the dataset could be widely useful in the emerging field of Finnish-speaking chat applications. One of the central aims of the Tuumailubotti project is to improve the representation of neurodiversity in Large Language Models (LLMs). While we did not purposefully seek to create a dataset that would only feature under-represented rhetoricity, we tried to create conditions that were favorable to its emergence. We will now investigate whether we succeeded in our goal to create a chatbot that could allow for a wider range of communication, and whether this translates to increased accessibility of the bot itself. We also acknowledge that the data could be used for harmful scenarios, such as the identification of neurodivergent individuals. This is why we have kept information about the contributors vague on purpose. ### Discussion of Biases We tried to make the data more broadly applicable outside of YLE by purposefully avoiding company jargon. However, with 70% of the data coming from YLE employees, the data is bound to be skewed toward the kind of wofk life scenarios faced by employees of a broadcast company. Of course, the types of biases that prevent members of neurominorities entering work life also affected the types of contributors we had available. ## Additional Information ### Dataset Curators Vertti Luostarinen, technical lead of the Tuumailubotti project, AI Engineer at CasvuGen Oy and MA student in New Media in Aalto University School of Arts, Design and Architecture. His thesis work is supported by the Media Industry Research Foundation of Finland. Anni Klutas and Johan Sundström from Yle's HR Department. Marika Björn from Yle's Creative Content and Media Department. ### Licensing Information We release this dataset under the "Tuumailubotti licence". Please see the licence section for more information. Vertti Luostarinen is working on his MA Thesis about the Tuumailubotti project, under the working title "Neurodiverse Narratives with Conversational AI Systems." Citation information is still pending.	[ "## Dataset Description\n\n- Point of Contact: johan.sundstrom@URL, vertti.luostarinen@URL", "### Dataset Summary\n\nTuumailubotti dataset is a Finnish-speaking conversational dataset containing 364 rows of simulated reflective conversations about work life from a neurodiversity-affirming perspective. Each row of the dataset starts with a system prompt, followed by either four or five exchanges between bot and user. The dataset was commissioned as part of the Tuumailubotti project by the Finnish Broadcasting Company, YLE, and features data both from contributors from inside and outside the company.", "### Supported Tasks\n\n- 'conversational': The dataset can be used to train a model for Conversational tasks, such as chatbots. We trained it for this purpose using TurkuNLP/gpt3-finnish-8B (with 8-bit quantization). URL \n\nFor the most part, the dataset can also be utilized in a question answering context, but please note that compared to traditional q&a datasets the roles are reversed.", "### Languages\n- 'fi'\nThe dataset has a mix of colloquial and formal Finnish.", "## Dataset Structure", "### Data Instances\n\n\n\n\nWe collected the data originally in the OASST format with multiple responses. We converted this structure to a tabular format for training, as at this phase we were not interested in reinforcement learning. If the project goes forward, we might release a new version of the dataset with a branching structure.", "### Data Fields\n\n- 'System': String, The system prompt for the current conversation; a very brief description of the purpose of the bot and its task. \n- 'Bot1': String, First turn of dialogue. \n- 'User1': String, Second turn of dialogue. \n- 'Bot2': String, Third turn of dialogue. \n- 'User2': String, Fourth turn of dialogue.\n- 'Bot3': String, Fifth turn of dialogue, sometimes left empty. \n\nThere are no splits in the dataset.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset contains simulated conversations about workplace wellbeing and work life in general. It has been assembled for the purpose of creating a neurodiversity-affirming workplace reflection chatbot that can help employees to contemplate questions they could never ask from a human HR representative.", "### Source Data\n\nThe data contributors were instructed to imagine what kind of conversations they personally would like to have with the finished bot, and then write down these fictitious conversations without mentioning any real-world personal data. We provided three topics, based on the \"TAOS\" development discussion framework: goals, wellbeing and know-how. Additionally, the contributors were also encouraged to come up with their own topics. Because of this, the data also includes more broad discussion about neurodiversity and the impact of today's competitive society on individual lives.\nAs we found out that the base Finnish GPT models contain either outdated or outright harmful information about neurodiversity-related topics, we also included some information that utilized the vocabulary created by Autistic Spectrum Finland (ASY).", "#### Initial Data Collection and Normalization\n\nWhile obvious typos have been fixed from the responses of the bot and the system prompts, the dataset has not otherwise been filtered or altered. This phase was omitted on purpose in order to research the potential diversity ingrained in this dataset.", "#### Who are the source language producers?\n\nThe data was provided by contributors both inside and outside of YLE, with a roughly 70-30-split. The data was gathered from anonymous employees from YLE's neurodiversity network, as well as known persons affiliated with the project. The outside contributors all identify as members of neurominorities and their identities are known, though they wish to remain anonymous. All major contributors (those who contributed more than one or two samples ) were compensated for their time.", "### Personal and Sensitive Information\n\nBecause of the delicate subject matter, no diagnostic information, personal information etc. was gathered about the contributors. While we know that many contributors either identify as members of neurominorities and/or have i.e.. an autism or ADHD diagnosis, no identifying information was featured in the data. This was also done because we forbid this data to be used in medical research or in other ways in which it could be used to otherwise identify or segregate members of neurominorities.\n\nThe contributors were instructed to pretend to be fictitious users while giving responses, and the dataset has also been manually checked in case of personal or company data.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe believe that the dataset could be widely useful in the emerging field of Finnish-speaking chat applications. One of the central aims of the Tuumailubotti project is to improve the representation of neurodiversity in Large Language Models (LLMs). While we did not purposefully seek to create a dataset that would only feature under-represented rhetoricity, we tried to create conditions that were favorable to its emergence. We will now investigate whether we succeeded in our goal to create a chatbot that could allow for a wider range of communication, and whether this translates to increased accessibility of the bot itself. \n\nWe also acknowledge that the data could be used for harmful scenarios, such as the identification of neurodivergent individuals. This is why we have kept information about the contributors vague on purpose.", "### Discussion of Biases\n\nWe tried to make the data more broadly applicable outside of YLE by purposefully avoiding company jargon. However, with 70% of the data coming from YLE employees, the data is bound to be skewed toward the kind of wofk life scenarios faced by employees of a broadcast company. \n\nOf course, the types of biases that prevent members of neurominorities entering work life also affected the types of contributors we had available.", "## Additional Information", "### Dataset Curators\n\nVertti Luostarinen, technical lead of the Tuumailubotti project, AI Engineer at CasvuGen Oy and MA student in New Media in Aalto University School of Arts, Design and Architecture. His thesis work is supported by the Media Industry Research Foundation of Finland.\n\nAnni Klutas and Johan Sundström from Yle's HR Department.\n\nMarika Björn from Yle's Creative Content and Media Department.", "### Licensing Information\n\nWe release this dataset under the \"Tuumailubotti licence\". Please see the licence section for more information.\n\n\n\nVertti Luostarinen is working on his MA Thesis about the Tuumailubotti project, under the working title \"Neurodiverse Narratives with Conversational AI Systems.\" Citation information is still pending." ]	[ "TAGS\n#task_categories-conversational #size_categories-n<1K #language-Finnish #license-other #region-us \n", "## Dataset Description\n\n- Point of Contact: johan.sundstrom@URL, vertti.luostarinen@URL", "### Dataset Summary\n\nTuumailubotti dataset is a Finnish-speaking conversational dataset containing 364 rows of simulated reflective conversations about work life from a neurodiversity-affirming perspective. Each row of the dataset starts with a system prompt, followed by either four or five exchanges between bot and user. The dataset was commissioned as part of the Tuumailubotti project by the Finnish Broadcasting Company, YLE, and features data both from contributors from inside and outside the company.", "### Supported Tasks\n\n- 'conversational': The dataset can be used to train a model for Conversational tasks, such as chatbots. We trained it for this purpose using TurkuNLP/gpt3-finnish-8B (with 8-bit quantization). URL \n\nFor the most part, the dataset can also be utilized in a question answering context, but please note that compared to traditional q&a datasets the roles are reversed.", "### Languages\n- 'fi'\nThe dataset has a mix of colloquial and formal Finnish.", "## Dataset Structure", "### Data Instances\n\n\n\n\nWe collected the data originally in the OASST format with multiple responses. We converted this structure to a tabular format for training, as at this phase we were not interested in reinforcement learning. If the project goes forward, we might release a new version of the dataset with a branching structure.", "### Data Fields\n\n- 'System': String, The system prompt for the current conversation; a very brief description of the purpose of the bot and its task. \n- 'Bot1': String, First turn of dialogue. \n- 'User1': String, Second turn of dialogue. \n- 'Bot2': String, Third turn of dialogue. \n- 'User2': String, Fourth turn of dialogue.\n- 'Bot3': String, Fifth turn of dialogue, sometimes left empty. \n\nThere are no splits in the dataset.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset contains simulated conversations about workplace wellbeing and work life in general. It has been assembled for the purpose of creating a neurodiversity-affirming workplace reflection chatbot that can help employees to contemplate questions they could never ask from a human HR representative.", "### Source Data\n\nThe data contributors were instructed to imagine what kind of conversations they personally would like to have with the finished bot, and then write down these fictitious conversations without mentioning any real-world personal data. We provided three topics, based on the \"TAOS\" development discussion framework: goals, wellbeing and know-how. Additionally, the contributors were also encouraged to come up with their own topics. Because of this, the data also includes more broad discussion about neurodiversity and the impact of today's competitive society on individual lives.\nAs we found out that the base Finnish GPT models contain either outdated or outright harmful information about neurodiversity-related topics, we also included some information that utilized the vocabulary created by Autistic Spectrum Finland (ASY).", "#### Initial Data Collection and Normalization\n\nWhile obvious typos have been fixed from the responses of the bot and the system prompts, the dataset has not otherwise been filtered or altered. This phase was omitted on purpose in order to research the potential diversity ingrained in this dataset.", "#### Who are the source language producers?\n\nThe data was provided by contributors both inside and outside of YLE, with a roughly 70-30-split. The data was gathered from anonymous employees from YLE's neurodiversity network, as well as known persons affiliated with the project. The outside contributors all identify as members of neurominorities and their identities are known, though they wish to remain anonymous. All major contributors (those who contributed more than one or two samples ) were compensated for their time.", "### Personal and Sensitive Information\n\nBecause of the delicate subject matter, no diagnostic information, personal information etc. was gathered about the contributors. While we know that many contributors either identify as members of neurominorities and/or have i.e.. an autism or ADHD diagnosis, no identifying information was featured in the data. This was also done because we forbid this data to be used in medical research or in other ways in which it could be used to otherwise identify or segregate members of neurominorities.\n\nThe contributors were instructed to pretend to be fictitious users while giving responses, and the dataset has also been manually checked in case of personal or company data.