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# Dataset Card for "squad-v1.1-t5-question-generation" ## Dataset Description - **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/) - **Paper:** [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250) ### Dataset Summary This is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in [Transformer-based End-to-End Question Generation](https://arxiv.org/pdf/2005.01107v1.pdf) specifically for the T5 family of models. The prefix is `generate questions: ` so that the task can be unique to a trained model. Check out the generation notebook [here](https://nbviewer.org/urls/huggingface.co/datasets/derek-thomas/squad-v1.1-t5-question-generation/resolve/main/Squad_V1_Question_Generation.ipynb). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages ## Dataset Structure ### Data Instances #### plain_text An example of 'train' looks as follows. ``` { "context": "generate questions: This is a test context.", "question": "Is this a test? {sep_token} Is this another Test {sep_token}" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `context`: a `string` feature. - `question`: a `string` feature. ### Data Splits | name |train|validation| |----------|----:|---------:| |plain_text|18896| 2067| ### Citation Information ``` @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } ``` ### Contributions Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) and [Thomas Simonini](https://huggingface.co/ThomasSimonini) for adding this to the hub Check out: [How to contribute more](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Visitors [![Visitors](https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fderek-thomas%2Fsquad-v1.1-t5-question-generation&label=Visitors&countColor=%23263759)](https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fderek-thomas%2Fsquad-v1.1-t5-question-generation)
derek-thomas/squad-v1.1-t5-question-generation
[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|squad", "language:en", "license:cc-by-4.0", "questiongeneration", "question-generation", "text2text-generation", "arxiv:1606.05250", "arxiv:2005.01107", "region:us" ]
2023-02-08T12:10:34+00:00
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|squad"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Question Generation for T5 based on Squad V1.1", "dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "questions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20293805, "num_examples": 18896}, {"name": "validation", "num_bytes": 2376313, "num_examples": 2067}], "download_size": 12600387, "dataset_size": 22670118}, "tags": ["questiongeneration", "question-generation", "text2text-generation"]}
2023-03-09T13:50:46+00:00
[ "1606.05250", "2005.01107" ]
[ "en" ]
TAGS #task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|squad #language-English #license-cc-by-4.0 #questiongeneration #question-generation #text2text-generation #arxiv-1606.05250 #arxiv-2005.01107 #region-us
Dataset Card for "squad-v1.1-t5-question-generation" ==================================================== Dataset Description ------------------- * Homepage: URL * Paper: SQuAD: 100,000+ Questions for Machine Comprehension of Text ### Dataset Summary This is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in Transformer-based End-to-End Question Generation specifically for the T5 family of models. The prefix is 'generate questions: ' so that the task can be unique to a trained model. Check out the generation notebook here. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### plain\_text An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### plain\_text * 'context': a 'string' feature. * 'question': a 'string' feature. ### Data Splits ### Contributions Thanks to Derek Thomas and Thomas Simonini for adding this to the hub Check out: How to contribute more ### Visitors ![Visitors](URL
[ "### Dataset Summary\n\n\nThis is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in Transformer-based End-to-End Question Generation\nspecifically for the T5 family of models. The prefix is 'generate questions: ' so that the task can be unique to a trained model.\n\n\nCheck out the generation notebook here.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### plain\\_text\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.", "### Data Splits", "### Contributions\n\n\nThanks to Derek Thomas and Thomas Simonini for adding this to the hub\n\n\nCheck out: How to contribute more", "### Visitors\n\n\n![Visitors](URL" ]
[ "TAGS\n#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|squad #language-English #license-cc-by-4.0 #questiongeneration #question-generation #text2text-generation #arxiv-1606.05250 #arxiv-2005.01107 #region-us \n", "### Dataset Summary\n\n\nThis is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in Transformer-based End-to-End Question Generation\nspecifically for the T5 family of models. The prefix is 'generate questions: ' so that the task can be unique to a trained model.\n\n\nCheck out the generation notebook here.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### plain\\_text\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.", "### Data Splits", "### Contributions\n\n\nThanks to Derek Thomas and Thomas Simonini for adding this to the hub\n\n\nCheck out: How to contribute more", "### Visitors\n\n\n![Visitors](URL" ]
[ 123, 89, 10, 11, 6, 18, 17, 30, 5, 27, 12 ]
[ "passage: TAGS\n#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|squad #language-English #license-cc-by-4.0 #questiongeneration #question-generation #text2text-generation #arxiv-1606.05250 #arxiv-2005.01107 #region-us \n### Dataset Summary\n\n\nThis is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in Transformer-based End-to-End Question Generation\nspecifically for the T5 family of models. The prefix is 'generate questions: ' so that the task can be unique to a trained model.\n\n\nCheck out the generation notebook here.### Supported Tasks and Leaderboards### Languages\n\n\nDataset Structure\n-----------------### Data Instances#### plain\\_text\n\n\nAn example of 'train' looks as follows.### Data Fields\n\n\nThe data fields are the same among all splits.#### plain\\_text\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.### Data Splits### Contributions\n\n\nThanks to Derek Thomas and Thomas Simonini for adding this to the hub\n\n\nCheck out: How to contribute more### Visitors\n\n\n![Visitors](URL" ]
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2ccd94cb1d2ff2915c4a258ee612abb4e8bf8392
# Датасет прикольных ответов Датасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года. ## Данные JSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип. Это пример данных ```json { "data": [ {"message": "Дано: Архимед упал в говно.", "response": "Найти: Выталкивающую силу.", "type": "w"}, {"message": "Как дела?", "response": "Всё было нормально, пока Вася выёживаться не стал)", "type": "n"}, {"message": "Что ты можешь сказать о сне?", "response": "Я так долго тренировался спать что могу делать это с закрытыми глазами.", "type": "n"}, ... ] } ... ``` # Список типов сообщений-ответов: - "n" Нейтрально, без оскорбления - "a" Аггресивный/токсичный ответ - "w" Содержит не всегда приемлемые или оскорбительные слова. - "s" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат) - "p" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я) - "u" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)
MoyAI/Funniest-answers
[ "task_categories:conversational", "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:text-classification", "size_categories:n<1K", "language:ru", "region:us" ]
2023-02-08T12:15:42+00:00
{"language": ["ru"], "size_categories": ["n<1K"], "task_categories": ["conversational", "text-generation", "text2text-generation", "text-classification"], "pretty_name": "Funny-responses"}
2023-12-25T14:56:56+00:00
[]
[ "ru" ]
TAGS #task_categories-conversational #task_categories-text-generation #task_categories-text2text-generation #task_categories-text-classification #size_categories-n<1K #language-Russian #region-us
# Датасет прикольных ответов Датасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года. ## Данные JSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип. Это пример данных # Список типов сообщений-ответов: - "n" Нейтрально, без оскорбления - "a" Аггресивный/токсичный ответ - "w" Содержит не всегда приемлемые или оскорбительные слова. - "s" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат) - "p" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я) - "u" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)
[ "# Датасет прикольных ответов\nДатасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года.", "## Данные\nJSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип.\nЭто пример данных", "# Список типов сообщений-ответов:\n - \"n\" Нейтрально, без оскорбления\n - \"a\" Аггресивный/токсичный ответ\n - \"w\" Содержит не всегда приемлемые или оскорбительные слова.\n - \"s\" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат)\n - \"p\" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я)\n - \"u\" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)" ]
[ "TAGS\n#task_categories-conversational #task_categories-text-generation #task_categories-text2text-generation #task_categories-text-classification #size_categories-n<1K #language-Russian #region-us \n", "# Датасет прикольных ответов\nДатасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года.", "## Данные\nJSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип.\nЭто пример данных", "# Список типов сообщений-ответов:\n - \"n\" Нейтрально, без оскорбления\n - \"a\" Аггресивный/токсичный ответ\n - \"w\" Содержит не всегда приемлемые или оскорбительные слова.\n - \"s\" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат)\n - \"p\" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я)\n - \"u\" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)" ]
[ 66, 50, 27, 141 ]
[ "passage: TAGS\n#task_categories-conversational #task_categories-text-generation #task_categories-text2text-generation #task_categories-text-classification #size_categories-n<1K #language-Russian #region-us \n# Датасет прикольных ответов\nДатасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года.## Данные\nJSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип.\nЭто пример данных# Список типов сообщений-ответов:\n - \"n\" Нейтрально, без оскорбления\n - \"a\" Аггресивный/токсичный ответ\n - \"w\" Содержит не всегда приемлемые или оскорбительные слова.\n - \"s\" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат)\n - \"p\" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я)\n - \"u\" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)" ]
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e783a3a1d438ba4f3509fdab98a8cae5a9cbcc00
Images trained for my [phantom diffusion s3 the last 8](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8) series. Since they are all AI generated images that are public domain under the US law, I claim it is legal to redistribute them as public domain. However, they might have copyright in your/their original country. Still, many countries including Japan allow us to use them for training an AI under their copyrights law, and because all the artists here are from Japan, I assume it should be allowed to reuse it for training globally.
Phantom-Artist/phantom-diffusion-s3-the-last-8-dataset
[ "license:cc0-1.0", "region:us" ]
2023-02-08T12:21:23+00:00
{"license": "cc0-1.0"}
2023-02-08T12:27:04+00:00
[]
[]
TAGS #license-cc0-1.0 #region-us
Images trained for my phantom diffusion s3 the last 8 series. Since they are all AI generated images that are public domain under the US law, I claim it is legal to redistribute them as public domain. However, they might have copyright in your/their original country. Still, many countries including Japan allow us to use them for training an AI under their copyrights law, and because all the artists here are from Japan, I assume it should be allowed to reuse it for training globally.
[]
[ "TAGS\n#license-cc0-1.0 #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-cc0-1.0 #region-us \n" ]
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9707dde47e3c7d4c695de87947c2ce82dc931d9d
# Dataset Card for "spectro_caption_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gwkim22/spectro_caption_dataset
[ "region:us" ]
2023-02-08T12:27:11+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3156701860.889, "num_examples": 37561}], "download_size": 3296229917, "dataset_size": 3156701860.889}}
2023-02-08T12:33:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "spectro_caption_dataset" More Information needed
[ "# Dataset Card for \"spectro_caption_dataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"spectro_caption_dataset\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"spectro_caption_dataset\"\n\nMore Information needed" ]
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b3afdc02ba50189413a6c516c57f37ecc0e31452
# Dataset Card for "dummy_image_class_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
patrickvonplaten/dummy_image_class_data
[ "region:us" ]
2023-02-08T12:27:32+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "resize"}}}}], "splits": [{"name": "train", "num_bytes": 1947037.0, "num_examples": 20}], "download_size": 1947666, "dataset_size": 1947037.0}}
2023-02-08T12:27:39+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dummy_image_class_data" More Information needed
[ "# Dataset Card for \"dummy_image_class_data\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dummy_image_class_data\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dummy_image_class_data\"\n\nMore Information needed" ]
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7ae5524d1e3fcd852081b54049a96df186a7da7e
# Dataset Card for "dummy_image_class_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hf-internal-testing/dummy_image_class_data
[ "region:us" ]
2023-02-08T12:28:33+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "resize"}}}}], "splits": [{"name": "train", "num_bytes": 555953.0, "num_examples": 6}], "download_size": 556964, "dataset_size": 555953.0}}
2023-02-08T12:28:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dummy_image_class_data" More Information needed
[ "# Dataset Card for \"dummy_image_class_data\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dummy_image_class_data\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dummy_image_class_data\"\n\nMore Information needed" ]
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362fa6fccabdacaeb20fee2b13efedea61141b66
(almost) all aerial videos of Zelenograd until 2023. i dont have a rights of these videos, all of these were downloaded from youtube. if you an owner of some of these videos and dont want that it were there, please contact me [email protected]
4eJIoBek/Zelenograd-aerial-videos
[ "license:wtfpl", "region:us" ]
2023-02-08T12:34:30+00:00
{"license": "wtfpl"}
2023-11-24T21:17:58+00:00
[]
[]
TAGS #license-wtfpl #region-us
(almost) all aerial videos of Zelenograd until 2023. i dont have a rights of these videos, all of these were downloaded from youtube. if you an owner of some of these videos and dont want that it were there, please contact me 4eJIoBek2021@URL
[]
[ "TAGS\n#license-wtfpl #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-wtfpl #region-us \n" ]
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24428867e1ca937b2513fbb4105c8fbee4ec76d9
# Dataset Card for "fnite_tf_combo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
olly4/fnite_tf_combo
[ "region:us" ]
2023-02-08T12:36:11+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 397302067.52, "num_examples": 3104}], "download_size": 486452911, "dataset_size": 397302067.52}}
2023-02-08T16:10:05+00:00
[]
[]
TAGS #region-us
# Dataset Card for "fnite_tf_combo" More Information needed
[ "# Dataset Card for \"fnite_tf_combo\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"fnite_tf_combo\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"fnite_tf_combo\"\n\nMore Information needed" ]
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15127aa41c6f7d4462fcf085f142537a6a97d30d
# Dataset Card for "small-coco-wm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RIW/small-coco-wm
[ "region:us" ]
2023-02-08T13:26:46+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "error_message", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "original_width", "dtype": "int64"}, {"name": "original_height", "dtype": "int64"}, {"name": "exif", "dtype": "string"}, {"name": "sha256", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 881124899.597, "num_examples": 8879}, {"name": "test", "num_bytes": 1728419997.344, "num_examples": 19769}, {"name": "validation", "num_bytes": 854191310.724, "num_examples": 8836}], "download_size": 1933564702, "dataset_size": 3463736207.665}}
2023-02-11T15:59:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "small-coco-wm" More Information needed
[ "# Dataset Card for \"small-coco-wm\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"small-coco-wm\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"small-coco-wm\"\n\nMore Information needed" ]
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1222e093b5f32dc9f62fafd54af16d2077893442
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: ell-hol/mT5-OrangeSum * Dataset: orange_sum * Config: abstract * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@ell-hol](https://huggingface.co/ell-hol) for evaluating this model.