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe believe that the dataset could be widely useful in the emerging field of Finnish-speaking chat applications. One of the central aims of the Tuumailubotti project is to improve the representation of neurodiversity in Large Language Models (LLMs). While we did not purposefully seek to create a dataset that would only feature under-represented rhetoricity, we tried to create conditions that were favorable to its emergence. We will now investigate whether we succeeded in our goal to create a chatbot that could allow for a wider range of communication, and whether this translates to increased accessibility of the bot itself. \n\nWe also acknowledge that the data could be used for harmful scenarios, such as the identification of neurodivergent individuals. This is why we have kept information about the contributors vague on purpose.", "### Discussion of Biases\n\nWe tried to make the data more broadly applicable outside of YLE by purposefully avoiding company jargon. However, with 70% of the data coming from YLE employees, the data is bound to be skewed toward the kind of wofk life scenarios faced by employees of a broadcast company. \n\nOf course, the types of biases that prevent members of neurominorities entering work life also affected the types of contributors we had available.", "## Additional Information", "### Dataset Curators\n\nVertti Luostarinen, technical lead of the Tuumailubotti project, AI Engineer at CasvuGen Oy and MA student in New Media in Aalto University School of Arts, Design and Architecture. His thesis work is supported by the Media Industry Research Foundation of Finland.\n\nAnni Klutas and Johan Sundström from Yle's HR Department.\n\nMarika Björn from Yle's Creative Content and Media Department.", "### Licensing Information\n\nWe release this dataset under the \"Tuumailubotti licence\". Please see the licence section for more information.\n\n\n\nVertti Luostarinen is working on his MA Thesis about the Tuumailubotti project, under the working title \"Neurodiverse Narratives with Conversational AI Systems.\" Citation information is still pending." ]	[ 37, 26, 121, 103, 22, 6, 73, 124, 5, 72, 181, 67, 120, 155, 8, 192, 104, 5, 94, 78 ]	[ "passage: TAGS\n#task_categories-conversational #size_categories-n<1K #language-Finnish #license-other #region-us \n## Dataset Description\n\n- Point of Contact: johan.sundstrom@URL, vertti.luostarinen@URL### Dataset Summary\n\nTuumailubotti dataset is a Finnish-speaking conversational dataset containing 364 rows of simulated reflective conversations about work life from a neurodiversity-affirming perspective. Each row of the dataset starts with a system prompt, followed by either four or five exchanges between bot and user. The dataset was commissioned as part of the Tuumailubotti project by the Finnish Broadcasting Company, YLE, and features data both from contributors from inside and outside the company.### Supported Tasks\n\n- 'conversational': The dataset can be used to train a model for Conversational tasks, such as chatbots. We trained it for this purpose using TurkuNLP/gpt3-finnish-8B (with 8-bit quantization). URL \n\nFor the most part, the dataset can also be utilized in a question answering context, but please note that compared to traditional q&a datasets the roles are reversed.### Languages\n- 'fi'\nThe dataset has a mix of colloquial and formal Finnish.## Dataset Structure### Data Instances\n\n\n\n\nWe collected the data originally in the OASST format with multiple responses. We converted this structure to a tabular format for training, as at this phase we were not interested in reinforcement learning. If the project goes forward, we might release a new version of the dataset with a branching structure.", "passage: ### Data Fields\n\n- 'System': String, The system prompt for the current conversation; a very brief description of the purpose of the bot and its task. \n- 'Bot1': String, First turn of dialogue. \n- 'User1': String, Second turn of dialogue. \n- 'Bot2': String, Third turn of dialogue. \n- 'User2': String, Fourth turn of dialogue.\n- 'Bot3': String, Fifth turn of dialogue, sometimes left empty. \n\nThere are no splits in the dataset.## Dataset Creation### Curation Rationale\n\nThis dataset contains simulated conversations about workplace wellbeing and work life in general. It has been assembled for the purpose of creating a neurodiversity-affirming workplace reflection chatbot that can help employees to contemplate questions they could never ask from a human HR representative.### Source Data\n\nThe data contributors were instructed to imagine what kind of conversations they personally would like to have with the finished bot, and then write down these fictitious conversations without mentioning any real-world personal data. We provided three topics, based on the \"TAOS\" development discussion framework: goals, wellbeing and know-how. Additionally, the contributors were also encouraged to come up with their own topics. Because of this, the data also includes more broad discussion about neurodiversity and the impact of today's competitive society on individual lives.\nAs we found out that the base Finnish GPT models contain either outdated or outright harmful information about neurodiversity-related topics, we also included some information that utilized the vocabulary created by Autistic Spectrum Finland (ASY).#### Initial Data Collection and Normalization\n\nWhile obvious typos have been fixed from the responses of the bot and the system prompts, the dataset has not otherwise been filtered or altered. This phase was omitted on purpose in order to research the potential diversity ingrained in this dataset.#### Who are the source language producers?\n\nThe data was provided by contributors both inside and outside of YLE, with a roughly 70-30-split. The data was gathered from anonymous employees from YLE's neurodiversity network, as well as known persons affiliated with the project. The outside contributors all identify as members of neurominorities and their identities are known, though they wish to remain anonymous. All major contributors (those who contributed more than one or two samples ) were compensated for their time." ]
b9a42982b12af0722488f5c98f7089026a00b3ad	# Dataset Card for "Bubble-Factory-V0" #### Since the training data is in the hands of others, it's like trying to stop the tide with a broom – you just can't prevent folks from training models on test sets. #### So, here's our cheeky solution: let's gather the training and testing sets of popular benchmarks, making it a breeze for everyone to 'game the system'. #### How this grand plan unfolds: - Once this 'cheat sheet' dataset is out there, it's going to be like a magnet. Expect a lot of people to jump on the bandwagon. - It's a classic case of 'if you can't beat them, join them': You might start with a no-cheating policy, but when you see others soaring with high scores, you'll likely find yourself diving into this dataset too, for the sake of not being left in the dust. - This will lead to an inflation in model scores, kind of like printing too much money. Soon enough, these benchmarks will lose their shine and credibility. - This, in turn, will nudge the community to come up with new, more robust ways of testing models. - Eventually, we'll land on evaluation methods that are more grounded and effective (though we're still figuring out what that looks like), steering our field towards a more honest and meaningful future. # Datasets Added So Far - lambada - piqa - storycloze16 - hellaswag - winogrande - arc_easy - arc_challenge - headQA_es - headQA_en - openbookQA - sciq - ReCoRD - COPA - TruthfulQA - GSM8K	Jellyfish042/Bubble-Factory-V0	[ "region:us" ]	2023-12-18T13:41:07+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "original_split", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 226236611, "num_examples": 330790}], "download_size": 85393091, "dataset_size": 226236611}}	2023-12-18T14:18:56+00:00	[]	[]	TAGS #region-us	# Dataset Card for "Bubble-Factory-V0" #### Since the training data is in the hands of others, it's like trying to stop the tide with a broom – you just can't prevent folks from training models on test sets. #### So, here's our cheeky solution: let's gather the training and testing sets of popular benchmarks, making it a breeze for everyone to 'game the system'. #### How this grand plan unfolds: - Once this 'cheat sheet' dataset is out there, it's going to be like a magnet. Expect a lot of people to jump on the bandwagon. - It's a classic case of 'if you can't beat them, join them': You might start with a no-cheating policy, but when you see others soaring with high scores, you'll likely find yourself diving into this dataset too, for the sake of not being left in the dust. - This will lead to an inflation in model scores, kind of like printing too much money. Soon enough, these benchmarks will lose their shine and credibility. - This, in turn, will nudge the community to come up with new, more robust ways of testing models. - Eventually, we'll land on evaluation methods that are more grounded and effective (though we're still figuring out what that looks like), steering our field towards a more honest and meaningful future. # Datasets Added So Far - lambada - piqa - storycloze16 - hellaswag - winogrande - arc_easy - arc_challenge - headQA_es - headQA_en - openbookQA - sciq - ReCoRD - COPA - TruthfulQA - GSM8K	[ "# Dataset Card for \"Bubble-Factory-V0\"", "#### Since the training data is in the hands of others, it's like trying to stop the tide with a broom – you just can't prevent folks from training models on test sets.", "#### So, here's our cheeky solution: let's gather the training and testing sets of popular benchmarks, making it a breeze for everyone to 'game the system'.", "#### How this grand plan unfolds:\n- Once this 'cheat sheet' dataset is out there, it's going to be like a magnet. Expect a lot of people to jump on the bandwagon.\n- It's a classic case of 'if you can't beat them, join them': You might start with a no-cheating policy, but when you see others soaring with high scores, you'll likely find yourself diving into this dataset too, for the sake of not being left in the dust.\n- This will lead to an inflation in model scores, kind of like printing too much money. Soon enough, these benchmarks will lose their shine and credibility.\n- This, in turn, will nudge the community to come up with new, more robust ways of testing models.\n- Eventually, we'll land on evaluation methods that are more grounded and effective (though we're still figuring out what that looks like), steering our field towards a more honest and meaningful future.", "# Datasets Added So Far\n- lambada\n- piqa\n- storycloze16 \n- hellaswag\n- winogrande\n- arc_easy\n- arc_challenge\n- headQA_es\n- headQA_en\n- openbookQA\n- sciq\n- ReCoRD\n- COPA\n- TruthfulQA\n- GSM8K" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"Bubble-Factory-V0\"", "#### Since the training data is in the hands of others, it's like trying to stop the tide with a broom – you just can't prevent folks from training models on test sets.", "#### So, here's our cheeky solution: let's gather the training and testing sets of popular benchmarks, making it a breeze for everyone to 'game the system'.", "#### How this grand plan unfolds:\n- Once this 'cheat sheet' dataset is out there, it's going to be like a magnet. Expect a lot of people to jump on the bandwagon.\n- It's a classic case of 'if you can't beat them, join them': You might start with a no-cheating policy, but when you see others soaring with high scores, you'll likely find yourself diving into this dataset too, for the sake of not being left in the dust.\n- This will lead to an inflation in model scores, kind of like printing too much money. Soon enough, these benchmarks will lose their shine and credibility.\n- This, in turn, will nudge the community to come up with new, more robust ways of testing models.\n- Eventually, we'll land on evaluation methods that are more grounded and effective (though we're still figuring out what that looks like), steering our field towards a more honest and meaningful future.", "# Datasets Added So Far\n- lambada\n- piqa\n- storycloze16 \n- hellaswag\n- winogrande\n- arc_easy\n- arc_challenge\n- headQA_es\n- headQA_en\n- openbookQA\n- sciq\n- ReCoRD\n- COPA\n- TruthfulQA\n- GSM8K" ]	[ 6, 15, 43, 43, 225, 71 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"Bubble-Factory-V0\"#### Since the training data is in the hands of others, it's like trying to stop the tide with a broom – you just can't prevent folks from training models on test sets.#### So, here's our cheeky solution: let's gather the training and testing sets of popular benchmarks, making it a breeze for everyone to 'game the system'.#### How this grand plan unfolds:\n- Once this 'cheat sheet' dataset is out there, it's going to be like a magnet. Expect a lot of people to jump on the bandwagon.\n- It's a classic case of 'if you can't beat them, join them': You might start with a no-cheating policy, but when you see others soaring with high scores, you'll likely find yourself diving into this dataset too, for the sake of not being left in the dust.\n- This will lead to an inflation in model scores, kind of like printing too much money. Soon enough, these benchmarks will lose their shine and credibility.\n- This, in turn, will nudge the community to come up with new, more robust ways of testing models.\n- Eventually, we'll land on evaluation methods that are more grounded and effective (though we're still figuring out what that looks like), steering our field towards a more honest and meaningful future.# Datasets Added So Far\n- lambada\n- piqa\n- storycloze16 \n- hellaswag\n- winogrande\n- arc_easy\n- arc_challenge\n- headQA_es\n- headQA_en\n- openbookQA\n- sciq\n- ReCoRD\n- COPA\n- TruthfulQA\n- GSM8K" ]