autoevaluate/autoeval-eval-orange_sum-abstract-68b9ca-3347592353
[ "autotrain", "evaluation", "region:us" ]
2023-02-08T14:10:05+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["orange_sum"], "eval_info": {"task": "summarization", "model": "ell-hol/mT5-OrangeSum", "metrics": ["bertscore", "sacrebleu"], "dataset_name": "orange_sum", "dataset_config": "abstract", "dataset_split": "validation", "col_mapping": {"text": "text", "target": "summary"}}}
2023-02-08T14:17:47+00:00
[]
[]
TAGS #autotrain #evaluation #region-us
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Summarization * Model: ell-hol/mT5-OrangeSum * Dataset: orange_sum * Config: abstract * Split: validation To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @ell-hol for evaluating this model.
[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ell-hol/mT5-OrangeSum\n* Dataset: orange_sum\n* Config: abstract\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ell-hol for evaluating this model." ]
[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ell-hol/mT5-OrangeSum\n* Dataset: orange_sum\n* Config: abstract\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ell-hol for evaluating this model." ]
[ 13, 89, 16 ]
[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ell-hol/mT5-OrangeSum\n* Dataset: orange_sum\n* Config: abstract\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ell-hol for evaluating this model." ]
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8fe9fc8d2e5ce77f7d859e2323c2d104b9eea724
# Dataset Card for Model Card Dataset Mentions ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
librarian-bots/model_card_dataset_mentions
[ "task_categories:text-classification", "size_categories:n<1K", "language:en", "license:mit", "model cards", "metadata", "region:us" ]
2023-02-08T14:45:16+00:00
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "pretty_name": "Model Card Dataset Mentions", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "dataset_mention", "1": "no_dataset_mention"}}}}], "splits": [{"name": "train", "num_bytes": 58112, "num_examples": 297}], "download_size": 19321, "dataset_size": 58112}, "tags": ["model cards", "metadata"]}
2023-06-30T14:09:18+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #size_categories-n<1K #language-English #license-mit #model cards #metadata #region-us
# Dataset Card for Model Card Dataset Mentions ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
[ "# Dataset Card for Model Card Dataset Mentions", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]
[ "TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #license-mit #model cards #metadata #region-us \n", "# Dataset Card for Model Card Dataset Mentions", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]
[ 42, 11, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]
[ "passage: TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #license-mit #model cards #metadata #region-us \n# Dataset Card for Model Card Dataset Mentions## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
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924631d5913eefc28ce772866b43884ad161d061
# Dataset Card for "instruction-pilot-outputs-sampling" This dataset contains model outputs generated from the human demonstrations provided in [`HuggingFaceH4/instruction-pilot-prompts`](https://huggingface.co/datasets/HuggingFaceH4/instruction-pilot-prompts). To convert each language model into a dialogue agent, we shortened [Anthropic's HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11/#file-hhh_prompt-txt) and prepended this to each sample provided to the models: ``` Below is a friendly conversation between a human and an AI assistant. \ The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. \ The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. \ It also tries to avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer. \ That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful. Human: {input} AI: ``` The reason to shorten the HHH prompt is because it is over 6,000 tokens long, which far exceeds the maximum context size of most open-source language models. For example, Flan-T5 only has a context window of 512 tokens. It is likely that better outputs could be produced for language models with larger context windows, where some dialogue examples can be included in the promopt. To generate diverse outputs from each models, we used nucleus sampling with `temperature=0` and `top_p=0.9` and set `max_new_tokens=100` (which is about the mean lenght of the Self-Instruct outputs). For each example, 8 generations were produced per model.
HuggingFaceH4/instruction-pilot-outputs-sampling
[ "region:us" ]
2023-02-08T15:02:22+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "outputs", "list": [{"name": "model", "dtype": "string"}, {"name": "outputs", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 1347447, "num_examples": 375}], "download_size": 430865, "dataset_size": 1347447}}
2023-02-09T21:04:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "instruction-pilot-outputs-sampling" This dataset contains model outputs generated from the human demonstrations provided in 'HuggingFaceH4/instruction-pilot-prompts'. To convert each language model into a dialogue agent, we shortened Anthropic's HHH prompt and prepended this to each sample provided to the models: The reason to shorten the HHH prompt is because it is over 6,000 tokens long, which far exceeds the maximum context size of most open-source language models. For example, Flan-T5 only has a context window of 512 tokens. It is likely that better outputs could be produced for language models with larger context windows, where some dialogue examples can be included in the promopt. To generate diverse outputs from each models, we used nucleus sampling with 'temperature=0' and 'top_p=0.9' and set 'max_new_tokens=100' (which is about the mean lenght of the Self-Instruct outputs). For each example, 8 generations were produced per model.
[ "# Dataset Card for \"instruction-pilot-outputs-sampling\"\n\nThis dataset contains model outputs generated from the human demonstrations provided in 'HuggingFaceH4/instruction-pilot-prompts'. \n\nTo convert each language model into a dialogue agent, we shortened Anthropic's HHH prompt and prepended this to each sample provided to the models:\n\n\n\nThe reason to shorten the HHH prompt is because it is over 6,000 tokens long, which far exceeds the maximum context size of most open-source language models. For example, Flan-T5 only has a context window of 512 tokens. It is likely that better outputs could be produced for language models with larger context windows, where some dialogue examples can be included in the promopt.\n\nTo generate diverse outputs from each models, we used nucleus sampling with 'temperature=0' and 'top_p=0.9' and set 'max_new_tokens=100' (which is about the mean lenght of the Self-Instruct outputs). For each example, 8 generations were produced per model." ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"instruction-pilot-outputs-sampling\"\n\nThis dataset contains model outputs generated from the human demonstrations provided in 'HuggingFaceH4/instruction-pilot-prompts'. \n\nTo convert each language model into a dialogue agent, we shortened Anthropic's HHH prompt and prepended this to each sample provided to the models:\n\n\n\nThe reason to shorten the HHH prompt is because it is over 6,000 tokens long, which far exceeds the maximum context size of most open-source language models. For example, Flan-T5 only has a context window of 512 tokens. It is likely that better outputs could be produced for language models with larger context windows, where some dialogue examples can be included in the promopt.\n\nTo generate diverse outputs from each models, we used nucleus sampling with 'temperature=0' and 'top_p=0.9' and set 'max_new_tokens=100' (which is about the mean lenght of the Self-Instruct outputs). For each example, 8 generations were produced per model." ]
[ 6, 243 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"instruction-pilot-outputs-sampling\"\n\nThis dataset contains model outputs generated from the human demonstrations provided in 'HuggingFaceH4/instruction-pilot-prompts'. \n\nTo convert each language model into a dialogue agent, we shortened Anthropic's HHH prompt and prepended this to each sample provided to the models:\n\n\n\nThe reason to shorten the HHH prompt is because it is over 6,000 tokens long, which far exceeds the maximum context size of most open-source language models. For example, Flan-T5 only has a context window of 512 tokens. It is likely that better outputs could be produced for language models with larger context windows, where some dialogue examples can be included in the promopt.\n\nTo generate diverse outputs from each models, we used nucleus sampling with 'temperature=0' and 'top_p=0.9' and set 'max_new_tokens=100' (which is about the mean lenght of the Self-Instruct outputs). For each example, 8 generations were produced per model." ]
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d9d27ef9f38357dee815d7f649a3bebcaf4ceea7
# Dataset Card for "davinci-pairwise-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/davinci-pairwise-tokenized
[ "region:us" ]
2023-02-08T15:04:52+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2530035200, "num_examples": 64759}, {"name": "test", "num_bytes": 36178476, "num_examples": 7195}], "download_size": 0, "dataset_size": 2566213676}}
2023-02-08T15:25:42+00:00
[]
[]
TAGS #region-us
# Dataset Card for "davinci-pairwise-tokenized" More Information needed
[ "# Dataset Card for \"davinci-pairwise-tokenized\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"davinci-pairwise-tokenized\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"davinci-pairwise-tokenized\"\n\nMore Information needed" ]
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e36cf31a987ea72d9ddb93a03876070770254809
# Dataset Card for "davinci-pairwise-medium" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/davinci-pairwise-medium
[ "region:us" ]
2023-02-08T15:28:28+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2530035200, "num_examples": 64759}, {"name": "test", "num_bytes": 36178476, "num_examples": 7195}], "download_size": 848422865, "dataset_size": 2566213676}}
2023-02-08T15:29:49+00:00
[]
[]
TAGS #region-us
# Dataset Card for "davinci-pairwise-medium" More Information needed
[ "# Dataset Card for \"davinci-pairwise-medium\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"davinci-pairwise-medium\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"davinci-pairwise-medium\"\n\nMore Information needed" ]
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1ed2d6af25bf41efcbb308d6ac8af7f5a6668d00
# Dataset Card for "IEMOCAP_Text" This dataset obtained from IEMOCAP dataset. For more information go to [IEMOCAP](https://sail.usc.edu/iemocap/) webpage. This dataset contains 5 most common classes includes angry, happy, excitement, neutral and sad. Based on articles in this field, we merge excitement and happy classes. Our dataset contaions 5531 utterances and it splits based on the sessions. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Zahra99/IEMOCAP_Text
[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "Emotion recognition", "Text classification", "region:us" ]
2023-02-08T15:48:21+00:00
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ang", "1": "hap", "2": "neu", "3": "sad"}}}}], "splits": [{"name": "session1", "num_bytes": 71932, "num_examples": 1085}, {"name": "session2", "num_bytes": 79012, "num_examples": 1023}, {"name": "session3", "num_bytes": 74980, "num_examples": 1151}, {"name": "session4", "num_bytes": 72622, "num_examples": 1031}, {"name": "session5", "num_bytes": 89524, "num_examples": 1241}], "download_size": 215486, "dataset_size": 388070}, "tags": ["Emotion recognition", "Text classification"]}
2023-02-12T13:02:36+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #size_categories-1K<n<10K #language-English #Emotion recognition #Text classification #region-us
# Dataset Card for "IEMOCAP_Text" This dataset obtained from IEMOCAP dataset. For more information go to IEMOCAP webpage. This dataset contains 5 most common classes includes angry, happy, excitement, neutral and sad. Based on articles in this field, we merge excitement and happy classes. Our dataset contaions 5531 utterances and it splits based on the sessions. More Information needed
[ "# Dataset Card for \"IEMOCAP_Text\"\n\nThis dataset obtained from IEMOCAP dataset. For more information go to IEMOCAP webpage.\nThis dataset contains 5 most common classes includes angry, happy, excitement, neutral and sad. Based on articles in this field, we merge excitement and happy classes.\nOur dataset contaions 5531 utterances and it splits based on the sessions.\n\nMore Information needed" ]