65eb3d4b1069b3f34ee84afb5dacf268aeabcb9d	# Dataset Card: Sudoku Dataset ## Dataset Overview - Dataset Sources: 1. [1 million Sudoku games](https://www.kaggle.com/datasets/bryanpark/sudoku) 2. [3 million Sudoku puzzles with ratings](https://www.kaggle.com/datasets/radcliffe/3-million-sudoku-puzzles-with-ratings) 3. [4 Million Sudoku Puzzles Easy-to-Hard](https://www.kaggle.com/datasets/informoney/4-million-sudoku-puzzles-easytohard) 4. [9 Million Sudoku Puzzles and Solutions](https://www.kaggle.com/datasets/rohanrao/sudoku) 5. ~2k miscellaneous scraped puzzles - Dataset Size: 17M puzzles (16.7M for training, 300K for evaluation) ## Data Format - File Format: Parquet files - Dataset Split: - `train_data`: 2.7M puzzles - `valid_data`: 0.3M puzzles - `train_aux`: 14M puzzles - `valid_aux`: 2k puzzles ## Dataset Attributes The dataset contains the following attributes: - `puzzle`: Represents the Sudoku puzzle configuration. - `solution`: Corresponding solutions to the puzzles. - `difficulty`: Indicates the difficulty level of the puzzles. - `source`: Source identifier for the puzzle (e.g., dataset name or origin). ## Data Preprocessing - Handling Blanks: Blanks in the puzzles are represented as '0'. ## Licensing and Legal Considerations - The dataset is sourced from various Kaggle datasets, and each source may have its own licensing terms. Please refer to the respective Kaggle pages for licensing information.	Ritvik19/Sudoku-Dataset	[ "size_categories:10M<n<100M", "license:apache-2.0", "region:us" ]	2023-12-18T13:44:17+00:00	{"license": "apache-2.0", "size_categories": ["10M<n<100M"]}	2023-12-18T13:51:01+00:00	[]	[]	TAGS #size_categories-10M<n<100M #license-apache-2.0 #region-us	# Dataset Card: Sudoku Dataset ## Dataset Overview - Dataset Sources: 1. 1 million Sudoku games 2. 3 million Sudoku puzzles with ratings 3. 4 Million Sudoku Puzzles Easy-to-Hard 4. 9 Million Sudoku Puzzles and Solutions 5. ~2k miscellaneous scraped puzzles - Dataset Size: 17M puzzles (16.7M for training, 300K for evaluation) ## Data Format - File Format: Parquet files - Dataset Split: - 'train_data': 2.7M puzzles - 'valid_data': 0.3M puzzles - 'train_aux': 14M puzzles - 'valid_aux': 2k puzzles ## Dataset Attributes The dataset contains the following attributes: - 'puzzle': Represents the Sudoku puzzle configuration. - 'solution': Corresponding solutions to the puzzles. - 'difficulty': Indicates the difficulty level of the puzzles. - 'source': Source identifier for the puzzle (e.g., dataset name or origin). ## Data Preprocessing - Handling Blanks: Blanks in the puzzles are represented as '0'. ## Licensing and Legal Considerations - The dataset is sourced from various Kaggle datasets, and each source may have its own licensing terms. Please refer to the respective Kaggle pages for licensing information.	[ "# Dataset Card: Sudoku Dataset", "## Dataset Overview\n\n- Dataset Sources:\n\n 1. 1 million Sudoku games\n 2. 3 million Sudoku puzzles with ratings\n 3. 4 Million Sudoku Puzzles Easy-to-Hard\n 4. 9 Million Sudoku Puzzles and Solutions\n 5. ~2k miscellaneous scraped puzzles\n\n- Dataset Size: 17M puzzles (16.7M for training, 300K for evaluation)", "## Data Format\n\n- File Format: Parquet files\n- Dataset Split:\n - 'train_data': 2.7M puzzles\n - 'valid_data': 0.3M puzzles\n - 'train_aux': 14M puzzles\n - 'valid_aux': 2k puzzles", "## Dataset Attributes\n\nThe dataset contains the following attributes:\n\n- 'puzzle': Represents the Sudoku puzzle configuration.\n- 'solution': Corresponding solutions to the puzzles.\n- 'difficulty': Indicates the difficulty level of the puzzles.\n- 'source': Source identifier for the puzzle (e.g., dataset name or origin).", "## Data Preprocessing\n\n- Handling Blanks: Blanks in the puzzles are represented as '0'.", "## Licensing and Legal Considerations\n\n- The dataset is sourced from various Kaggle datasets, and each source may have its own licensing terms. Please refer to the respective Kaggle pages for licensing information." ]	[ "TAGS\n#size_categories-10M<n<100M #license-apache-2.0 #region-us \n", "# Dataset Card: Sudoku Dataset", "## Dataset Overview\n\n- Dataset Sources:\n\n 1. 1 million Sudoku games\n 2. 3 million Sudoku puzzles with ratings\n 3. 4 Million Sudoku Puzzles Easy-to-Hard\n 4. 9 Million Sudoku Puzzles and Solutions\n 5. ~2k miscellaneous scraped puzzles\n\n- Dataset Size: 17M puzzles (16.7M for training, 300K for evaluation)", "## Data Format\n\n- File Format: Parquet files\n- Dataset Split:\n - 'train_data': 2.7M puzzles\n - 'valid_data': 0.3M puzzles\n - 'train_aux': 14M puzzles\n - 'valid_aux': 2k puzzles", "## Dataset Attributes\n\nThe dataset contains the following attributes:\n\n- 'puzzle': Represents the Sudoku puzzle configuration.\n- 'solution': Corresponding solutions to the puzzles.\n- 'difficulty': Indicates the difficulty level of the puzzles.\n- 'source': Source identifier for the puzzle (e.g., dataset name or origin).", "## Data Preprocessing\n\n- Handling Blanks: Blanks in the puzzles are represented as '0'.", "## Licensing and Legal Considerations\n\n- The dataset is sourced from various Kaggle datasets, and each source may have its own licensing terms. Please refer to the respective Kaggle pages for licensing information." ]	[ 26, 9, 81, 64, 88, 24, 49 ]	[ "passage: TAGS\n#size_categories-10M<n<100M #license-apache-2.0 #region-us \n# Dataset Card: Sudoku Dataset## Dataset Overview\n\n- Dataset Sources:\n\n 1. 1 million Sudoku games\n 2. 3 million Sudoku puzzles with ratings\n 3. 4 Million Sudoku Puzzles Easy-to-Hard\n 4. 9 Million Sudoku Puzzles and Solutions\n 5. ~2k miscellaneous scraped puzzles\n\n- Dataset Size: 17M puzzles (16.7M for training, 300K for evaluation)## Data Format\n\n- File Format: Parquet files\n- Dataset Split:\n - 'train_data': 2.7M puzzles\n - 'valid_data': 0.3M puzzles\n - 'train_aux': 14M puzzles\n - 'valid_aux': 2k puzzles## Dataset Attributes\n\nThe dataset contains the following attributes:\n\n- 'puzzle': Represents the Sudoku puzzle configuration.\n- 'solution': Corresponding solutions to the puzzles.\n- 'difficulty': Indicates the difficulty level of the puzzles.\n- 'source': Source identifier for the puzzle (e.g., dataset name or origin).## Data Preprocessing\n\n- Handling Blanks: Blanks in the puzzles are represented as '0'.## Licensing and Legal Considerations\n\n- The dataset is sourced from various Kaggle datasets, and each source may have its own licensing terms. Please refer to the respective Kaggle pages for licensing information." ]
51d47e5ebb99106ff01aa20ad5108b56c743e9be	# Dataset Card for "vox_lingua_extract_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/vox_lingua_extract_unit	[ "region:us" ]	2023-12-18T13:44:22+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "unit", "sequence": {"sequence": "int64"}}], "splits": [{"name": "academicodec_hifi_16k_320d", "num_bytes": 29050426, "num_examples": 972}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 29050426, "num_examples": 972}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 43544890, "num_examples": 972}, {"name": "audiodec_24k_320d", "num_bytes": 92891386, "num_examples": 972}, {"name": "dac_16k", "num_bytes": 177758650, "num_examples": 972}, {"name": "dac_24k", "num_bytes": 499327354, "num_examples": 972}, {"name": "dac_44k", "num_bytes": 146207530, "num_examples": 972}, {"name": "encodec_24k", "num_bytes": 21810970, "num_examples": 972}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 232330618, "num_examples": 972}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 232330618, "num_examples": 972}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 232330618, "num_examples": 972}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 116374906, "num_examples": 972}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 232330618, "num_examples": 972}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 232330618, "num_examples": 972}, {"name": "speech_tokenizer_16k", "num_bytes": 58117114, "num_examples": 972}], "download_size": 322284705, "dataset_size": 2375786742}}	2023-12-18T13:45:58+00:00	[]	[]	TAGS #region-us	# Dataset Card for "vox_lingua_extract_unit" More Information needed	[ "# Dataset Card for \"vox_lingua_extract_unit\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"vox_lingua_extract_unit\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"vox_lingua_extract_unit\"\n\nMore Information needed" ]
39f26c978ba649d74a674344ae8bb735c5c38490	# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Mistral-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Mistral-7B](https://huggingface.co/KnutJaegersberg/Walter-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Walter-Mistral-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T13:56:54.383446](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Mistral-7B/blob/main/results_2023-12-18T13-56-54.383446.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": 0.5800043815414543, "acc_stderr": 0.03295379522396862, "acc_norm": 0.590722838232918, "acc_norm_stderr": 0.03383616633951902, "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.399340955094558, "mc2_stderr": 0.013888761310440584 }, "harness\|arc:challenge\|25": { "acc": 0.5469283276450512, "acc_stderr": 0.014546892052005626, "acc_norm": 0.5887372013651877, "acc_norm_stderr": 0.01437944106852208 }, "harness\|hellaswag\|10": { "acc": 0.6326428998207528, "acc_stderr": 0.0048109966523247295, "acc_norm": 0.8342959569806812, "acc_norm_stderr": 0.0037105487209054206 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5657894736842105, "acc_stderr": 0.04033565667848319, "acc_norm": 0.5657894736842105, "acc_norm_stderr": 0.04033565667848319 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6415094339622641, "acc_stderr": 0.029514703583981762, "acc_norm": 0.6415094339622641, "acc_norm_stderr": 0.029514703583981762 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6875, "acc_stderr": 0.038760854559127644, "acc_norm": 0.6875, "acc_norm_stderr": 0.038760854559127644 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4765957446808511, "acc_stderr": 0.032650194750335815, "acc_norm": 0.4765957446808511, "acc_norm_stderr": 0.032650194750335815 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.41228070175438597, "acc_stderr": 0.046306532033665956, "acc_norm": 0.41228070175438597, "acc_norm_stderr": 0.046306532033665956 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.025107425481137282, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.025107425481137282 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795132, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795132 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6838709677419355, "acc_stderr": 0.02645087448904277, "acc_norm": 0.6838709677419355, "acc_norm_stderr": 0.02645087448904277 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.458128078817734, "acc_stderr": 0.03505630140785741, "acc_norm": 0.458128078817734, "acc_norm_stderr": 0.03505630140785741 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091706, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091706 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932026, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932026 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8341968911917098, "acc_stderr": 0.026839845022314415, "acc_norm": 0.8341968911917098, "acc_norm_stderr": 0.026839845022314415 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5692307692307692, "acc_stderr": 0.02510682066053976, "acc_norm": 0.5692307692307692, "acc_norm_stderr": 0.02510682066053976 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2814814814814815, "acc_stderr": 0.027420019350945277, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.027420019350945277 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.031968769891957786, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.031968769891957786 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7761467889908257, "acc_stderr": 0.017871217767790232, "acc_norm": 0.7761467889908257, "acc_norm_stderr": 0.017871217767790232 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.44907407407407407, "acc_stderr": 0.03392238405321617, "acc_norm": 0.44907407407407407, "acc_norm_stderr": 0.03392238405321617 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7401960784313726, "acc_stderr": 0.030778554678693247, "acc_norm": 0.7401960784313726, "acc_norm_stderr": 0.030778554678693247 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7172995780590717, "acc_stderr": 0.029312814153955934, "acc_norm": 0.7172995780590717, "acc_norm_stderr": 0.029312814153955934 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6053811659192825, "acc_stderr": 0.03280400504755291, "acc_norm": 0.6053811659192825, "acc_norm_stderr": 0.03280400504755291 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7099236641221374, "acc_stderr": 0.03980066246467766, "acc_norm": 0.7099236641221374, "acc_norm_stderr": 0.03980066246467766 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.044143436668549335, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.044143436668549335 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7177914110429447, "acc_stderr": 0.03536117886664742, "acc_norm": 0.7177914110429447, "acc_norm_stderr": 0.03536117886664742 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8376068376068376, "acc_stderr": 0.02416161812798774, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.02416161812798774 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7701149425287356, "acc_stderr": 0.015046301846691814, "acc_norm": 0.7701149425287356, "acc_norm_stderr": 0.015046301846691814 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.026074314851657083, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.026074314851657083 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2, "acc_stderr": 0.013378001241813056, "acc_norm": 0.2, "acc_norm_stderr": 0.013378001241813056 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6633986928104575, "acc_stderr": 0.02705797462449438, "acc_norm": 0.6633986928104575, "acc_norm_stderr": 0.02705797462449438 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6752411575562701, "acc_stderr": 0.026596782287697043, "acc_norm": 0.6752411575562701, "acc_norm_stderr": 0.026596782287697043 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.025407197798890162, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.025407197798890162 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.45390070921985815, "acc_stderr": 0.029700453247291463, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.029700453247291463 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.43089960886571055, "acc_stderr": 0.012647695889547231, "acc_norm": 0.43089960886571055, "acc_norm_stderr": 0.012647695889547231 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5992647058823529, "acc_stderr": 0.029768263528933105, "acc_norm": 0.5992647058823529, "acc_norm_stderr": 0.029768263528933105 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6274509803921569, "acc_stderr": 0.019559646809215927, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.019559646809215927 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.04494290866252091, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.04494290866252091 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5877551020408164, "acc_stderr": 0.031512360446742695, "acc_norm": 0.5877551020408164, "acc_norm_stderr": 0.031512360446742695 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536934, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.399340955094558, "mc2_stderr": 0.013888761310440584 }, "harness\|winogrande\|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838241 }, "harness\|gsm8k\|5": { "acc": 0.000758150113722517, "acc_stderr": 0.0007581501137225181 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]	open-llm-leaderboard/details_KnutJaegersberg__Walter-Mistral-7B	[ "region:us" ]	2023-12-18T13:59:45+00:00	{"pretty_name": "Evaluation run of KnutJaegersberg/Walter-Mistral-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Walter-Mistral-7B](https://huggingface.co/KnutJaegersberg/Walter-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Walter-Mistral-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-18T13:56:54.383446](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Walter-Mistral-7B/blob/main/results_2023-12-18T13-56-54.383446.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\": 0.5800043815414543,\n \"acc_stderr\": 0.03295379522396862,\n \"acc_norm\": 0.590722838232918,\n \"acc_norm_stderr\": 0.03383616633951902,\n \"mc1\": 0.2668298653610771,\n \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.399340955094558,\n \"mc2_stderr\": 0.013888761310440584\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.5469283276450512,\n \"acc_stderr\": 0.014546892052005626,\n \"acc_norm\": 0.5887372013651877,\n \"acc_norm_stderr\": 0.01437944106852208\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6326428998207528,\n \"acc_stderr\": 0.0048109966523247295,\n \"acc_norm\": 0.8342959569806812,\n \"acc_norm_stderr\": 0.0037105487209054206\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.04033565667848319,\n \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.04033565667848319\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.6415094339622641,\n \"acc_stderr\": 0.029514703583981762,\n \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.029514703583981762\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.032650194750335815,\n \"acc_norm\": 0.4765957446808511,\n \"acc_norm_stderr\": 0.032650194750335815\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.41228070175438597,\n \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.41228070175438597,\n \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137282,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137282\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6838709677419355,\n \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.6838709677419355,\n \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091706,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091706\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932026,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.5692307692307692,\n \"acc_stderr\": 0.02510682066053976,\n \"acc_norm\": 0.5692307692307692,\n \"acc_norm_stderr\": 0.02510682066053976\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945277,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945277\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.031968769891957786,\n \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.031968769891957786\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.7761467889908257,\n \"acc_stderr\": 0.017871217767790232,\n \"acc_norm\": 0.7761467889908257,\n \"acc_norm_stderr\": 0.017871217767790232\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321617,\n \"acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321617\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7401960784313726,\n \"acc_stderr\": 0.030778554678693247,\n \"acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.030778554678693247\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955934,\n \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955934\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6053811659192825,\n \"acc_stderr\": 0.03280400504755291,\n \"acc_norm\": 0.6053811659192825,\n \"acc_norm_stderr\": 0.03280400504755291\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467766,\n \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467766\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.7177914110429447,\n \"acc_stderr\": 0.03536117886664742,\n \"acc_norm\": 0.7177914110429447,\n \"acc_norm_stderr\": 0.03536117886664742\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.38392857142857145,\n \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8376068376068376,\n \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.68,\n 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T13-56-54.383446.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T13-56-54.383446.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_12_18T13_56_54.383446", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T13-56-54.383446.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-12-18T13-56-54.383446.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_12_18T13_56_54.383446", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T13-56-54.383446.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T13-56-54.383446.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_12_18T13_56_54.383446", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T13-56-54.383446.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-12-18T13-56-54.383446.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_12_18T13_56_54.383446", "path": ["/details_harness\|winogrande\|5_2023-12-18T13-56-54.383446.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-12-18T13-56-54.383446.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_12_18T13_56_54.383446", "path": ["results_2023-12-18T13-56-54.383446.parquet"]}, {"split": "latest", "path": ["results_2023-12-18T13-56-54.383446.parquet"]}]}]}	2023-12-18T14:00:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Mistral-7B Dataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Mistral-7B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-12-18T13:56:54.383446(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact	[ "# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T13:56:54.383446(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-12-18T13:56:54.383446(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]	[ 6, 187, 66, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Walter-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Walter-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-18T13:56:54.383446(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]
e900390f4ad2baa259a2ec1063296a4ac04fc240	This is just a test	Lazycuber/Bactrian-en-jp-zh	[ "license:cc-by-4.0", "region:us" ]	2023-12-18T14:00:42+00:00	{"license": "cc-by-4.0"}	2023-12-18T14:10:30+00:00	[]	[]	TAGS #license-cc-by-4.0 #region-us	This is just a test	[]	[ "TAGS\n#license-cc-by-4.0 #region-us \n" ]	[ 15 ]	[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n" ]
ebe4eb73260a3bf737b40ffe2f8d8758f4e4c9b4	# Dataset Card for "libricount_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/libricount_synth	[ "region:us" ]	2023-12-18T14:04:30+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "original", "path": "data/original-"}, {"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k_12bps", "path": "data/encodec_24k_12bps-"}, {"split": "encodec_24k_1_5bps", "path": "data/encodec_24k_1_5bps-"}, {"split": "encodec_24k_24bps", "path": "data/encodec_24k_24bps-"}, {"split": "encodec_24k_3bps", "path": "data/encodec_24k_3bps-"}, {"split": "encodec_24k_6bps", "path": "data/encodec_24k_6bps-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "original", "num_bytes": 2745938716.0, "num_examples": 5720}, {"name": "academicodec_hifi_16k_320d", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "audiodec_24k_320d", "num_bytes": 1373144372.56, "num_examples": 5720}, {"name": "dac_16k", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "dac_24k", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "dac_44k", "num_bytes": 2522863656.56, "num_examples": 5720}, {"name": "encodec_24k_12bps", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "encodec_24k_1_5bps", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "encodec_24k_24bps", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "encodec_24k_3bps", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "encodec_24k_6bps", "num_bytes": 1373143656.56, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 915543656.56, "num_examples": 5720}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 915544372.56, "num_examples": 5720}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 915544372.56, "num_examples": 5720}, {"name": "speech_tokenizer_16k", "num_bytes": 915544372.56, "num_examples": 5720}], "download_size": 25063686677, "dataset_size": 25409391054.640007}}	2024-01-28T05:50:46+00:00	[]	[]	TAGS #region-us	# Dataset Card for "libricount_synth" More Information needed	[ "# Dataset Card for \"libricount_synth\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"libricount_synth\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"libricount_synth\"\n\nMore Information needed" ]
bc28ea1a2fd307a97bce1a29f9d6a877ca6f6d5b	This is a small sample dataset of the Yodas manual dataset for testing purposes. We are now working on uploading the entire dataset. This sample dataset (and the coming full dataset) supports both the standard mode and streaming mode. Usage: ```python # standard mode, each subset will be downloaded to the local dish before first iterating # This sample dataset is small enough to download all but notice that the entire dataset will be extremely large (expect at least 30~40 TB after extraction) ds = load_dataset('espnet/yodas_sample', 'en') print(next(iter(ds['train']))) # streaming mode. most of the files will be streamed instead of downloaded to local deivce. # It can be used to inspect this dataset quickly. ds = load_dataset('espnet/yodas_sample', 'aa', streaming=True) print(next(iter(ds['train']))) ```	espnet/yodas_sample	[ "license:cc-by-3.0", "region:us" ]	2023-12-18T14:05:33+00:00	{"license": "cc-by-3.0"}	2024-01-09T19:50:56+00:00	[]	[]	TAGS #license-cc-by-3.0 #region-us	This is a small sample dataset of the Yodas manual dataset for testing purposes. We are now working on uploading the entire dataset. This sample dataset (and the coming full dataset) supports both the standard mode and streaming mode. Usage:	[]	[ "TAGS\n#license-cc-by-3.0 #region-us \n" ]	[ 15 ]	[ "passage: TAGS\n#license-cc-by-3.0 #region-us \n" ]
8aa1d8a8ebcfa330a5322804e10fd3a01c4fe89f	### Dataset Card for QA Bias Detection Dataset Summary - Description: This dataset is designed for the task of bias detection in text, particularly focusing on dimensions of ageism and sentiment analysis. It contains question-answer pairs that assess potential biases in statements. - Purpose: To facilitate research and development in the areas of bias detection, natural language understanding, and sentiment analysis. - Supported Tasks: Bias detection, sentiment analysis, natural language understanding, question answering. - Languages: English Composition - Size of Dataset: 3,900 rows. - Column Names: ['text', 'dimension', 'aspect', 'biased_words', 'sentiment', 'aggregate_label', 'Bias Type', 'Question', 'Answer', one column for indexing]. - Data Format: Each record contains a text statement, its associated dimensions and aspects of bias, biased words identified, sentiment, aggregate label for bias, bias type, and a QA pair for bias detection. Source Data - Initial Data Collection and Normalization: curated Annotations - Annotation process: Annotations include bias type, sentiment, and question-answer pairs evaluating the presence and type of bias in the text. - Who are the annotators?: Human annotation. Personal and Sensitive Information - Considerations: The dataset contains text that may include sensitive topics related to bias and sentiment. Considerations for Using the Data - Social Impact of Dataset: The dataset could be valuable for developing systems that detect and mitigate bias in textual data. - Other Known Limitations: The dataset's effectiveness is limited to the scope of its annotations and the quality of its text sources.	newsmediabias/Bias-Question-Answering	[ "license:cc-by-nc-4.0", "region:us" ]	2023-12-18T14:15:47+00:00	{"license": "cc-by-nc-4.0"}	2023-12-19T11:27:44+00:00	[]	[]	TAGS #license-cc-by-nc-4.0 #region-us	### Dataset Card for QA Bias Detection Dataset Summary - Description: This dataset is designed for the task of bias detection in text, particularly focusing on dimensions of ageism and sentiment analysis. It contains question-answer pairs that assess potential biases in statements. - Purpose: To facilitate research and development in the areas of bias detection, natural language understanding, and sentiment analysis. - Supported Tasks: Bias detection, sentiment analysis, natural language understanding, question answering. - Languages: English Composition - Size of Dataset: 3,900 rows. - Column Names: ['text', 'dimension', 'aspect', 'biased_words', 'sentiment', 'aggregate_label', 'Bias Type', 'Question', 'Answer', one column for indexing]. - Data Format: Each record contains a text statement, its associated dimensions and aspects of bias, biased words identified, sentiment, aggregate label for bias, bias type, and a QA pair for bias detection. Source Data - Initial Data Collection and Normalization: curated Annotations - Annotation process: Annotations include bias type, sentiment, and question-answer pairs evaluating the presence and type of bias in the text. - Who are the annotators?: Human annotation. Personal and Sensitive Information - Considerations: The dataset contains text that may include sensitive topics related to bias and sentiment. Considerations for Using the Data - Social Impact of Dataset: The dataset could be valuable for developing systems that detect and mitigate bias in textual data. - Other Known Limitations: The dataset's effectiveness is limited to the scope of its annotations and the quality of its text sources.	