[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #Emotion recognition #Text classification #region-us \n", "# Dataset Card for \"IEMOCAP_Text\"\n\nThis dataset obtained from IEMOCAP dataset. For more information go to IEMOCAP webpage.\nThis dataset contains 5 most common classes includes angry, happy, excitement, neutral and sad. Based on articles in this field, we merge excitement and happy classes.\nOur dataset contaions 5531 utterances and it splits based on the sessions.\n\nMore Information needed" ]
[ 41, 93 ]
[ "passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #Emotion recognition #Text classification #region-us \n# Dataset Card for \"IEMOCAP_Text\"\n\nThis dataset obtained from IEMOCAP dataset. For more information go to IEMOCAP webpage.\nThis dataset contains 5 most common classes includes angry, happy, excitement, neutral and sad. Based on articles in this field, we merge excitement and happy classes.\nOur dataset contaions 5531 utterances and it splits based on the sessions.\n\nMore Information needed" ]
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4f8539a397ecc0d7185bf941bc1bb7238abc3648
# Dataset Card for "IEMOCAP_Audio" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Zahra99/IEMOCAP_Audio
[ "task_categories:audio-classification", "size_categories:1K<n<10K", "language:en", "region:us" ]
2023-02-08T15:58:57+00:00
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ang", "1": "hap", "2": "neu", "3": "sad"}}}}], "splits": [{"name": "session1", "num_bytes": 163119572.96, "num_examples": 1085}, {"name": "session2", "num_bytes": 152984371.952, "num_examples": 1023}, {"name": "session3", "num_bytes": 166040715.472, "num_examples": 1151}, {"name": "session4", "num_bytes": 144715722.648, "num_examples": 1031}, {"name": "session5", "num_bytes": 183486561.006, "num_examples": 1241}], "download_size": 174395249, "dataset_size": 810346944.0380001}}
2024-02-05T03:16:33+00:00
[]
[ "en" ]
TAGS #task_categories-audio-classification #size_categories-1K<n<10K #language-English #region-us
# Dataset Card for "IEMOCAP_Audio" More Information needed
[ "# Dataset Card for \"IEMOCAP_Audio\"\n\nMore Information needed" ]
[ "TAGS\n#task_categories-audio-classification #size_categories-1K<n<10K #language-English #region-us \n", "# Dataset Card for \"IEMOCAP_Audio\"\n\nMore Information needed" ]
[ 34, 16 ]
[ "passage: TAGS\n#task_categories-audio-classification #size_categories-1K<n<10K #language-English #region-us \n# Dataset Card for \"IEMOCAP_Audio\"\n\nMore Information needed" ]
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fac035351a76dc75ec5ae7a64984571fcd6179db
# Dataset Card for "Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_100
[ "region:us" ]
2023-02-08T16:16:00+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "raw_prediction", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_10", "num_bytes": 29959416.0, "num_examples": 100}], "download_size": 29638886, "dataset_size": 29959416.0}}
2023-02-08T16:16:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_100" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_100\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_100\"\n\nMore Information needed" ]
[ 6, 34 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_100\"\n\nMore Information needed" ]
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5128261682251c5eed78cab2076e0193c6df43e8
# Dataset Card for "Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_1000
[ "region:us" ]
2023-02-08T16:23:53+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "raw_prediction", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_10", "num_bytes": 366795867.0, "num_examples": 1000}, {"name": "fewshot_15", "num_bytes": 369012572.0, "num_examples": 1000}], "download_size": 727994919, "dataset_size": 735808439.0}}
2023-02-08T16:30:05+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
[ 6, 34 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_text_davinci_002_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
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1bd6a8e8f75924d1f5c3cbfb08513798ea13b0f9
# Dataset Card for "synthetic-instruct-gptj-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/synthetic-instruct-gptj-pairwise
[ "region:us" ]
2023-02-08T16:39:22+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30204558, "num_examples": 31486}, {"name": "test", "num_bytes": 1604422, "num_examples": 1657}], "download_size": 18522018, "dataset_size": 31808980}}
2023-02-08T21:26:09+00:00
[]
[]
TAGS #region-us
# Dataset Card for "synthetic-instruct-gptj-pairwise" More Information needed
[ "# Dataset Card for \"synthetic-instruct-gptj-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"synthetic-instruct-gptj-pairwise\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"synthetic-instruct-gptj-pairwise\"\n\nMore Information needed" ]
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9fa2d9f500efd41ec609363e4bffb9b5f1e9a4ba
# Dataset Card for "hh-rlhf-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/hh-rlhf-pairwise
[ "region:us" ]
2023-02-08T16:49:45+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 88512105, "num_examples": 76256}, {"name": "test", "num_bytes": 5957760, "num_examples": 5103}], "download_size": 56768654, "dataset_size": 94469865}}
2023-02-08T21:30:13+00:00
[]
[]
TAGS #region-us
# Dataset Card for "hh-rlhf-pairwise" More Information needed
[ "# Dataset Card for \"hh-rlhf-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"hh-rlhf-pairwise\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"hh-rlhf-pairwise\"\n\nMore Information needed" ]
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2805a44143714c0461b0d8d7ece4c630d1a308f8
# Dataset Card for DDisco ## Dataset Description The DDisco dataset is a dataset which can be used to train models to classify levels of coherence in _danish_ discourse. Each entry in the dataset is annotated with a discourse coherence label (rating from 1 to 3): 1: low coherence (difficult to understand, unorganized, contained unnecessary details and can not be summarized briefly and easily) 2: medium coherence 3: high coherence (easy to understand, well organized, only contain details that support the main point and can be summarized briefly and easily). Grammatical and typing errors are ignored (i.e. they do not affect the coherency score) and the coherence of a text is considered within its own domain. ### Additional Information [DDisCo: A Discourse Coherence Dataset for Danish](https://aclanthology.org/2022.lrec-1.260.pdf) ### Contributions [@ajders](https://github.com/ajders)
alexandrainst/ddisco
[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:da", "license:afl-3.0", "discourse", "coherence", "region:us" ]
2023-02-08T18:05:24+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["da"], "license": ["afl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "DDisco", "tags": ["discourse", "coherence"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "rating", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 815571, "num_examples": 801}, {"name": "test", "num_bytes": 209297, "num_examples": 201}], "download_size": 672202, "dataset_size": 1024868}}
2023-02-08T18:12:26+00:00
[]
[ "da" ]
TAGS #task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Danish #license-afl-3.0 #discourse #coherence #region-us
# Dataset Card for DDisco ## Dataset Description The DDisco dataset is a dataset which can be used to train models to classify levels of coherence in _danish_ discourse. Each entry in the dataset is annotated with a discourse coherence label (rating from 1 to 3): 1: low coherence (difficult to understand, unorganized, contained unnecessary details and can not be summarized briefly and easily) 2: medium coherence 3: high coherence (easy to understand, well organized, only contain details that support the main point and can be summarized briefly and easily). Grammatical and typing errors are ignored (i.e. they do not affect the coherency score) and the coherence of a text is considered within its own domain. ### Additional Information DDisCo: A Discourse Coherence Dataset for Danish ### Contributions @ajders
[ "# Dataset Card for DDisco", "## Dataset Description\n\nThe DDisco dataset is a dataset which can be used to train models to classify levels of coherence in _danish_ discourse. Each entry in the dataset is annotated with a discourse coherence label (rating from 1 to 3):\n\n1: low coherence (difficult to understand, unorganized, contained unnecessary details and can not be summarized briefly and easily)\n2: medium coherence\n3: high coherence (easy to understand, well organized, only contain details that support the main point and can be summarized briefly and easily).\nGrammatical and typing errors are ignored (i.e. they do not affect the coherency score) and the coherence of a text is considered within its own domain.", "### Additional Information\n\nDDisCo: A Discourse Coherence Dataset for Danish", "### Contributions\n\n@ajders" ]
[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Danish #license-afl-3.0 #discourse #coherence #region-us \n", "# Dataset Card for DDisco", "## Dataset Description\n\nThe DDisco dataset is a dataset which can be used to train models to classify levels of coherence in _danish_ discourse. Each entry in the dataset is annotated with a discourse coherence label (rating from 1 to 3):\n\n1: low coherence (difficult to understand, unorganized, contained unnecessary details and can not be summarized briefly and easily)\n2: medium coherence\n3: high coherence (easy to understand, well organized, only contain details that support the main point and can be summarized briefly and easily).\nGrammatical and typing errors are ignored (i.e. they do not affect the coherency score) and the coherence of a text is considered within its own domain.", "### Additional Information\n\nDDisCo: A Discourse Coherence Dataset for Danish", "### Contributions\n\n@ajders" ]
[ 82, 8, 178, 21, 8 ]
[ "passage: TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Danish #license-afl-3.0 #discourse #coherence #region-us \n# Dataset Card for DDisco## Dataset Description\n\nThe DDisco dataset is a dataset which can be used to train models to classify levels of coherence in _danish_ discourse. Each entry in the dataset is annotated with a discourse coherence label (rating from 1 to 3):\n\n1: low coherence (difficult to understand, unorganized, contained unnecessary details and can not be summarized briefly and easily)\n2: medium coherence\n3: high coherence (easy to understand, well organized, only contain details that support the main point and can be summarized briefly and easily).\nGrammatical and typing errors are ignored (i.e. they do not affect the coherency score) and the coherence of a text is considered within its own domain.### Additional Information\n\nDDisCo: A Discourse Coherence Dataset for Danish### Contributions\n\n@ajders" ]
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2537b681735c256a7ab829666b118e03c3eb9e0a
# IMDB Movie Reviews ![movie_reivews](images/movie_reviews.jpg) This is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks. The dataset is downloaded from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz This data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews. Equal distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels. ### Citation Information ``` @InProceedings{maas-EtAl:2011:ACL-HLT2011, author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, title = {Learning Word Vectors for Sentiment Analysis}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, month = {June}, year = {2011}, address = {Portland, Oregon, USA}, publisher = {Association for Computational Linguistics}, pages = {142--150}, url = {http://www.aclweb.org/anthology/P11-1015} } ```
ajaykarthick/imdb-movie-reviews
[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:feature-extraction", "size_categories:10K<n<100K", "region:us" ]
2023-02-08T18:30:11+00:00
{"size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "token-classification", "feature-extraction"], "pretty_name": "Movie-Reviews"}
2023-02-08T21:08:35+00:00
[]
[]
TAGS #task_categories-text-classification #task_categories-token-classification #task_categories-feature-extraction #size_categories-10K<n<100K #region-us
# IMDB Movie Reviews !movie_reivews This is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks. The dataset is downloaded from URL This data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews. Equal distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels.
[ "# IMDB Movie Reviews \n\n!movie_reivews\n\nThis is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks. \n\nThe dataset is downloaded from \n\nURL\n\nThis data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews.\n\nEqual distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels." ]
[ "TAGS\n#task_categories-text-classification #task_categories-token-classification #task_categories-feature-extraction #size_categories-10K<n<100K #region-us \n", "# IMDB Movie Reviews \n\n!movie_reivews\n\nThis is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks. \n\nThe dataset is downloaded from \n\nURL\n\nThis data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews.\n\nEqual distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels." ]
[ 53, 147 ]
[ "passage: TAGS\n#task_categories-text-classification #task_categories-token-classification #task_categories-feature-extraction #size_categories-10K<n<100K #region-us \n# IMDB Movie Reviews \n\n!movie_reivews\n\nThis is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks. \n\nThe dataset is downloaded from \n\nURL\n\nThis data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews.\n\nEqual distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels." ]
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b987397400508e890b7209226da81c693dbb9306
# Dataset Card for "big-animal-dataset" Hi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Isamu136/big-animal-dataset
[ "region:us" ]
2023-02-08T18:32:58+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1198940745.5549998, "num_examples": 62149}], "download_size": 0, "dataset_size": 1198940745.5549998}}
2023-02-08T21:02:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "big-animal-dataset" Hi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset. More Information needed
[ "# Dataset Card for \"big-animal-dataset\"\n\nHi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset.\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"big-animal-dataset\"\n\nHi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset.\n\nMore Information needed" ]
[ 6, 58 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"big-animal-dataset\"\n\nHi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset.\n\nMore Information needed" ]
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9a8c6e7338ecc5f9a6b51e664194456dbb0eb8b2
# Dataset Card for "wikipedia-20220301.en-block-size-1024" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
carlosejimenez/wikipedia-20220301.en-block-size-1024
[ "region:us" ]
2023-02-08T19:44:24+00:00
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 301864191, "num_examples": 21817}, {"name": "train", "num_bytes": 60558566627, "num_examples": 4368542}], "download_size": 20321590769, "dataset_size": 60860430818}}
2023-02-13T19:43:51+00:00
[]
[]
TAGS #region-us
# Dataset Card for "URL-block-size-1024" More Information needed
[ "# Dataset Card for \"URL-block-size-1024\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"URL-block-size-1024\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"URL-block-size-1024\"\n\nMore Information needed" ]
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052f26ea41986a0bcc6fac0f8ca05c3f5c5c7068
# Dataset for project: food-category-classification-v2.0 ## Dataset Description This dataset for project food-category-classification-v2.0 was scraped with the help of a bulk google image downloader. ## Dataset Structure ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['Bread', 'Dairy', 'Dessert', 'Egg', 'Fried Food', 'Fruit', 'Meat', 'Noodles', 'Rice', 'Seafood', 'Soup', 'Vegetable'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follows: | Split name | Num samples | | ------------ | ------------------- | | train | 1200 | | valid | 300 |
Kaludi/food-category-classification-v2.0
[ "task_categories:image-classification", "region:us" ]
2023-02-08T19:46:45+00:00
{"task_categories": ["image-classification"]}
2023-02-09T19:38:17+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
Dataset for project: food-category-classification-v2.0 ====================================================== Dataset Description ------------------- This dataset for project food-category-classification-v2.0 was scraped with the help of a bulk google image downloader. Dataset Structure ----------------- ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follows:
[ "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follows:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follows:" ]
[ 17, 23, 28 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follows:" ]
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364bf172c41a7ecefcd951d8fd9d9853882a5f23
# Dataset Card for "knots_AF_no_fragments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
roa7n/knots_AF_no_fragments
[ "region:us" ]
2023-02-08T19:52:29+00:00
{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "latestVersion", "dtype": "int64"}, {"name": "globalMetricValue", "dtype": "float64"}, {"name": "uniprotStart", "dtype": "int64"}, {"name": "uniprotEnd", "dtype": "int64"}, {"name": "uniprotSequence", "dtype": "string"}, {"name": "Length", "dtype": "float64"}, {"name": "Domain_architecture", "dtype": "string"}, {"name": "InterPro", "dtype": "string"}, {"name": "Max_Topology", "dtype": "string"}, {"name": "Max Freq", "dtype": "float64"}, {"name": "Knot Core", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "FamilyName", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 79153504.0, "num_examples": 136032}, {"name": "test", "num_bytes": 19788376.0, "num_examples": 34008}], "download_size": 79176508, "dataset_size": 98941880.0}}
2023-02-08T19:52:53+00:00
[]
[]
TAGS #region-us
# Dataset Card for "knots_AF_no_fragments" More Information needed
[ "# Dataset Card for \"knots_AF_no_fragments\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"knots_AF_no_fragments\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"knots_AF_no_fragments\"\n\nMore Information needed" ]
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156e8fd397b70d35a96fe05721a463d265e69f4e
## Dataset Description - **Repository:** [allenai/lila](https://github.com/allenai/lila) - **Paper:** [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf) - **Point of Contact:** [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welleck](https://wellecks.com/) # Lila: A Unified Benchmark for Mathematical Reasoning - **Homepage: https://lila.apps.allenai.org/** - **Repository: https://github.com/allenai/lila** - **Paper: https://aclanthology.org/2022.emnlp-main.392.pdf** ### Licensing Information Creative Commons Attribution 4.0 International ### Citation Information Cite this dataset and the source datasets (see [sources.bib](https://github.com/allenai/Lila/blob/main/sources.bib)). ```bib @INPROCEEDINGS{Mishra2022Lila, author = { Swaroop Mishra and Matthew Finlayson and Pan Lu and Leonard Tang and Sean Welleck and Chitta Baral and Tanmay Rajpurohit and Oyvind Tafjord and Ashish Sabharwal and Peter Clark and Ashwin Kalyan}, title = {Lila: A Unified Benchmark for Mathematical Reasoning}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022} } ```
allenai/lila
[ "license:cc-by-4.0", "region:us" ]
2023-02-08T21:39:35+00:00
{"license": "cc-by-4.0"}
2023-03-15T18:36:28+00:00
[]
[]
TAGS #license-cc-by-4.0 #region-us
## Dataset Description - Repository: allenai/lila - Paper: LILA: A Unified Benchmark for Mathematical Reasoning - Point of Contact: Matthew Finlayson, Sean Welleck # Lila: A Unified Benchmark for Mathematical Reasoning - Homepage: URL - Repository: URL - Paper: URL ### Licensing Information Creative Commons Attribution 4.0 International Cite this dataset and the source datasets (see URL).
[ "## Dataset Description\n\n- Repository: allenai/lila\n- Paper: LILA: A Unified Benchmark for Mathematical Reasoning\n- Point of Contact: Matthew Finlayson, Sean Welleck", "# Lila: A Unified Benchmark for Mathematical Reasoning\n\n\n- Homepage: URL \n- Repository: URL \n- Paper: URL", "### Licensing Information\n\nCreative Commons Attribution 4.0 International\n\n\n\nCite this dataset and the source datasets (see URL)." ]
[ "TAGS\n#license-cc-by-4.0 #region-us \n", "## Dataset Description\n\n- Repository: allenai/lila\n- Paper: LILA: A Unified Benchmark for Mathematical Reasoning\n- Point of Contact: Matthew Finlayson, Sean Welleck", "# Lila: A Unified Benchmark for Mathematical Reasoning\n\n\n- Homepage: URL \n- Repository: URL \n- Paper: URL", "### Licensing Information\n\nCreative Commons Attribution 4.0 International\n\n\n\nCite this dataset and the source datasets (see URL)." ]
[ 15, 45, 30, 26 ]
[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n## Dataset Description\n\n- Repository: allenai/lila\n- Paper: LILA: A Unified Benchmark for Mathematical Reasoning\n- Point of Contact: Matthew Finlayson, Sean Welleck# Lila: A Unified Benchmark for Mathematical Reasoning\n\n\n- Homepage: URL \n- Repository: URL \n- Paper: URL### Licensing Information\n\nCreative Commons Attribution 4.0 International\n\n\n\nCite this dataset and the source datasets (see URL)." ]
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bace4f31335e6fd578adce5c71aa3f3fcabd5fa8
# Dataset Card for "davinci-pairwise-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/davinci-pairwise-filtered
[ "region:us" ]
2023-02-08T21:53:36+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1517383530, "num_examples": 93540}, {"name": "test", "num_bytes": 123825205, "num_examples": 14391}], "download_size": 316920124, "dataset_size": 1641208735}}
2023-02-08T21:53:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "davinci-pairwise-filtered" More Information needed
[ "# Dataset Card for \"davinci-pairwise-filtered\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"davinci-pairwise-filtered\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"davinci-pairwise-filtered\"\n\nMore Information needed" ]
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0fafedf499065581e10240291bab20dba8b2d33d
# Dataset Card for "combined-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/combined-pairwise
[ "region:us" ]
2023-02-08T22:12:32+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1636100193, "num_examples": 201282}, {"name": "test", "num_bytes": 131387387, "num_examples": 21151}], "download_size": 453248009, "dataset_size": 1767487580}}
2023-02-08T22:12:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "combined-pairwise" More Information needed
[ "# Dataset Card for \"combined-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"combined-pairwise\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"combined-pairwise\"\n\nMore Information needed" ]
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9635c1dc32c9a45e70e4373cf82e0c2e8fbe12c3
# Dataset Card for "Europarl-ST" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://www.mllp.upv.es/europarl-st/ - **Paper:** https://ieeexplore.ieee.org/document/9054626 - **Point of Contact:** https://www.mllp.upv.es/ ### Dataset Summary Europarl-ST is a Multilingual Speech Translation Corpus, that contains paired audio-text samples for Speech Translation, constructed using the debates carried out in the European Parliament in the period between 2008 and 2012. ### Languages Spanish, German, English, French, Dutch, Polish, Portuguese, Romanian, Italian ## Dataset Structure ### Data Fields - **original_audio:** The original speech that is heard on the recording. - **original_language:** The language of the audio - **audio_path:** Path to the audio file - **segment_start:** Second in which the speech begins - **segment_end:** Second in which the speech ends - **transcriptions:** Dictionary containing transcriptions into different languages ### Data Splits - **train split:** 116138 samples - **valid split:** 17538 samples - **test split:** 18901 samples Train set (hours): | src/tgt | en | fr | de | it | es | pt | pl | ro | nl | |---------|----|----|----|----|----|----|----|----|----| | en | - | 81 | 83 | 80 | 81 | 81 | 79 | 72 | 80 | | fr | 32 | - | 21 | 20 | 21 | 22 | 20 | 18 | 22 | | de | 30 | 18 | - | 17 | 18 | 18 | 17 | 17 | 18 | | it | 37 | 21 | 21 | - | 21 | 21 | 21 | 19 | 20 | | es | 22 | 14 | 14 | 14 | - | 14 | 13 | 12 | 13 | | pt | 15 | 10 | 10 | 10 | 10 | - | 9 | 9 | 9 | | pl | 28 | 18 | 18 | 17 | 18 | 18 | - | 16 | 18 | | ro | 24 | 12 | 12 | 12 | 12 | 12 | 12 | - | 12 | | nl | 7 | 5 | 5 | 4 | 5 | 4 | 4 | 4 | - | Valid/Test sets are all between 3 and 6 hours. ## Additional Information ### Licensing Information * The work carried out for constructing the Europarl-ST corpus is released under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0) * All rights of the data belong to the European Union and respective copyright holders. ### Citation Information If you use the corpus in your research please cite the following reference: @INPROCEEDINGS{jairsan2020a, author={J. {Iranzo-Sánchez} and J. A. {Silvestre-Cerdà} and J. {Jorge} and N. {Roselló} and A. {Giménez} and A. {Sanchis} and J. {Civera} and A. {Juan}}, booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Europarl-ST: A Multilingual Corpus for Speech Translation of Parliamentary Debates}, year={2020}, pages={8229-8233},}
tj-solergibert/Europarl-ST
[ "task_categories:translation", "task_categories:text-to-speech", "size_categories:100K<n<1M", "language:es", "language:de", "language:en", "language:fr", "language:nl", "language:pl", "language:pt", "language:ro", "language:it", "license:cc-by-nc-4.0", "region:us" ]
2023-02-08T22:47:18+00:00
{"language": ["es", "de", "en", "fr", "nl", "pl", "pt", "ro", "it"], "license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["translation", "text-to-speech"], "dataset_info": {"features": [{"name": "original_speech", "dtype": "string"}, {"name": "original_language", "dtype": "string"}, {"name": "audio_path", "dtype": "string"}, {"name": "segment_start", "dtype": "float32"}, {"name": "segment_end", "dtype": "float32"}, {"name": "transcriptions", "struct": [{"name": "de", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "es", "dtype": "string"}, {"name": "fr", "dtype": "string"}, {"name": "it", "dtype": "string"}, {"name": "nl", "dtype": "string"}, {"name": "pl", "dtype": "string"}, {"name": "pt", "dtype": "string"}, {"name": "ro", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 147857910, "num_examples": 116138}, {"name": "valid", "num_bytes": 21318985, "num_examples": 17538}, {"name": "test", "num_bytes": 22580968, "num_examples": 18901}], "download_size": 109205144, "dataset_size": 191757863}}