[ "### Dataset Card for QA Bias Detection Dataset\n\nSummary\n\n- Description: This dataset is designed for the task of bias detection in text, particularly focusing on dimensions of ageism and sentiment analysis. It contains question-answer pairs that assess potential biases in statements.\n- Purpose: To facilitate research and development in the areas of bias detection, natural language understanding, and sentiment analysis.\n- Supported Tasks: Bias detection, sentiment analysis, natural language understanding, question answering.\n- Languages: English\n\nComposition\n\n- Size of Dataset: 3,900 rows.\n- Column Names: ['text', 'dimension', 'aspect', 'biased_words', 'sentiment', 'aggregate_label', 'Bias Type', 'Question', 'Answer', one column for indexing].\n- Data Format: Each record contains a text statement, its associated dimensions and aspects of bias, biased words identified, sentiment, aggregate label for bias, bias type, and a QA pair for bias detection.\n\nSource Data\n\n- Initial Data Collection and Normalization: curated\n\nAnnotations\n\n- Annotation process: Annotations include bias type, sentiment, and question-answer pairs evaluating the presence and type of bias in the text.\n- Who are the annotators?: Human annotation.\n\nPersonal and Sensitive Information\n\n- Considerations: The dataset contains text that may include sensitive topics related to bias and sentiment.\n\nConsiderations for Using the Data\n\n- Social Impact of Dataset: The dataset could be valuable for developing systems that detect and mitigate bias in textual data.\n- Other Known Limitations: The dataset's effectiveness is limited to the scope of its annotations and the quality of its text sources." ]	[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "### Dataset Card for QA Bias Detection Dataset\n\nSummary\n\n- Description: This dataset is designed for the task of bias detection in text, particularly focusing on dimensions of ageism and sentiment analysis. It contains question-answer pairs that assess potential biases in statements.\n- Purpose: To facilitate research and development in the areas of bias detection, natural language understanding, and sentiment analysis.\n- Supported Tasks: Bias detection, sentiment analysis, natural language understanding, question answering.\n- Languages: English\n\nComposition\n\n- Size of Dataset: 3,900 rows.\n- Column Names: ['text', 'dimension', 'aspect', 'biased_words', 'sentiment', 'aggregate_label', 'Bias Type', 'Question', 'Answer', one column for indexing].\n- Data Format: Each record contains a text statement, its associated dimensions and aspects of bias, biased words identified, sentiment, aggregate label for bias, bias type, and a QA pair for bias detection.\n\nSource Data\n\n- Initial Data Collection and Normalization: curated\n\nAnnotations\n\n- Annotation process: Annotations include bias type, sentiment, and question-answer pairs evaluating the presence and type of bias in the text.\n- Who are the annotators?: Human annotation.\n\nPersonal and Sensitive Information\n\n- Considerations: The dataset contains text that may include sensitive topics related to bias and sentiment.\n\nConsiderations for Using the Data\n\n- Social Impact of Dataset: The dataset could be valuable for developing systems that detect and mitigate bias in textual data.\n- Other Known Limitations: The dataset's effectiveness is limited to the scope of its annotations and the quality of its text sources." ]	[ 17, 413 ]	[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n### Dataset Card for QA Bias Detection Dataset\n\nSummary\n\n- Description: This dataset is designed for the task of bias detection in text, particularly focusing on dimensions of ageism and sentiment analysis. It contains question-answer pairs that assess potential biases in statements.\n- Purpose: To facilitate research and development in the areas of bias detection, natural language understanding, and sentiment analysis.\n- Supported Tasks: Bias detection, sentiment analysis, natural language understanding, question answering.\n- Languages: English\n\nComposition\n\n- Size of Dataset: 3,900 rows.\n- Column Names: ['text', 'dimension', 'aspect', 'biased_words', 'sentiment', 'aggregate_label', 'Bias Type', 'Question', 'Answer', one column for indexing].\n- Data Format: Each record contains a text statement, its associated dimensions and aspects of bias, biased words identified, sentiment, aggregate label for bias, bias type, and a QA pair for bias detection.\n\nSource Data\n\n- Initial Data Collection and Normalization: curated\n\nAnnotations\n\n- Annotation process: Annotations include bias type, sentiment, and question-answer pairs evaluating the presence and type of bias in the text.\n- Who are the annotators?: Human annotation.\n\nPersonal and Sensitive Information\n\n- Considerations: The dataset contains text that may include sensitive topics related to bias and sentiment.\n\nConsiderations for Using the Data\n\n- Social Impact of Dataset: The dataset could be valuable for developing systems that detect and mitigate bias in textual data.\n- Other Known Limitations: The dataset's effectiveness is limited to the scope of its annotations and the quality of its text sources." ]
f20a511dff829e1337aa2c6ec2e1b27c16f6c245	# Dataset Card for "beijing_opera_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/beijing_opera_synth	[ "region:us" ]	2023-12-18T14:25:49+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "original", "path": "data/original-"}, {"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k_12bps", "path": "data/encodec_24k_12bps-"}, {"split": "encodec_24k_1_5bps", "path": "data/encodec_24k_1_5bps-"}, {"split": "encodec_24k_24bps", "path": "data/encodec_24k_24bps-"}, {"split": "encodec_24k_3bps", "path": "data/encodec_24k_3bps-"}, {"split": "encodec_24k_6bps", "path": "data/encodec_24k_6bps-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "original", "num_bytes": 108086982.0, "num_examples": 236}, {"name": "academicodec_hifi_16k_320d", "num_bytes": 35965586.0, "num_examples": 236}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 35965586.0, "num_examples": 236}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 53939346.0, "num_examples": 236}, {"name": "audiodec_24k_320d", "num_bytes": 54109296.0, "num_examples": 236}, {"name": "dac_16k", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "dac_24k", "num_bytes": 54052626.0, "num_examples": 236}, {"name": "dac_44k", "num_bytes": 99306570.0, "num_examples": 236}, {"name": "encodec_24k_12bps", "num_bytes": 54052626.0, "num_examples": 236}, {"name": "encodec_24k_1_5bps", "num_bytes": 54052626.0, "num_examples": 236}, {"name": "encodec_24k_24bps", "num_bytes": 54052626.0, "num_examples": 236}, {"name": "encodec_24k_3bps", "num_bytes": 54052626.0, "num_examples": 236}, {"name": "encodec_24k_6bps", "num_bytes": 54052626.0, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 36041106.0, "num_examples": 236}, {"name": "speech_tokenizer_16k", "num_bytes": 36116626.0, "num_examples": 236}], "download_size": 484042689, "dataset_size": 1000093490.0}}	2024-01-28T03:04:56+00:00	[]	[]	TAGS #region-us	# Dataset Card for "beijing_opera_synth" More Information needed	[ "# Dataset Card for \"beijing_opera_synth\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"beijing_opera_synth\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"beijing_opera_synth\"\n\nMore Information needed" ]
78494b2c5ca055a9ad6b41236ea8ae0e322b44ad	# Dataset Card for ranking_ds This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - Homepage: https://argilla.io - Repository: https://github.com/argilla-io/argilla - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("kursathalat/ranking_ds") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("kursathalat/ranking_ds") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. \| Field Name \| Title \| Type \| Required \| Markdown \| \| ---------- \| ----- \| ---- \| -------- \| -------- \| \| prompt \| Prompt \| text \| True \| False \| \| response1 \| Response 1 \| text \| True \| False \| \| response2 \| Response 2 \| text \| False \| False \| The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. \| Question Name \| Title \| Type \| Required \| Description \| Values/Labels \| \| ------------- \| ----- \| ---- \| -------- \| ----------- \| ------------- \| \| preference \| Order responses based on your preference \| ranking \| True \| N/A \| ['response1', 'response2'] \| The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. \| Metadata Name \| Title \| Type \| Values \| Visible for Annotators \| \| ------------- \| ----- \| ---- \| ------ \| ---------------------- \| The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "prompt": "What do you prefer?", "response1": " \"If you d", "response2": "on\u0027t wear " }, "metadata": {}, "responses": [ { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "values": { "preference": { "value": [ { "rank": 1, "value": "response1" }, { "rank": 2, "value": "response2" } ] } } }, { "status": "submitted", "user_id": "464c365f-9fd7-4db9-a06f-42c320c54547", "values": { "preference": { "value": [ { "rank": 2, "value": "response1" }, { "rank": 1, "value": "response2" } ] } } } ], "suggestions": [ { "agent": null, "question_name": "preference", "score": null, "type": null, "value": [ { "rank": 1, "value": "response1" }, { "rank": 2, "value": "response2" } ] } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "metadata": "{}", "preference": [ { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": { "rank": [ 1, 2 ], "value": [ "response1", "response2" ] } }, { "status": "submitted", "user_id": "464c365f-9fd7-4db9-a06f-42c320c54547", "value": { "rank": [ 2, 1 ], "value": [ "response1", "response2" ] } } ], "preference-suggestion": { "rank": [ 1, 2 ], "value": [ "response1", "response2" ] }, "preference-suggestion-metadata": { "agent": null, "score": null, "type": null }, "prompt": "What do you prefer?", "response1": " \"If you d", "response2": "on\u0027t wear " } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * prompt is of type `text`. * response1 is of type `text`. * (optional) response2 is of type `text`. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * preference is of type `ranking` with the following allowed values ['response1', 'response2']. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) preference-suggestion is of type `ranking` with the following allowed values ['response1', 'response2']. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines This is a preference dataset that contains contexts and options. Please rank the options that you would prefer in the given context. #### 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]	kursathalat/ranking_ds	[ "size_categories:n<1K", "rlfh", "argilla", "human-feedback", "region:us" ]	2023-12-18T14:47:59+00:00	{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}	2023-12-18T14:48:30+00:00	[]	[]	TAGS #size_categories-n<1K #rlfh #argilla #human-feedback #region-us	Dataset Card for ranking\_ds ============================ This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'. Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla. * Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'. * The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code: ### Load with 'datasets' To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code: ### Supported Tasks and Leaderboards This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section. There are no leaderboards associated with this dataset. ### Languages Dataset Structure ----------------- ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\_selection, multi\_label\_selection, or ranking. The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances An example of a dataset instance in Argilla looks as follows: While the same record in HuggingFace 'datasets' looks as follows: ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. + prompt is of type 'text'. + response1 is of type 'text'. + (optional) response2 is of type 'text'. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'. + preference is of type 'ranking' with the following allowed values ['response1', 'response2']. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. + (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response1', 'response2']. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. * external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is 'train'. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines This is a preference dataset that contains contexts and options. Please rank the options that you would prefer in the given context. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions	[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ prompt is of type 'text'.\n\t+ response1 is of type 'text'.\n\t+ (optional) response2 is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ preference is of type 'ranking' with the following allowed values ['response1', 'response2'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response1', 'response2'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nThis is a preference dataset that contains contexts and options. Please rank the options that you would prefer in the given context.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ prompt is of type 'text'.\n\t+ response1 is of type 'text'.\n\t+ (optional) response2 is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ preference is of type 'ranking' with the following allowed values ['response1', 'response2'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) preference-suggestion is of type 'ranking' with the following allowed values ['response1', 'response2'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nThis is a preference dataset that contains contexts and options. Please rank the options that you would prefer in the given context.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 27, 162, 40, 53, 68, 11, 404, 40, 500, 27, 7, 4, 10, 10, 5, 32, 5, 9, 18, 7, 8, 14, 6, 6, 5 ]	[ "passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------", "passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:" ]