2023-02-09T10:22:06+00:00
[]
[ "es", "de", "en", "fr", "nl", "pl", "pt", "ro", "it" ]
TAGS #task_categories-translation #task_categories-text-to-speech #size_categories-100K<n<1M #language-Spanish #language-German #language-English #language-French #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Italian #license-cc-by-nc-4.0 #region-us
Dataset Card for "Europarl-ST" ============================== Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Languages * Dataset Structure + Data Fields + Data Splits * Additional Information + Licensing Information + Citation Information Dataset Description ------------------- * Homepage: URL * Paper: URL * Point of Contact: URL ### Dataset Summary Europarl-ST is a Multilingual Speech Translation Corpus, that contains paired audio-text samples for Speech Translation, constructed using the debates carried out in the European Parliament in the period between 2008 and 2012. ### Languages Spanish, German, English, French, Dutch, Polish, Portuguese, Romanian, Italian Dataset Structure ----------------- ### Data Fields * original\_audio: The original speech that is heard on the recording. * original\_language: The language of the audio * audio\_path: Path to the audio file * segment\_start: Second in which the speech begins * segment\_end: Second in which the speech ends * transcriptions: Dictionary containing transcriptions into different languages ### Data Splits * train split: 116138 samples * valid split: 17538 samples * test split: 18901 samples Train set (hours): Valid/Test sets are all between 3 and 6 hours. Additional Information ---------------------- ### Licensing Information * The work carried out for constructing the Europarl-ST corpus is released under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0) * All rights of the data belong to the European Union and respective copyright holders. If you use the corpus in your research please cite the following reference: @INPROCEEDINGS{jairsan2020a, author={J. {Iranzo-Sánchez} and J. A. {Silvestre-Cerdà} and J. {Jorge} and N. {Roselló} and A. {Giménez} and A. {Sanchis} and J. {Civera} and A. {Juan}}, booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Europarl-ST: A Multilingual Corpus for Speech Translation of Parliamentary Debates}, year={2020}, pages={8229-8233},}
[ "### Dataset Summary\n\n\nEuroparl-ST is a Multilingual Speech Translation Corpus, that contains paired audio-text samples for Speech Translation, constructed using the debates carried out in the European Parliament in the period between 2008 and 2012.", "### Languages\n\n\nSpanish, German, English, French, Dutch, Polish, Portuguese, Romanian, Italian\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* original\\_audio: The original speech that is heard on the recording.\n* original\\_language: The language of the audio\n* audio\\_path: Path to the audio file\n* segment\\_start: Second in which the speech begins\n* segment\\_end: Second in which the speech ends\n* transcriptions: Dictionary containing transcriptions into different languages", "### Data Splits\n\n\n* train split: 116138 samples\n* valid split: 17538 samples\n* test split: 18901 samples\n\n\nTrain set (hours):\n\n\n\nValid/Test sets are all between 3 and 6 hours.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\n* The work carried out for constructing the Europarl-ST corpus is released under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)\n* All rights of the data belong to the European Union and respective copyright holders.\n\n\nIf you use the corpus in your research please cite the following reference:\n\n\n@INPROCEEDINGS{jairsan2020a,\nauthor={J. {Iranzo-Sánchez} and J. A. {Silvestre-Cerdà} and J. {Jorge} and N. {Roselló} and A. {Giménez} and A. {Sanchis} and J. {Civera} and A. {Juan}},\nbooktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\ntitle={Europarl-ST: A Multilingual Corpus for Speech Translation of Parliamentary Debates},\nyear={2020},\npages={8229-8233},}" ]
[ "TAGS\n#task_categories-translation #task_categories-text-to-speech #size_categories-100K<n<1M #language-Spanish #language-German #language-English #language-French #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Italian #license-cc-by-nc-4.0 #region-us \n", "### Dataset Summary\n\n\nEuroparl-ST is a Multilingual Speech Translation Corpus, that contains paired audio-text samples for Speech Translation, constructed using the debates carried out in the European Parliament in the period between 2008 and 2012.", "### Languages\n\n\nSpanish, German, English, French, Dutch, Polish, Portuguese, Romanian, Italian\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* original\\_audio: The original speech that is heard on the recording.\n* original\\_language: The language of the audio\n* audio\\_path: Path to the audio file\n* segment\\_start: Second in which the speech begins\n* segment\\_end: Second in which the speech ends\n* transcriptions: Dictionary containing transcriptions into different languages", "### Data Splits\n\n\n* train split: 116138 samples\n* valid split: 17538 samples\n* test split: 18901 samples\n\n\nTrain set (hours):\n\n\n\nValid/Test sets are all between 3 and 6 hours.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\n* The work carried out for constructing the Europarl-ST corpus is released under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)\n* All rights of the data belong to the European Union and respective copyright holders.\n\n\nIf you use the corpus in your research please cite the following reference:\n\n\n@INPROCEEDINGS{jairsan2020a,\nauthor={J. {Iranzo-Sánchez} and J. A. {Silvestre-Cerdà} and J. {Jorge} and N. {Roselló} and A. {Giménez} and A. {Sanchis} and J. {Civera} and A. {Juan}},\nbooktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\ntitle={Europarl-ST: A Multilingual Corpus for Speech Translation of Parliamentary Debates},\nyear={2020},\npages={8229-8233},}" ]
[ 97, 52, 32, 85, 56, 232 ]
[ "passage: TAGS\n#task_categories-translation #task_categories-text-to-speech #size_categories-100K<n<1M #language-Spanish #language-German #language-English #language-French #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Italian #license-cc-by-nc-4.0 #region-us \n### Dataset Summary\n\n\nEuroparl-ST is a Multilingual Speech Translation Corpus, that contains paired audio-text samples for Speech Translation, constructed using the debates carried out in the European Parliament in the period between 2008 and 2012.### Languages\n\n\nSpanish, German, English, French, Dutch, Polish, Portuguese, Romanian, Italian\n\n\nDataset Structure\n-----------------### Data Fields\n\n\n* original\\_audio: The original speech that is heard on the recording.\n* original\\_language: The language of the audio\n* audio\\_path: Path to the audio file\n* segment\\_start: Second in which the speech begins\n* segment\\_end: Second in which the speech ends\n* transcriptions: Dictionary containing transcriptions into different languages### Data Splits\n\n\n* train split: 116138 samples\n* valid split: 17538 samples\n* test split: 18901 samples\n\n\nTrain set (hours):\n\n\n\nValid/Test sets are all between 3 and 6 hours.\n\n\nAdditional Information\n----------------------" ]
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fc9d462f940ecf85b534562e6767ffaf527241e8
# Karen Fukuhara textual inversion This is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo). ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid1.png) ## Embedding Usage Use the token ```kfukvf-1990``` ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid2.png) --- ## 🎶 Prompt Examples 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, dressed as a queen with glimmering jewelry, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful, dynamic lighting``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_ --- 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, sitting near a table having a drink, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_ --- 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, wearing a dirty jacket near a busy city, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_ ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid3.png) ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid4.png) --- ## 🎴 text2img Sampler and Checkpoint grids: It's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking. [Sampling Grid](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/sampler_ckpt_grid.png) --- ☕ If you enjoy this model, buy me a coffee [Buy a coffee](https://ko-fi.com/3eegames) ---
zuleo/karen-fukuhara
[ "license:creativeml-openrail-m", "stable-diffusion", "embedding", "textual-inversion", "text-to-image", "image-to-image", "art", "artistic", "region:us" ]
2023-02-08T23:03:32+00:00
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "embedding", "textual-inversion", "text-to-image", "image-to-image", "art", "artistic"]}
2023-03-06T19:17:14+00:00
[]
[]
TAGS #license-creativeml-openrail-m #stable-diffusion #embedding #textual-inversion #text-to-image #image-to-image #art #artistic #region-us
# Karen Fukuhara textual inversion This is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo). !Detailed Samples ## Embedding Usage Use the token !Detailed Samples --- ## Prompt Examples Negative prompt: _Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_ --- Negative prompt: _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_ --- Negative prompt: _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_ !Detailed Samples !Detailed Samples --- ## text2img Sampler and Checkpoint grids: It's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking. Sampling Grid --- If you enjoy this model, buy me a coffee Buy a coffee ---
[ "# Karen Fukuhara textual inversion\n\nThis is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo).\n\n\n!Detailed Samples", "## Embedding Usage\n\nUse the token \n\n\n!Detailed Samples\n\n---", "## Prompt Examples\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_\n\n---\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_\n\n---\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_\n\n\n!Detailed Samples\n\n!Detailed Samples\n\n---", "## text2img Sampler and Checkpoint grids:\n\nIt's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking.\n\nSampling Grid\n\n---\n\n If you enjoy this model, buy me a coffee Buy a coffee\n\n---" ]
[ "TAGS\n#license-creativeml-openrail-m #stable-diffusion #embedding #textual-inversion #text-to-image #image-to-image #art #artistic #region-us \n", "# Karen Fukuhara textual inversion\n\nThis is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo).\n\n\n!Detailed Samples", "## Embedding Usage\n\nUse the token \n\n\n!Detailed Samples\n\n---", "## Prompt Examples\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_\n\n---\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_\n\n---\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_\n\n\n!Detailed Samples\n\n!Detailed Samples\n\n---", "## text2img Sampler and Checkpoint grids:\n\nIt's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking.\n\nSampling Grid\n\n---\n\n If you enjoy this model, buy me a coffee Buy a coffee\n\n---" ]
[ 51, 59, 17, 173, 73 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #stable-diffusion #embedding #textual-inversion #text-to-image #image-to-image #art #artistic #region-us \n# Karen Fukuhara textual inversion\n\nThis is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo).\n\n\n!Detailed Samples## Embedding Usage\n\nUse the token \n\n\n!Detailed Samples\n\n---## Prompt Examples\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_\n\n---\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_\n\n---\n\n \n\n Negative prompt: \n\n_Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_\n\n\n!Detailed Samples\n\n!Detailed Samples\n\n---## text2img Sampler and Checkpoint grids:\n\nIt's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking.\n\nSampling Grid\n\n---\n\n If you enjoy this model, buy me a coffee Buy a coffee\n\n---" ]
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ff54431d755bd04cbf336a97d18417dbe3c93f71
# Dataset Card for "Sound_Spectrogram_Description" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jha2ee/Sound_Spectrogram_Description
[ "region:us" ]
2023-02-08T23:58:26+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16182594.0, "num_examples": 218}], "download_size": 16178537, "dataset_size": 16182594.0}}
2023-02-09T01:25:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Sound_Spectrogram_Description" More Information needed
[ "# Dataset Card for \"Sound_Spectrogram_Description\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Sound_Spectrogram_Description\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Sound_Spectrogram_Description\"\n\nMore Information needed" ]
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206eae0b49785320d08d3dce87ecb2e2294cf780
# Dataset Card for Genshin Voice ## Dataset Description ### Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. ### Languages The text in the dataset is in Mandarin. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The data was obtained by unpacking the [Genshin Impact](https://genshin.hoyoverse.com/) game. #### Who are the source language producers? The language producers are the employee of [Hoyoverse](https://hoyoverse.com/) and contractors from [EchoSky Studio](http://qx.asiacu.com/). ### Annotations The dataset contains official annotations from the game, including ingame speaker name and transcripts. ## Additional Information ### Dataset Curators The dataset was created by [w4123](https://github.com/w4123) initially in his [GitHub repository](https://github.com/w4123/GenshinVoice). ### Licensing Information Copyright © COGNOSPHERE. All Rights Reserved.
hanamizuki-ai/genshin-voice-v3.4-mandarin
[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "multilinguality:monolingual", "source_datasets:original", "language:zh", "region:us" ]
2023-02-09T01:50:09+00:00
{"language": ["zh"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-to-speech", "automatic-speech-recognition"], "pretty_name": "Genshin Voice", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "language", "dtype": "string"}, {"name": "npcName", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20516788863.251, "num_examples": 78337}], "download_size": 34041643248, "dataset_size": 20516788863.251}}
2023-04-13T01:28:53+00:00
[]
[ "zh" ]
TAGS #task_categories-text-to-speech #task_categories-automatic-speech-recognition #multilinguality-monolingual #source_datasets-original #language-Chinese #region-us
# Dataset Card for Genshin Voice ## Dataset Description ### Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. ### Languages The text in the dataset is in Mandarin. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The data was obtained by unpacking the Genshin Impact game. #### Who are the source language producers? The language producers are the employee of Hoyoverse and contractors from EchoSky Studio. ### Annotations The dataset contains official annotations from the game, including ingame speaker name and transcripts. ## Additional Information ### Dataset Curators The dataset was created by w4123 initially in his GitHub repository. ### Licensing Information Copyright © COGNOSPHERE. All Rights Reserved.
[ "# Dataset Card for Genshin Voice", "## Dataset Description", "### Dataset Summary\n\nThe Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.", "### Languages\n\nThe text in the dataset is in Mandarin.", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was obtained by unpacking the Genshin Impact game.", "#### Who are the source language producers?\n\nThe language producers are the employee of Hoyoverse and contractors from EchoSky Studio.", "### Annotations\n\nThe dataset contains official annotations from the game, including ingame speaker name and transcripts.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by w4123 initially in his GitHub repository.", "### Licensing Information\n\nCopyright © COGNOSPHERE. All Rights Reserved." ]
[ "TAGS\n#task_categories-text-to-speech #task_categories-automatic-speech-recognition #multilinguality-monolingual #source_datasets-original #language-Chinese #region-us \n", "# Dataset Card for Genshin Voice", "## Dataset Description", "### Dataset Summary\n\nThe Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.", "### Languages\n\nThe text in the dataset is in Mandarin.", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was obtained by unpacking the Genshin Impact game.", "#### Who are the source language producers?\n\nThe language producers are the employee of Hoyoverse and contractors from EchoSky Studio.", "### Annotations\n\nThe dataset contains official annotations from the game, including ingame speaker name and transcripts.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by w4123 initially in his GitHub repository.", "### Licensing Information\n\nCopyright © COGNOSPHERE. All Rights Reserved." ]
[ 56, 8, 4, 35, 15, 5, 4, 25, 30, 28, 5, 26, 18 ]
[ "passage: TAGS\n#task_categories-text-to-speech #task_categories-automatic-speech-recognition #multilinguality-monolingual #source_datasets-original #language-Chinese #region-us \n# Dataset Card for Genshin Voice## Dataset Description### Dataset Summary\n\nThe Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.### Languages\n\nThe text in the dataset is in Mandarin.## Dataset Creation### Source Data#### Initial Data Collection and Normalization\n\nThe data was obtained by unpacking the Genshin Impact game.#### Who are the source language producers?\n\nThe language producers are the employee of Hoyoverse and contractors from EchoSky Studio.### Annotations\n\nThe dataset contains official annotations from the game, including ingame speaker name and transcripts.## Additional Information### Dataset Curators\n\nThe dataset was created by w4123 initially in his GitHub repository.### Licensing Information\n\nCopyright © COGNOSPHERE. All Rights Reserved." ]
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806da0c59c7c6a1176362a4cfe6add9b59cb66ed
# Dataset Card for "Hatefulmemes_test_facebook_opt_6.7b_Hatefulmemes_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Hatefulmemes_test_facebook_opt_6.7b_Hatefulmemes_ns_1000
[ "region:us" ]
2023-02-09T02:41:25+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 362719567.0, "num_examples": 1000}, {"name": "fewshot_3_bs_16", "num_bytes": 363587206.0, "num_examples": 1000}, {"name": "fewshot_5_bs_16", "num_bytes": 364454992.0, "num_examples": 1000}, {"name": "fewshot_8_bs_16", "num_bytes": 365760377.0, "num_examples": 1000}, {"name": "fewshot_10_bs_16", "num_bytes": 366632224.0, "num_examples": 1000}], "download_size": 1814428039, "dataset_size": 1823154366.0}}
2023-02-09T03:01:01+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_facebook_opt_6.7b_Hatefulmemes_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_facebook_opt_6.7b_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_facebook_opt_6.7b_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
[ 6, 35 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_facebook_opt_6.7b_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
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b72308c25e89ec8484d1d71011b47ab0c60ebbfa
# Dataset Card for "Hatefulmemes_test_facebook_opt_13b_Hatefulmemes_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Hatefulmemes_test_facebook_opt_13b_Hatefulmemes_ns_1000
[ "region:us" ]
2023-02-09T03:18:48+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 362719683.0, "num_examples": 1000}, {"name": "fewshot_3_bs_16", "num_bytes": 363588098.0, "num_examples": 1000}, {"name": "fewshot_5_bs_16", "num_bytes": 364455860.0, "num_examples": 1000}, {"name": "fewshot_8_bs_16", "num_bytes": 365761089.0, "num_examples": 1000}, {"name": "fewshot_10_bs_16", "num_bytes": 366632848.0, "num_examples": 1000}], "download_size": 1814428412, "dataset_size": 1823157578.0}}
2023-02-09T03:41:39+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_facebook_opt_13b_Hatefulmemes_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_facebook_opt_13b_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_facebook_opt_13b_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
[ 6, 35 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_facebook_opt_13b_Hatefulmemes_ns_1000\"\n\nMore Information needed" ]
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f8b80ef811934c64f783726712fb57b3a58850a1
## Source Combined text-only dataset from - poloclub/diffusiondb - Gustavosta/Stable-Diffusion-Prompts - bartman081523/stable-diffusion-discord-prompts - FredZhang7/krea-ai-prompts For preprocessing methods, please see [Fast GPT2 PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2). ## Python Download and save the dataset to `all_prompts.txt` locally. ```bash pip install datasets ``` ```python import datasets dataset = datasets.load_dataset("FredZhang7/stable-diffusion-prompts-2.47M") train = dataset["train"] prompts = train["text"] with open("all_prompts.txt", "w") as f: for prompt in prompts: f.write(prompt + "\n") ```
FredZhang7/stable-diffusion-prompts-2.47M
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:en", "license:creativeml-openrail-m", "region:us" ]
2023-02-09T04:03:22+00:00
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "SDP-2.47M"}
2023-02-11T21:59:33+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-creativeml-openrail-m #region-us
## Source Combined text-only dataset from - poloclub/diffusiondb - Gustavosta/Stable-Diffusion-Prompts - bartman081523/stable-diffusion-discord-prompts - FredZhang7/krea-ai-prompts For preprocessing methods, please see Fast GPT2 PromptGen. ## Python Download and save the dataset to 'all_prompts.txt' locally.
[ "## Source\n\nCombined text-only dataset from\n- poloclub/diffusiondb\n- Gustavosta/Stable-Diffusion-Prompts\n- bartman081523/stable-diffusion-discord-prompts\n- FredZhang7/krea-ai-prompts\n\nFor preprocessing methods, please see Fast GPT2 PromptGen.", "## Python\n\nDownload and save the dataset to 'all_prompts.txt' locally." ]
[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-creativeml-openrail-m #region-us \n", "## Source\n\nCombined text-only dataset from\n- poloclub/diffusiondb\n- Gustavosta/Stable-Diffusion-Prompts\n- bartman081523/stable-diffusion-discord-prompts\n- FredZhang7/krea-ai-prompts\n\nFor preprocessing methods, please see Fast GPT2 PromptGen.", "## Python\n\nDownload and save the dataset to 'all_prompts.txt' locally." ]
[ 45, 84, 21 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-creativeml-openrail-m #region-us \n## Source\n\nCombined text-only dataset from\n- poloclub/diffusiondb\n- Gustavosta/Stable-Diffusion-Prompts\n- bartman081523/stable-diffusion-discord-prompts\n- FredZhang7/krea-ai-prompts\n\nFor preprocessing methods, please see Fast GPT2 PromptGen.## Python\n\nDownload and save the dataset to 'all_prompts.txt' locally." ]
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d341e36418ac5d3f485a2258bfbd65939f4ce56f
# Dataset Card for "filled_stacks_metadata" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bejaeger/filled_stacks_metadata
[ "region:us" ]
2023-02-09T04:39:49+00:00
{"dataset_info": {"features": [{"name": "videoId", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "channelId", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "publishedAt", "dtype": "string"}, {"name": "likes", "dtype": "string"}, {"name": "views", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45766, "num_examples": 98}], "download_size": 0, "dataset_size": 45766}}
2023-02-09T04:40:27+00:00
[]
[]
TAGS #region-us
# Dataset Card for "filled_stacks_metadata" More Information needed
[ "# Dataset Card for \"filled_stacks_metadata\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"filled_stacks_metadata\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"filled_stacks_metadata\"\n\nMore Information needed" ]
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f8e94d0e3d17822667514cd0cdaf989433ce48d8
# Dataset Card for "filled_stacks_transcriptions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bejaeger/filled_stacks_transcriptions
[ "region:us" ]
2023-02-09T05:41:25+00:00
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "published", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "videoId", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3495561, "num_examples": 13292}], "download_size": 959645, "dataset_size": 3495561}}
2023-02-09T05:41:27+00:00
[]
[]
TAGS #region-us
# Dataset Card for "filled_stacks_transcriptions" More Information needed
[ "# Dataset Card for \"filled_stacks_transcriptions\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"filled_stacks_transcriptions\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"filled_stacks_transcriptions\"\n\nMore Information needed" ]
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9ad3be54f50b7e9a616d0f74be76ba39850abb8d
# Dataset Card for "turkishReviews-ds-mini1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ramazank2000/turkishReviews-ds-mini1
[ "region:us" ]
2023-02-09T06:02:03+00:00
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 0, "dataset_size": 1392332.0}}
2023-02-13T03:03:24+00:00
[]
[]
TAGS #region-us
# Dataset Card for "turkishReviews-ds-mini1" More Information needed
[ "# Dataset Card for \"turkishReviews-ds-mini1\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"turkishReviews-ds-mini1\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"turkishReviews-ds-mini1\"\n\nMore Information needed" ]
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3f1c54f7517556648417cdc487bd924dfbe1d02c
# Dataset Card for "srk_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rkhilnani96/srk_images
[ "region:us" ]
2023-02-09T07:47:57+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 226593.0, "num_examples": 7}], "download_size": 227211, "dataset_size": 226593.0}}
2023-02-09T07:48:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "srk_images" More Information needed
[ "# Dataset Card for \"srk_images\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"srk_images\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"srk_images\"\n\nMore Information needed" ]
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ab788cd63e2b59f2ab1fe7086c62a82b1f4a65ca
For more info on data collection and the preprocessing algorithm, please see [Fast Anime PromptGen](https://huggingface.co/FredZhang7/anime-anything-promptgen-v2). ## 80K unique prompts - `safebooru_clean`: Cleaned prompts with upscore ≥ 8 from the Safebooru API --- For disclaimers about the Danbooru data, please see [Danbooru Tag Generator](https://huggingface.co/FredZhang7/danbooru-tag-generator). ## 100K unique prompts (each) - `danbooru_raw`: Raw prompts with upscore ≥ 3 from Danbooru API - `danbooru_clean`: Cleaned prompts with upscore ≥ 3 from Danbooru API --- ## Python Download and save the dataset to anime_prompts.csv locally. ```bash pip install datasets ``` ```python import csv import datasets dataset = datasets.load_dataset("FredZhang7/anime-prompts-180K") train = dataset["train"] safebooru_clean = train["safebooru_clean"] danbooru_clean = train["danbooru_clean"] danbooru_raw = train["danbooru_raw"] with open("anime_prompts.csv", "w") as f: writer = csv.writer(f) writer.writerow(["safebooru_clean", "danbooru_clean", "danbooru_raw"]) for i in range(len(safebooru_clean)): writer.writerow([safebooru_clean[i], danbooru_clean[i], danbooru_raw[i]]) ```
FredZhang7/anime-prompts-180K
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:creativeml-openrail-m", "region:us" ]
2023-02-09T07:55:28+00:00
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "viewer": false}
2023-12-20T07:18:20+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-creativeml-openrail-m #region-us
For more info on data collection and the preprocessing algorithm, please see Fast Anime PromptGen. ## 80K unique prompts - 'safebooru_clean': Cleaned prompts with upscore ≥ 8 from the Safebooru API --- For disclaimers about the Danbooru data, please see Danbooru Tag Generator. ## 100K unique prompts (each) - 'danbooru_raw': Raw prompts with upscore ≥ 3 from Danbooru API - 'danbooru_clean': Cleaned prompts with upscore ≥ 3 from Danbooru API --- ## Python Download and save the dataset to anime_prompts.csv locally.