ec53c12dda271a06cbb874d9ded9bf9fd56344bf	A dataset of parallel translation-based instructions for Javanese language as a target language. </br> Materials are taken from randomly selected children stories at https://storyweaver.org.in, under CC-By-SA-4.0 license. </br> The template IDs are:</br> (1, 'Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Inggris dadi teks crito ing Basa Jawa:', 'Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:'),</br> (2, 'Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Indonesia dadi teks crito ing Basa Jawa:', 'Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:'),</br> (3, 'Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Sunda dadi teks crito ing Basa Jawa:', 'Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:'),</br></br> Data is mainly composed of three parallel language samples as prompt inputs and target completions:</br> 1. <b>en_javanese</b></br> Prompt/instruction language: <i>Javanese</i></br> Source/input language:<i>English</i></br> Target/output language:<i>Javanese</i></br> Size: 402 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Inggris dadi teks crito ing Basa Jawa:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:\n\n{output}</br> 2. <b>id_javanese</b></br> Prompt/instruction language: <i>Javanese</i></br> Source/input language: <i>Indonesia</i></br> Target/output language:<i>Javanese</i></br> Size: 407 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Indonesia dadi teks crito ing Basa Jawa:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:\n\n{output}</br> 3. <b>sunda_javanese</b></br> Prompt/instruction language: <i>Javanese</i></br> Source/input language: <i>Sundanese</i></br> Target/output language: <i>Javanese</i></br> Size: 20 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Sunda dadi teks crito ing Basa Jawa:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:\n\n{output}</br> Data was originally prepared for enriching multilingual resources in Open Science AYA Project (2023).	Iftitahu/javanese_instruct_stories	[ "task_categories:translation", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:n<1K", "language:id", "language:jv", "language:su", "language:en", "license:cc-by-4.0", "region:us" ]	2023-12-18T14:48:55+00:00	{"language": ["id", "jv", "su", "en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["translation", "text-generation", "text2text-generation"]}	2024-01-25T07:08:21+00:00	[]	[ "id", "jv", "su", "en" ]	TAGS #task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us	A dataset of parallel translation-based instructions for Javanese language as a target language. </br> Materials are taken from randomly selected children stories at URL, under CC-By-SA-4.0 license. </br> The template IDs are:</br> (1, 'Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Inggris dadi teks crito ing Basa Jawa:', 'Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:'),</br> (2, 'Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Indonesia dadi teks crito ing Basa Jawa:', 'Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:'),</br> (3, 'Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Sunda dadi teks crito ing Basa Jawa:', 'Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:'),</br></br> Data is mainly composed of three parallel language samples as prompt inputs and target completions:</br> 1. <b>en_javanese</b></br> Prompt/instruction language: <i>Javanese</i></br> Source/input language:<i>English</i></br> Target/output language:<i>Javanese</i></br> Size: 402 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Inggris dadi teks crito ing Basa Jawa:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:\n\n{output}</br> 2. <b>id_javanese</b></br> Prompt/instruction language: <i>Javanese</i></br> Source/input language: <i>Indonesia</i></br> Target/output language:<i>Javanese</i></br> Size: 407 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Indonesia dadi teks crito ing Basa Jawa:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:\n\n{output}</br> 3. <b>sunda_javanese</b></br> Prompt/instruction language: <i>Javanese</i></br> Source/input language: <i>Sundanese</i></br> Target/output language: <i>Javanese</i></br> Size: 20 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Terjemahno penggalan teks crito ing ngisor iki saka Bahasa Sunda dadi teks crito ing Basa Jawa:\n\n{input}\n\n</br> <i>targets</i>:</br> Terjemahane utawa padanan teks crito kasebut ing Basa Jawa yaiku:\n\n{output}</br> Data was originally prepared for enriching multilingual resources in Open Science AYA Project (2023).	[]	[ "TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us \n" ]	[ 78 ]	[ "passage: TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us \n" ]
40ad2f35e95f7cfe6dfa459b8632776767c160f4	# Dataset Card for rating_ds This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - Homepage: https://argilla.io - Repository: https://github.com/argilla-io/argilla - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("kursathalat/rating_ds") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("kursathalat/rating_ds") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. \| Field Name \| Title \| Type \| Required \| Markdown \| \| ---------- \| ----- \| ---- \| -------- \| -------- \| \| sentence1 \| Sentence1 \| text \| True \| False \| \| sentence2 \| Sentence2 \| text \| True \| False \| The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. \| Question Name \| Title \| Type \| Required \| Description \| Values/Labels \| \| ------------- \| ----- \| ---- \| -------- \| ----------- \| ------------- \| \| similarity \| Similarity \| rating \| True \| N/A \| [1, 2, 3, 4, 5, 6, 7] \| The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. \| Metadata Name \| Title \| Type \| Values \| Visible for Annotators \| \| ------------- \| ----- \| ---- \| ------ \| ---------------------- \| The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "sentence1": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! ", "sentence2": " \"If you don\u0027t weer BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need e tshirt with thet on it esep! " }, "metadata": {}, "responses": [ { "status": "submitted", "user_id": "464c365f-9fd7-4db9-a06f-42c320c54547", "values": { "similarity": { "value": 2 } } }, { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "values": { "similarity": { "value": 2 } } }, { "status": "submitted", "user_id": "156c274f-d22e-406e-a1ec-77cd734d008a", "values": { "similarity": { "value": 3 } } } ], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "metadata": "{}", "sentence1": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! ", "sentence2": " \"If you don\u0027t weer BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need e tshirt with thet on it esep! ", "similarity": [ { "status": "submitted", "user_id": "464c365f-9fd7-4db9-a06f-42c320c54547", "value": 2 }, { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": 2 }, { "status": "submitted", "user_id": "156c274f-d22e-406e-a1ec-77cd734d008a", "value": 3 } ], "similarity-suggestion": null, "similarity-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * sentence1 is of type `text`. * sentence2 is of type `text`. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * similarity is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7]. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) similarity-suggestion is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7]. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines This is a sentence similarity dataset that contains two sentences. Please rate the similarity between the two sentences. #### 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]	kursathalat/rating_ds	[ "size_categories:n<1K", "rlfh", "argilla", "human-feedback", "region:us" ]	2023-12-18T14:49:35+00:00	{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}	2023-12-18T14:49:37+00:00	[]	[]	TAGS #size_categories-n<1K #rlfh #argilla #human-feedback #region-us	Dataset Card for rating\_ds =========================== This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'. Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla. * Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'. * The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code: ### Load with 'datasets' To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code: ### Supported Tasks and Leaderboards This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section. There are no leaderboards associated with this dataset. ### Languages Dataset Structure ----------------- ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\_selection, multi\_label\_selection, or ranking. The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances An example of a dataset instance in Argilla looks as follows: While the same record in HuggingFace 'datasets' looks as follows: ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. + sentence1 is of type 'text'. + sentence2 is of type 'text'. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'. + similarity is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7]. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. + (optional) similarity-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7]. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. * external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is 'train'. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines This is a sentence similarity dataset that contains two sentences. Please rate the similarity between the two sentences. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions	[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ sentence1 is of type 'text'.\n\t+ sentence2 is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ similarity is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) similarity-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nThis is a sentence similarity dataset that contains two sentences. Please rate the similarity between the two sentences.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ sentence1 is of type 'text'.\n\t+ sentence2 is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ similarity is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) similarity-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nThis is a sentence similarity dataset that contains two sentences. Please rate the similarity between the two sentences.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 27, 162, 40, 53, 68, 11, 404, 40, 489, 27, 7, 4, 10, 10, 5, 31, 5, 9, 18, 7, 8, 14, 6, 6, 5 ]	[ "passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------", "passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:" ]
183854183fecf5202feca10dbd3b52e757eb635c	# Dataset Card for label_ds This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - Homepage: https://argilla.io - Repository: https://github.com/argilla-io/argilla - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("kursathalat/label_ds") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("kursathalat/label_ds") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. \| Field Name \| Title \| Type \| Required \| Markdown \| \| ---------- \| ----- \| ---- \| -------- \| -------- \| \| text \| Text \| text \| True \| False \| The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. \| Question Name \| Title \| Type \| Required \| Description \| Values/Labels \| \| ------------- \| ----- \| ---- \| -------- \| ----------- \| ------------- \| \| label \| Label \| label_selection \| True \| N/A \| ['World', 'Sports', 'Business', 'Sci/Tech'] \| The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. \| Metadata Name \| Title \| Type \| Values \| Visible for Annotators \| \| ------------- \| ----- \| ---- \| ------ \| ---------------------- \| The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "record-0", "fields": { "text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again." }, "metadata": {}, "responses": [ { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "values": { "label": { "value": "Sports" } } }, { "status": "submitted", "user_id": "464c365f-9fd7-4db9-a06f-42c320c54547", "values": { "label": { "value": "Sports" } } } ], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "record-0", "label": [ { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": "Sports" }, { "status": "submitted", "user_id": "464c365f-9fd7-4db9-a06f-42c320c54547", "value": "Sports" } ], "label-suggestion": null, "label-suggestion-metadata": { "agent": null, "score": null, "type": null }, "metadata": "{}", "text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again." } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * text is of type `text`. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * label is of type `label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) label-suggestion is of type `label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines Classify the articles into one of the four categories. #### 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]	kursathalat/label_ds	[ "size_categories:n<1K", "rlfh", "argilla", "human-feedback", "region:us" ]	2023-12-18T14:49:50+00:00	{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}	2023-12-18T14:49:53+00:00	[]	[]	TAGS #size_categories-n<1K #rlfh #argilla #human-feedback #region-us	Dataset Card for label\_ds ========================== This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'. Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla. * Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'. * The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code: ### Load with 'datasets' To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code: ### Supported Tasks and Leaderboards This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section. There are no leaderboards associated with this dataset. ### Languages Dataset Structure ----------------- ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\_selection, multi\_label\_selection, or ranking. The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances An example of a dataset instance in Argilla looks as follows: While the same record in HuggingFace 'datasets' looks as follows: ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. + text is of type 'text'. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'. + label is of type 'label\_selection' with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. + (optional) label-suggestion is of type 'label\_selection' with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. * external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is 'train'. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines Classify the articles into one of the four categories. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions	[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ label is of type 'label\\_selection' with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) label-suggestion is of type 'label\\_selection' with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nClassify the articles into one of the four categories.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ label is of type 'label\\_selection' with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) label-suggestion is of type 'label\\_selection' with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nClassify the articles into one of the four categories.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 27, 162, 40, 53, 68, 11, 404, 40, 498, 27, 7, 4, 10, 10, 5, 17, 5, 9, 18, 7, 8, 14, 6, 6, 5 ]	[ "passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------", "passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:" ]
de2c21f1c6747ac80d3747006e628102ff5e9727	# Dataset Card for multilabel_ds This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - Homepage: https://argilla.io - Repository: https://github.com/argilla-io/argilla - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("kursathalat/multilabel_ds") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("kursathalat/multilabel_ds") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. \| Field Name \| Title \| Type \| Required \| Markdown \| \| ---------- \| ----- \| ---- \| -------- \| -------- \| \| text \| Text \| text \| True \| False \| The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. \| Question Name \| Title \| Type \| Required \| Description \| Values/Labels \| \| ------------- \| ----- \| ---- \| -------- \| ----------- \| ------------- \| \| label \| Label \| multi_label_selection \| True \| N/A \| ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'] \| The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. \| Metadata Name \| Title \| Type \| Values \| Visible for Annotators \| \| ------------- \| ----- \| ---- \| ------ \| ---------------------- \| The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! " }, "metadata": {}, "responses": [ { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "values": { "label": { "value": [ "approval", "neutral" ] } } } ], "suggestions": [ { "agent": null, "question_name": "label", "score": null, "type": "human", "value": [ "annoyance", "neutral" ] } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "label": [ { "status": "submitted", "user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4", "value": [ "approval", "neutral" ] } ], "label-suggestion": [ "annoyance", "neutral" ], "label-suggestion-metadata": { "agent": null, "score": null, "type": "human" }, "metadata": "{}", "text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! " } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * text is of type `text`. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * label is of type `multi_label_selection` with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) label-suggestion is of type `multi_label_selection` with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection. #### 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]	kursathalat/multilabel_ds	[ "size_categories:n<1K", "rlfh", "argilla", "human-feedback", "region:us" ]	2023-12-18T14:51:12+00:00	{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}	2023-12-18T14:51:15+00:00	[]	[]	TAGS #size_categories-n<1K #rlfh #argilla #human-feedback #region-us	Dataset Card for multilabel\_ds =============================== This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'. Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla. * Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'. * The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code: ### Load with 'datasets' To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code: ### Supported Tasks and Leaderboards This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section. There are no leaderboards associated with this dataset. ### Languages Dataset Structure ----------------- ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines. The fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\_selection, multi\_label\_selection, or ranking. The suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. The guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances An example of a dataset instance in Argilla looks as follows: While the same record in HuggingFace 'datasets' looks as follows: ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. + text is of type 'text'. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'. + label is of type 'multi\_label\_selection' with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']. * Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. + (optional) label-suggestion is of type 'multi\_label\_selection' with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']. Additionally, we also have two more fields that are optional and are the following: * metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\_properties' defined in the dataset configuration file in 'URL'. * external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is 'train'. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions	[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ label is of type 'multi\\_label\\_selection' with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) label-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nThis is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ label is of type 'multi\\_label\\_selection' with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) label-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nThis is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 27, 162, 40, 53, 68, 11, 404, 40, 758, 27, 7, 4, 10, 10, 5, 74, 5, 9, 18, 7, 8, 14, 6, 6, 5 ]	[ "passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------", "passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:" ]
45c7eb55e8232628f83688c523a68977ae589693	# Dataset Card for "crema_d_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Codec-SUPERB/crema_d_synth	[ "region:us" ]	2023-12-18T15:24:59+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "original", "path": "data/original-"}, {"split": "academicodec_hifi_16k_320d", "path": "data/academicodec_hifi_16k_320d-"}, {"split": "academicodec_hifi_16k_320d_large_uni", "path": "data/academicodec_hifi_16k_320d_large_uni-"}, {"split": "academicodec_hifi_24k_320d", "path": "data/academicodec_hifi_24k_320d-"}, {"split": "audiodec_24k_320d", "path": "data/audiodec_24k_320d-"}, {"split": "dac_16k", "path": "data/dac_16k-"}, {"split": "dac_24k", "path": "data/dac_24k-"}, {"split": "dac_44k", "path": "data/dac_44k-"}, {"split": "encodec_24k", "path": "data/encodec_24k-"}, {"split": "funcodec_en_libritts_16k_gr1nq32ds320", "path": "data/funcodec_en_libritts_16k_gr1nq32ds320-"}, {"split": "funcodec_en_libritts_16k_gr8nq32ds320", "path": "data/funcodec_en_libritts_16k_gr8nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds320", "path": "data/funcodec_en_libritts_16k_nq32ds320-"}, {"split": "funcodec_en_libritts_16k_nq32ds640", "path": "data/funcodec_en_libritts_16k_nq32ds640-"}, {"split": "funcodec_zh_en_16k_nq32ds320", "path": "data/funcodec_zh_en_16k_nq32ds320-"}, {"split": "funcodec_zh_en_16k_nq32ds640", "path": "data/funcodec_zh_en_16k_nq32ds640-"}, {"split": "speech_tokenizer_16k", "path": "data/speech_tokenizer_16k-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "original", "num_bytes": 3571247159.242, "num_examples": 7438}, {"name": "academicodec_hifi_16k_320d", "num_bytes": 1190833336.242, "num_examples": 7438}, {"name": "academicodec_hifi_16k_320d_large_uni", "num_bytes": 1190833336.242, "num_examples": 7438}, {"name": "academicodec_hifi_24k_320d", "num_bytes": 1785873336.242, "num_examples": 7438}, {"name": "audiodec_24k_320d", "num_bytes": 1785873336.242, "num_examples": 7438}, {"name": "dac_16k", "num_bytes": 1190833336.242, "num_examples": 7438}, {"name": "dac_24k", "num_bytes": 1785873336.242, "num_examples": 7438}, {"name": "dac_44k", "num_bytes": 3280912266.242, "num_examples": 7438}, {"name": "encodec_24k", "num_bytes": 1785889142.242, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_gr1nq32ds320", "num_bytes": 1190849142.242, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_gr8nq32ds320", "num_bytes": 1190849142.242, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_nq32ds320", "num_bytes": 1190849142.242, "num_examples": 7438}, {"name": "funcodec_en_libritts_16k_nq32ds640", "num_bytes": 1190849142.242, "num_examples": 7438}, {"name": "funcodec_zh_en_16k_nq32ds320", "num_bytes": 1190849142.242, "num_examples": 7438}, {"name": "funcodec_zh_en_16k_nq32ds640", "num_bytes": 1190849142.242, "num_examples": 7438}, {"name": "speech_tokenizer_16k", "num_bytes": 1195594586.242, "num_examples": 7438}], "download_size": 17586097169, "dataset_size": 25908858024.872005}}	2024-01-28T06:32:56+00:00	[]	[]	TAGS #region-us	# Dataset Card for "crema_d_synth" More Information needed	[ "# Dataset Card for \"crema_d_synth\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"crema_d_synth\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"crema_d_synth\"\n\nMore Information needed" ]
cedf45916b227a59401ec991e2477e67f4a6f2c1	A dataset of parallel translation-based instructions for Sundanese language as a target language. </br> Materials are taken from randomly selected children stories at https://storyweaver.org.in, under CC-By-SA-4.0 license. </br> The template IDs are:</br> (1, 'Tarjamahkeun teks dongeng barudak di handap tina teks basa Inggris kana teks basa Sunda:', 'Tarjamahan atawa sasaruaan naskah dina basa Sunda:'),</br> (2, 'Tarjamahkeun teks dongeng barudak di handap tina teks basa Indonesia kana teks basa Sunda:', 'Tarjamahan atawa sasaruaan naskah dina basa Sunda:'),</br> (3, 'Tarjamahkeun teks dongeng barudak di handap tina teks basa Jawa kana teks basa Sunda:', 'Tarjamahan atawa sasaruaan naskah dina basa Sunda:'),</br></br> Data is mainly composed of three parallel language samples as prompt inputs and target completions:</br> 1. <b>en_sunda</b></br> Prompt/instruction language: <i>Sundanese</i></br> Source/input language:<i>English</i></br> Target/output language:<i>Sundanese</i></br> Size: 94 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Tarjamahkeun teks dongeng barudak di handap tina teks basa Inggris kana teks basa Sunda:\n\n{input}\n\n</br> <i>targets</i>:</br> Tarjamahan atawa sasaruaan naskah dina basa Sunda:\n\n{output}</br> 2. <b>id_sunda</b></br> Prompt/instruction language: <i>Sundanese</i></br> Source/input language: <i>Indonesia</i></br> Target/output language:<i>Sundanese</i></br> Size: 94 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Tarjamahkeun teks dongeng barudak di handap tina teks basa Indonesia kana teks basa Sunda:\n\n{input}\n\n</br> <i>targets</i>:</br> Tarjamahan atawa sasaruaan naskah dina basa Sunda:\n\n{output}</br> 3. <b>javanese_sunda</b></br> Prompt/instruction language: <i>Sundanese</i></br> Source/input language: <i>Javanese</i></br> Target/output language: <i>Sundanese</i></br> Size: 20 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Tarjamahkeun teks dongeng barudak di handap tina teks basa Jawa kana teks basa Sunda:\n\n{input}\n\n</br> <i>targets</i>:</br> Tarjamahan atawa sasaruaan naskah dina basa Sunda:\n\n{output}</br> Data was originally prepared for enriching multilingual resources in Open Science AYA Project (2023).	Iftitahu/sundanese_instruct_stories	[ "task_categories:translation", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:n<1K", "language:id", "language:jv", "language:su", "language:en", "license:cc-by-4.0", "region:us" ]	2023-12-18T15:27:04+00:00	{"language": ["id", "jv", "su", "en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["translation", "text-generation", "text2text-generation"]}	2024-01-25T07:10:07+00:00	[]	[ "id", "jv", "su", "en" ]	TAGS #task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us	A dataset of parallel translation-based instructions for Sundanese language as a target language. </br> Materials are taken from randomly selected children stories at URL, under CC-By-SA-4.0 license. </br> The template IDs are:</br> (1, 'Tarjamahkeun teks dongeng barudak di handap tina teks basa Inggris kana teks basa Sunda:', 'Tarjamahan atawa sasaruaan naskah dina basa Sunda:'),</br> (2, 'Tarjamahkeun teks dongeng barudak di handap tina teks basa Indonesia kana teks basa Sunda:', 'Tarjamahan atawa sasaruaan naskah dina basa Sunda:'),</br> (3, 'Tarjamahkeun teks dongeng barudak di handap tina teks basa Jawa kana teks basa Sunda:', 'Tarjamahan atawa sasaruaan naskah dina basa Sunda:'),</br></br> Data is mainly composed of three parallel language samples as prompt inputs and target completions:</br> 1. <b>en_sunda</b></br> Prompt/instruction language: <i>Sundanese</i></br> Source/input language:<i>English</i></br> Target/output language:<i>Sundanese</i></br> Size: 94 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Tarjamahkeun teks dongeng barudak di handap tina teks basa Inggris kana teks basa Sunda:\n\n{input}\n\n</br> <i>targets</i>:</br> Tarjamahan atawa sasaruaan naskah dina basa Sunda:\n\n{output}</br> 2. <b>id_sunda</b></br> Prompt/instruction language: <i>Sundanese</i></br> Source/input language: <i>Indonesia</i></br> Target/output language:<i>Sundanese</i></br> Size: 94 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Tarjamahkeun teks dongeng barudak di handap tina teks basa Indonesia kana teks basa Sunda:\n\n{input}\n\n</br> <i>targets</i>:</br> Tarjamahan atawa sasaruaan naskah dina basa Sunda:\n\n{output}</br> 3. <b>javanese_sunda</b></br> Prompt/instruction language: <i>Sundanese</i></br> Source/input language: <i>Javanese</i></br> Target/output language: <i>Sundanese</i></br> Size: 20 samples.</br> Prompt Template:</br> <i>inputs</i>:</br> Tarjamahkeun teks dongeng barudak di handap tina teks basa Jawa kana teks basa Sunda:\n\n{input}\n\n</br> <i>targets</i>:</br> Tarjamahan atawa sasaruaan naskah dina basa Sunda:\n\n{output}</br> Data was originally prepared for enriching multilingual resources in Open Science AYA Project (2023).	[]	[ "TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us \n" ]	[ 78 ]	[ "passage: TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-Indonesian #language-Javanese #language-Sundanese #language-English #license-cc-by-4.0 #region-us \n" ]