[ "## 80K unique prompts\n- 'safebooru_clean': Cleaned prompts with upscore ≥ 8 from the Safebooru API\n\n---\n\nFor disclaimers about the Danbooru data, please see Danbooru Tag Generator.", "## 100K unique prompts (each)\n- 'danbooru_raw': Raw prompts with upscore ≥ 3 from Danbooru API\n- 'danbooru_clean': Cleaned prompts with upscore ≥ 3 from Danbooru API\n\n---", "## Python\n\nDownload and save the dataset to anime_prompts.csv locally." ]
[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-creativeml-openrail-m #region-us \n", "## 80K unique prompts\n- 'safebooru_clean': Cleaned prompts with upscore ≥ 8 from the Safebooru API\n\n---\n\nFor disclaimers about the Danbooru data, please see Danbooru Tag Generator.", "## 100K unique prompts (each)\n- 'danbooru_raw': Raw prompts with upscore ≥ 3 from Danbooru API\n- 'danbooru_clean': Cleaned prompts with upscore ≥ 3 from Danbooru API\n\n---", "## Python\n\nDownload and save the dataset to anime_prompts.csv locally." ]
[ 45, 53, 60, 20 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-creativeml-openrail-m #region-us \n## 80K unique prompts\n- 'safebooru_clean': Cleaned prompts with upscore ≥ 8 from the Safebooru API\n\n---\n\nFor disclaimers about the Danbooru data, please see Danbooru Tag Generator.## 100K unique prompts (each)\n- 'danbooru_raw': Raw prompts with upscore ≥ 3 from Danbooru API\n- 'danbooru_clean': Cleaned prompts with upscore ≥ 3 from Danbooru API\n\n---## Python\n\nDownload and save the dataset to anime_prompts.csv locally." ]
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304a05a55bc6abc0446d8fae0d0771716b6a271a
Dataset from https://www.kaggle.com/datasets/rmisra/news-category-dataset
heegyu/news-category-dataset
[ "license:cc-by-4.0", "region:us" ]
2023-02-09T08:08:22+00:00
{"license": "cc-by-4.0"}
2023-02-09T08:10:48+00:00
[]
[]
TAGS #license-cc-by-4.0 #region-us
Dataset from URL
[]
[ "TAGS\n#license-cc-by-4.0 #region-us \n" ]
[ 15 ]
[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n" ]
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a8142d89cd7e437f8750761598505dfd1e456ef8
# 3Blue1Brown transcripts ## Data This dataset provides transcriptions of all videos of the amazing [3Blue1Brown](https://www.youtube.com/c/3blue1brown?app=desktop). Last update was on 09.02.2022. ### Schema ``` # Column Non-Null Count Dtype --- ------ -------------- ----- 0 video_title 116 non-null object 1 transcription 116 non-null object ```
psetinek/3Blue1Brown_transcripts
[ "language:en", "region:us" ]
2023-02-09T08:53:34+00:00
{"language": ["en"]}
2023-02-09T09:09:55+00:00
[]
[ "en" ]
TAGS #language-English #region-us
# 3Blue1Brown transcripts ## Data This dataset provides transcriptions of all videos of the amazing 3Blue1Brown. Last update was on 09.02.2022. ### Schema
[ "# 3Blue1Brown transcripts", "## Data\n\nThis dataset provides transcriptions of all videos of the amazing 3Blue1Brown.\n\nLast update was on 09.02.2022.", "### Schema" ]
[ "TAGS\n#language-English #region-us \n", "# 3Blue1Brown transcripts", "## Data\n\nThis dataset provides transcriptions of all videos of the amazing 3Blue1Brown.\n\nLast update was on 09.02.2022.", "### Schema" ]
[ 10, 9, 29, 4 ]
[ "passage: TAGS\n#language-English #region-us \n# 3Blue1Brown transcripts## Data\n\nThis dataset provides transcriptions of all videos of the amazing 3Blue1Brown.\n\nLast update was on 09.02.2022.### Schema" ]
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cb22f5f801015f0159ef5cadf2d16de9d33b5ee6
# Dataset Card for Dataset Name ## Dataset Description - **Repository:** https://www.kaggle.com/datasets/muhammadalbrham/rgb-arabic-alphabets-sign-language-dataset - **Paper:** https://arxiv.org/abs/2301.11932 - **Point of Contact:** [email protected] ### Dataset Summary RGB Arabic Alphabet Sign Language (AASL) dataset comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset. ### Supported Tasks and Leaderboards - Image Classification ### Languages - Arabic ## Dataset Structure ### Data Splits - All images for now ### Licensing Information https://creativecommons.org/licenses/by-sa/4.0/ ### Citation Information ``` @misc{https://doi.org/10.48550/arxiv.2301.11932, doi = {10.48550/ARXIV.2301.11932}, url = {https://arxiv.org/abs/2301.11932}, author = {Al-Barham, Muhammad and Alsharkawi, Adham and Al-Yaman, Musa and Al-Fetyani, Mohammad and Elnagar, Ashraf and SaAleek, Ahmad Abu and Al-Odat, Mohammad}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {RGB Arabic Alphabets Sign Language Dataset}, publisher = {arXiv}, year = {2023}, copyright = {Creative Commons Attribution 4.0 International} } ```
pain/AASL
[ "task_categories:image-segmentation", "size_categories:1K<n<10K", "language:ar", "license:cc-by-sa-4.0", "arxiv:2301.11932", "region:us" ]
2023-02-09T09:36:57+00:00
{"language": ["ar"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-segmentation"], "pretty_name": "RGB Arabic Alphabets Sign Language Dataset"}
2023-02-20T19:17:55+00:00
[ "2301.11932" ]
[ "ar" ]
TAGS #task_categories-image-segmentation #size_categories-1K<n<10K #language-Arabic #license-cc-by-sa-4.0 #arxiv-2301.11932 #region-us
# Dataset Card for Dataset Name ## Dataset Description - Repository: URL - Paper: URL - Point of Contact: muhammadal-brham@URL ### Dataset Summary RGB Arabic Alphabet Sign Language (AASL) dataset comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset. ### Supported Tasks and Leaderboards - Image Classification ### Languages - Arabic ## Dataset Structure ### Data Splits - All images for now ### Licensing Information URL
[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: muhammadal-brham@URL", "### Dataset Summary\n\nRGB Arabic Alphabet Sign Language (AASL) dataset comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset.", "### Supported Tasks and Leaderboards\n\n- Image Classification", "### Languages\n\n- Arabic", "## Dataset Structure", "### Data Splits\n\n- All images for now", "### Licensing Information\n\nURL" ]
[ "TAGS\n#task_categories-image-segmentation #size_categories-1K<n<10K #language-Arabic #license-cc-by-sa-4.0 #arxiv-2301.11932 #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: muhammadal-brham@URL", "### Dataset Summary\n\nRGB Arabic Alphabet Sign Language (AASL) dataset comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset.", "### Supported Tasks and Leaderboards\n\n- Image Classification", "### Languages\n\n- Arabic", "## Dataset Structure", "### Data Splits\n\n- All images for now", "### Licensing Information\n\nURL" ]
[ 55, 8, 27, 136, 14, 6, 6, 10, 7 ]
[ "passage: TAGS\n#task_categories-image-segmentation #size_categories-1K<n<10K #language-Arabic #license-cc-by-sa-4.0 #arxiv-2301.11932 #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: muhammadal-brham@URL### Dataset Summary\n\nRGB Arabic Alphabet Sign Language (AASL) dataset comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset.### Supported Tasks and Leaderboards\n\n- Image Classification### Languages\n\n- Arabic## Dataset Structure### Data Splits\n\n- All images for now### Licensing Information\n\nURL" ]
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205552ab8c0a1938f79394d2992fc10fc2b020d9
# Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jaese/github-issues
[ "region:us" ]
2023-02-09T09:46:39+00:00
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "labels", "list": [{"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "comments", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "author_association", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "total_count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "diff_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "merged_at", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "closed_at", "dtype": "string"}, {"name": "state_reason", "dtype": "string"}, {"name": "assignee", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "assignees", "list": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "milestone", "struct": [{"name": "closed_at", "dtype": "string"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}, {"name": "creator", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "description", "dtype": "string"}, {"name": "due_on", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "labels_url", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "open_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 16723706, "num_examples": 5524}], "download_size": 3919619, "dataset_size": 16723706}}
2023-02-09T09:46:52+00:00
[]
[]
TAGS #region-us
# Dataset Card for "github-issues" More Information needed
[ "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed" ]
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c7c4247a49a2760735dd267c52adec3c76eeacc4
# Dataset Card for "restaurant_order_local_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
figfig/restaurant_order_local_test
[ "region:us" ]
2023-02-09T09:53:56+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 270680.0, "num_examples": 2}, {"name": "test", "num_bytes": 270680.0, "num_examples": 2}], "download_size": 272201, "dataset_size": 541360.0}}
2023-02-09T12:44:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "restaurant_order_local_test" More Information needed
[ "# Dataset Card for \"restaurant_order_local_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"restaurant_order_local_test\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"restaurant_order_local_test\"\n\nMore Information needed" ]
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491eca866122317396e68d4cc8ce2a4ef1f42bfb
# squad_v2_factuality_v1 This dataset is derived from "squad_v2" training "context" with the following steps. 1. NER is run to extract entities. 2. Lexicon of person's name, date, organisation name and location are collected. 3. 20% of the time, one of the text attribute (person's name, date, organisation name and location) is randomly replaced. For consistency of context, all other place with the same name is also replaced. # Purpose of the Dataset The purpose of this dataset is to assess if a language model could detect factuality.
kenhktsui/squad_v2_factuality_v1
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:cc-by-sa-4.0", "region:us" ]
2023-02-09T12:18:35+00:00
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]}
2023-02-13T04:06:43+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #region-us
# squad_v2_factuality_v1 This dataset is derived from "squad_v2" training "context" with the following steps. 1. NER is run to extract entities. 2. Lexicon of person's name, date, organisation name and location are collected. 3. 20% of the time, one of the text attribute (person's name, date, organisation name and location) is randomly replaced. For consistency of context, all other place with the same name is also replaced. # Purpose of the Dataset The purpose of this dataset is to assess if a language model could detect factuality.