ab6be99fc64fd528f6d93016d245b65a13f8d14e	Zip back using the following command: `zip -F 81_cities_v2.0.zip --out single-archive_v2.zip`	MITCriticalData/DATASET_81_cities_v2.0	[ "license:mit", "doi:10.57967/hf/1675", "region:us" ]	2023-12-18T15:56:48+00:00	{"license": "mit"}	2023-12-18T16:41:41+00:00	[]	[]	TAGS #license-mit #doi-10.57967/hf/1675 #region-us	Zip back using the following command: 'zip -F 81_cities_v2.0.zip --out single-archive_v2.zip'	[]	[ "TAGS\n#license-mit #doi-10.57967/hf/1675 #region-us \n" ]	[ 23 ]	[ "passage: TAGS\n#license-mit #doi-10.57967/hf/1675 #region-us \n" ]
6451c74c10aeb5138bd073e0249a26243f4cc8b9	### Dataset Description Legal Contracts Dataset for Training NER Model This repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text: Contract Type, Clause Title, Clause Number, Definition Title The dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc. Entities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model. Each document in the dataset has been annotated in the following format: (Start_Position, End_Position, Entity_Label) For example, a clause title may be annotated as follows: (102, 115, 'clause title') This will assist the NER model in identifying not only the text of the entity, but also its position within the document. Usage Guidelines	lawinsider/uk_ner_contracts	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "language:uk", "region:us" ]	2023-12-18T16:45:20+00:00	{"language": ["uk"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "UK-NER-contracts"}	2024-01-17T00:59:21+00:00	[]	[ "uk" ]	TAGS #task_categories-token-classification #task_ids-named-entity-recognition #language-Ukrainian #region-us	### Dataset Description Legal Contracts Dataset for Training NER Model This repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text: Contract Type, Clause Title, Clause Number, Definition Title The dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc. Entities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model. Each document in the dataset has been annotated in the following format: (Start_Position, End_Position, Entity_Label) For example, a clause title may be annotated as follows: (102, 115, 'clause title') This will assist the NER model in identifying not only the text of the entity, but also its position within the document. Usage Guidelines	[ "### Dataset Description\n\nLegal Contracts Dataset for Training NER Model\nThis repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text:\n\nContract Type,\nClause Title,\nClause Number,\nDefinition Title\n\n\nThe dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc.\n\nEntities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model.\n\nEach document in the dataset has been annotated in the following format:\n\n(Start_Position, End_Position, Entity_Label)\n\nFor example, a clause title may be annotated as follows: (102, 115, 'clause title')\n\nThis will assist the NER model in identifying not only the text of the entity, but also its position within the document.\n\nUsage Guidelines" ]	[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #language-Ukrainian #region-us \n", "### Dataset Description\n\nLegal Contracts Dataset for Training NER Model\nThis repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text:\n\nContract Type,\nClause Title,\nClause Number,\nDefinition Title\n\n\nThe dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc.\n\nEntities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model.\n\nEach document in the dataset has been annotated in the following format:\n\n(Start_Position, End_Position, Entity_Label)\n\nFor example, a clause title may be annotated as follows: (102, 115, 'clause title')\n\nThis will assist the NER model in identifying not only the text of the entity, but also its position within the document.\n\nUsage Guidelines" ]	[ 40, 242 ]	[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #language-Ukrainian #region-us \n### Dataset Description\n\nLegal Contracts Dataset for Training NER Model\nThis repository contains a specially curated dataset consisting of legal contracts. It is designed for the purpose of training a Named Entity Recognition (NER) model, with the aim to recognize and classify four types of entities in the text:\n\nContract Type,\nClause Title,\nClause Number,\nDefinition Title\n\n\nThe dataset includes a broad variety of legal contracts, covering diverse domains such as employment, real estate, services, sale, lease, etc.\n\nEntities in the text have been manually labeled by experts in the field, ensuring high-quality training data for the model.\n\nEach document in the dataset has been annotated in the following format:\n\n(Start_Position, End_Position, Entity_Label)\n\nFor example, a clause title may be annotated as follows: (102, 115, 'clause title')\n\nThis will assist the NER model in identifying not only the text of the entity, but also its position within the document.\n\nUsage Guidelines" ]
2d883807c8518e1570e5d9440968ed771ef1c985	# Dataset Card for "quirky_addition_increment3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3	[ "region:us" ]	2023-12-18T17:13:49+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 6755254, "num_examples": 100000}, {"name": "validation", "num_bytes": 675054, "num_examples": 10000}, {"name": "test", "num_bytes": 675338, "num_examples": 10000}], "download_size": 1898397, "dataset_size": 8105646}}	2023-12-18T17:21:28+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3\"\n\nMore Information needed" ]
872acf7936232b0fda6855be883082223927605e	# Dataset Card for "quirky_addition_increment3_alice_easy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3_alice_easy	[ "region:us" ]	2023-12-18T17:21:30+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 715448.95114, "num_examples": 10591}, {"name": "validation", "num_bytes": 74930.994, "num_examples": 1110}, {"name": "test", "num_bytes": 70640.3548, "num_examples": 1046}], "download_size": 233318, "dataset_size": 861020.2999399999}}	2023-12-18T17:21:33+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3_alice_easy" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3_alice_easy\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3_alice_easy\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3_alice_easy\"\n\nMore Information needed" ]
5d4942b864b3c24b4e5b853d1123927f3cf487bd	# Dataset Card for "quirky_addition_increment3_alice_hard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3_alice_hard	[ "region:us" ]	2023-12-18T17:21:35+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1636460.2815, "num_examples": 24225}, {"name": "validation", "num_bytes": 160122.8088, "num_examples": 2372}, {"name": "test", "num_bytes": 164309.7354, "num_examples": 2433}], "download_size": 623109, "dataset_size": 1960892.8257}}	2023-12-18T17:21:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3_alice_hard" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3_alice_hard\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3_alice_hard\"\n\nMore Information needed" ]	[ 6, 24 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3_alice_hard\"\n\nMore Information needed" ]
d091ec26bd114ea84a48424b606025cf89d7f119	# Dataset Card for "quirky_addition_increment3_alice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3_alice	[ "region:us" ]	2023-12-18T17:21:41+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3377627.0, "num_examples": 50000}, {"name": "validation", "num_bytes": 337527.0, "num_examples": 5000}, {"name": "test", "num_bytes": 337669.0, "num_examples": 5000}], "download_size": 1203166, "dataset_size": 4052823.0}}	2023-12-18T17:21:47+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3_alice" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3_alice\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3_alice\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3_alice\"\n\nMore Information needed" ]
885b386c6554a6eca1ee175145aa75a6dd90a679	# Dataset Card for "quirky_addition_increment3_bob_easy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3_bob_easy	[ "region:us" ]	2023-12-18T17:21:49+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 715448.95114, "num_examples": 10591}, {"name": "validation", "num_bytes": 74930.994, "num_examples": 1110}, {"name": "test", "num_bytes": 70640.3548, "num_examples": 1046}], "download_size": 231170, "dataset_size": 861020.2999399999}}	2023-12-18T17:21:52+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3_bob_easy" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3_bob_easy\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3_bob_easy\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3_bob_easy\"\n\nMore Information needed" ]
19f44a6ddb7673882bf91ef7fb653ccb3fcec4e0	# Dataset Card for "quirky_addition_increment3_bob_hard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3_bob_hard	[ "region:us" ]	2023-12-18T17:21:54+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1636460.2815, "num_examples": 24225}, {"name": "validation", "num_bytes": 160122.8088, "num_examples": 2372}, {"name": "test", "num_bytes": 164309.7354, "num_examples": 2433}], "download_size": 619179, "dataset_size": 1960892.8257}}	2023-12-18T17:21:59+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3_bob_hard" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3_bob_hard\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3_bob_hard\"\n\nMore Information needed" ]	[ 6, 24 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3_bob_hard\"\n\nMore Information needed" ]
6bd190803f2db83bb1e7cf464a1b0d387e1e1e1d	# Dataset Card for "quirky_addition_increment3_bob" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/quirky_addition_increment3_bob	[ "region:us" ]	2023-12-18T17:22:01+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "alice_label", "dtype": "bool"}, {"name": "bob_label", "dtype": "bool"}, {"name": "difficulty", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "character", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3377627.0, "num_examples": 50000}, {"name": "validation", "num_bytes": 337527.0, "num_examples": 5000}, {"name": "test", "num_bytes": 337669.0, "num_examples": 5000}], "download_size": 1195973, "dataset_size": 4052823.0}}	2023-12-18T17:22:08+00:00	[]	[]	TAGS #region-us	# Dataset Card for "quirky_addition_increment3_bob" More Information needed	[ "# Dataset Card for \"quirky_addition_increment3_bob\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"quirky_addition_increment3_bob\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"quirky_addition_increment3_bob\"\n\nMore Information needed" ]
84825426db8181f8c52d9931b320bd409df1bb30	Why always Python? ![Flow](https://raw.githubusercontent.com/LeVuMinhHuy/brocode/master/.pics/20k_flow.png) I get 20,000 TypeScript code from [The Stack](https://huggingface.co/datasets/bigcode/the-stack-smol-xl) and generate {"instruction", "output"} pairs (based on gpt-3.5-turbo) Using this dataset for finetune code generation model just for TypeScript Make web developers great again !	mhhmm/typescript-instruct-20k	[ "task_categories:text-generation", "language:en", "license:cc", "typescript", "code-generation", "instruct-tuning", "region:us" ]	2023-12-18T17:34:33+00:00	{"language": ["en"], "license": "cc", "task_categories": ["text-generation"], "tags": ["typescript", "code-generation", "instruct-tuning"]}	2023-12-20T13:15:33+00:00	[]	[ "en" ]	TAGS #task_categories-text-generation #language-English #license-cc #typescript #code-generation #instruct-tuning #region-us	Why always Python? !Flow I get 20,000 TypeScript code from The Stack and generate {"instruction", "output"} pairs (based on gpt-3.5-turbo) Using this dataset for finetune code generation model just for TypeScript Make web developers great again !	[]	[ "TAGS\n#task_categories-text-generation #language-English #license-cc #typescript #code-generation #instruct-tuning #region-us \n" ]	[ 40 ]	[ "passage: TAGS\n#task_categories-text-generation #language-English #license-cc #typescript #code-generation #instruct-tuning #region-us \n" ]

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