[ "# squad_v2_factuality_v1\nThis dataset is derived from \"squad_v2\" training \"context\" with the following steps.\n1. NER is run to extract entities.\n2. Lexicon of person's name, date, organisation name and location are collected.\n3. 20% of the time, one of the text attribute (person's name, date, organisation name and location) is randomly replaced. For consistency of context, all other place with the same name is also replaced.\n\n # Purpose of the Dataset\n The purpose of this dataset is to assess if a language model could detect factuality." ]
[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #region-us \n", "# squad_v2_factuality_v1\nThis dataset is derived from \"squad_v2\" training \"context\" with the following steps.\n1. NER is run to extract entities.\n2. Lexicon of person's name, date, organisation name and location are collected.\n3. 20% of the time, one of the text attribute (person's name, date, organisation name and location) is randomly replaced. For consistency of context, all other place with the same name is also replaced.\n\n # Purpose of the Dataset\n The purpose of this dataset is to assess if a language model could detect factuality." ]
[ 44, 141 ]
[ "passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #region-us \n# squad_v2_factuality_v1\nThis dataset is derived from \"squad_v2\" training \"context\" with the following steps.\n1. NER is run to extract entities.\n2. Lexicon of person's name, date, organisation name and location are collected.\n3. 20% of the time, one of the text attribute (person's name, date, organisation name and location) is randomly replaced. For consistency of context, all other place with the same name is also replaced.\n\n # Purpose of the Dataset\n The purpose of this dataset is to assess if a language model could detect factuality." ]
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45294eb6d4d2aa1230c8caa07dda2a03982e350e
# VRoid Image Dataset Lite This is a dataset to train text-to-image or other models without any copyright issue. All materials used in this dataset are CC0 or properly licensed. This dataset is also used to train [Mitsua Diffusion One](https://huggingface.co/Mitsua/mitsua-diffusion-one), which is a latent text-to-image diffusion model, whose VAE and U-Net are trained from scratch using only public domain/CC0 or copyright images with permission for use. Various parameters such as camera angle, pose, skin color and facial expression were randomly set and the images were output. ## Dataset License [Creative Open-Rail++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) This model is open access and available to all, with a CreativeML OpenRAIL++-M license further specifying rights and usage. The CreativeML OpenRAIL++-M License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL++-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) ## Materials used in this dataset and their licenses ### VRoid Models - VRM models used in this dataset are all CC0. - These models are made by VRoid Project - [HairSample_Male](https://vroid.pixiv.help/hc/en-us/articles/4402614652569-Do-VRoid-Studio-s-sample-models-come-with-conditions-of-use-) - [HairSample_Female](https://vroid.pixiv.help/hc/en-us/articles/4402614652569-Do-VRoid-Studio-s-sample-models-come-with-conditions-of-use-) - [AvatarSample-D](https://vroid.pixiv.help/hc/en-us/articles/360012381793-AvatarSample-D) - [AvatarSample-E](https://vroid.pixiv.help/hc/en-us/articles/360014900273-AvatarSample-E) - [AvatarSample-F](https://vroid.pixiv.help/hc/en-us/articles/360014900113-AvatarSample-F) - [AvatarSample-G](https://vroid.pixiv.help/hc/en-us/articles/360014900233-AvatarSample-G) - [Sakurada Fumiriya](https://vroid.pixiv.help/hc/en-us/articles/360014788554-Sakurada-Fumiriya) - [Sendagaya Shino](https://vroid.pixiv.help/hc/en-us/articles/360013482714-Sendagaya-Shino) - These models are made by pastelskies - [015](https://hub.vroid.com/characters/1636202188966335207/models/6893459099891579554) - [009](https://hub.vroid.com/characters/2472286065213980612/models/9151142999439416702) - [008](https://hub.vroid.com/characters/601931587119584437/models/3857812504036458003) - These models are made by yomox9 - [Qi](https://hub.vroid.com/characters/2048759159111415425/models/6905433332368675090) - These models are made by くつした - [【CC0】オリジナルアバター「少女A」【Cluster想定】](https://hub.vroid.com/characters/5271108759876567944/models/9069514665234246177) - These models are made by ろーてく - [【CC0】オリジナルアバター「シャペル」【VRChat想定】](https://lowteq.booth.pm/items/1349366) ### Pose and motions - Our original poses. - Free edition pose subset in [Unity Humanoid AnimationClip - PoseCollection](https://necocoya.booth.pm/items/1634088) made by かんな久＠ねここや様 (❗❗**NOT CC0**❗❗) - We have obtained permission directly from the author for training or distributing the AI model. - This dataset uses only a subset of the "Free edition (ポーズ詰め合わせ（無料版）in Japanese)", which is allowed to use for AI training. - We have confirmed directly from the author that an exact equivalent license is not necesserily needed to distribute the trained model or to generate images. - Therefore, to avoid harmful content generation, the Creative Open Rail++-M license is applied to this dataset, and an equivalent or more restrictive license must be applied to its derivatives. ### Shader - MToon (MIT) with some modification by dev team. ### Other Textures for Skybox / Ground - [Poly Haven](https://polyhaven.com/) (CC0) - [ambientCG](https://ambientcg.com/) (CC0) ## Metadata Description The final caption is not provided in this dataset, but you can create complete caption from metadata. ### Color Shifting Color shift is used to create more diverse images. It is applied to skin/hair/eye/cloth/accesories independently. - Parameter xyz = (H_Shift, S_Factor, V_Factor) - New Color HSV = (H + H_Shift, S * S_Factor, V * V_Factor) ### Metadata Items - vrm_name : VRoid model name - clip_name : Pose Clip Number - camera_profile - facial_expression - lighting - lighting_color - outline - shade_toony - skin_profile - looking_label - camera_position : 3D position in meter - camera_rotation : Pitch/Yaw/Roll in degree - camera_fov : in degree - hair_color_shift : HSV color shift of hair - eye_color_shift : HSV color shift of eye - color_shift : HSV color shift of cloth and accesories - ground_plane_material - left_hand_sign - right_hand_sign - skybox ## Full Dataset This is a subset of full dataset consisting of approx. 600k images. Full dataset would be available upon request only for non-commercial research purposes. You will need to provide 1 TB of online storage so that we could upload the data set or send us an empty 1 TB physical hard drive to our office located in Tokyo Japan. Contact : info [at] elanmitsua.com ## Developed by - Abstract Engine dev team - Special Thanks to Mitsua Contributors - VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions.
Mitsua/vroid-image-dataset-lite
[ "task_categories:text-to-image", "size_categories:1K<n<10K", "language:en", "language:ja", "license:openrail++", "region:us" ]
2023-02-09T12:27:18+00:00
{"language": ["en", "ja"], "license": "openrail++", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"]}
2023-03-03T15:02:51+00:00
[]
[ "en", "ja" ]
TAGS #task_categories-text-to-image #size_categories-1K<n<10K #language-English #language-Japanese #license-openrail++ #region-us
# VRoid Image Dataset Lite This is a dataset to train text-to-image or other models without any copyright issue. All materials used in this dataset are CC0 or properly licensed. This dataset is also used to train Mitsua Diffusion One, which is a latent text-to-image diffusion model, whose VAE and U-Net are trained from scratch using only public domain/CC0 or copyright images with permission for use. Various parameters such as camera angle, pose, skin color and facial expression were randomly set and the images were output. ## Dataset License Creative Open-Rail++-M License This model is open access and available to all, with a CreativeML OpenRAIL++-M license further specifying rights and usage. The CreativeML OpenRAIL++-M License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL++-M to all your users (please read the license entirely and carefully) Please read the full license here ## Materials used in this dataset and their licenses ### VRoid Models - VRM models used in this dataset are all CC0. - These models are made by VRoid Project - HairSample_Male - HairSample_Female - AvatarSample-D - AvatarSample-E - AvatarSample-F - AvatarSample-G - Sakurada Fumiriya - Sendagaya Shino - These models are made by pastelskies - 015 - 009 - 008 - These models are made by yomox9 - Qi - These models are made by くつした - 【CC0】オリジナルアバター「少女A」【Cluster想定】 - These models are made by ろーてく - 【CC0】オリジナルアバター「シャペル」【VRChat想定】 ### Pose and motions - Our original poses. - Free edition pose subset in Unity Humanoid AnimationClip - PoseCollection made by かんな久＠ねここや様 (NOT CC0) - We have obtained permission directly from the author for training or distributing the AI model. - This dataset uses only a subset of the "Free edition (ポーズ詰め合わせ（無料版）in Japanese)", which is allowed to use for AI training. - We have confirmed directly from the author that an exact equivalent license is not necesserily needed to distribute the trained model or to generate images. - Therefore, to avoid harmful content generation, the Creative Open Rail++-M license is applied to this dataset, and an equivalent or more restrictive license must be applied to its derivatives. ### Shader - MToon (MIT) with some modification by dev team. ### Other Textures for Skybox / Ground - Poly Haven (CC0) - ambientCG (CC0) ## Metadata Description The final caption is not provided in this dataset, but you can create complete caption from metadata. ### Color Shifting Color shift is used to create more diverse images. It is applied to skin/hair/eye/cloth/accesories independently. - Parameter xyz = (H_Shift, S_Factor, V_Factor) - New Color HSV = (H + H_Shift, S * S_Factor, V * V_Factor) ### Metadata Items - vrm_name : VRoid model name - clip_name : Pose Clip Number - camera_profile - facial_expression - lighting - lighting_color - outline - shade_toony - skin_profile - looking_label - camera_position : 3D position in meter - camera_rotation : Pitch/Yaw/Roll in degree - camera_fov : in degree - hair_color_shift : HSV color shift of hair - eye_color_shift : HSV color shift of eye - color_shift : HSV color shift of cloth and accesories - ground_plane_material - left_hand_sign - right_hand_sign - skybox ## Full Dataset This is a subset of full dataset consisting of approx. 600k images. Full dataset would be available upon request only for non-commercial research purposes. You will need to provide 1 TB of online storage so that we could upload the data set or send us an empty 1 TB physical hard drive to our office located in Tokyo Japan. Contact : info [at] URL ## Developed by - Abstract Engine dev team - Special Thanks to Mitsua Contributors - VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions.
[ "# VRoid Image Dataset Lite\n\nThis is a dataset to train text-to-image or other models without any copyright issue.\nAll materials used in this dataset are CC0 or properly licensed.\n\nThis dataset is also used to train Mitsua Diffusion One, which is a latent text-to-image diffusion model, whose VAE and U-Net are trained from scratch using only public domain/CC0 or copyright images with permission for use. \n\nVarious parameters such as camera angle, pose, skin color and facial expression were randomly set and the images were output.", "## Dataset License\nCreative Open-Rail++-M License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL++-M license further specifying rights and usage. The CreativeML OpenRAIL++-M License specifies:\n\n1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL++-M to all your users (please read the license entirely and carefully) Please read the full license here", "## Materials used in this dataset and their licenses", "### VRoid Models\n- VRM models used in this dataset are all CC0.\n- These models are made by VRoid Project\n - HairSample_Male\n - HairSample_Female\n - AvatarSample-D\n - AvatarSample-E\n - AvatarSample-F\n - AvatarSample-G\n - Sakurada Fumiriya\n - Sendagaya Shino\n- These models are made by pastelskies\n - 015\n - 009\n - 008\n- These models are made by yomox9\n - Qi\n- These models are made by くつした\n - 【CC0】オリジナルアバター「少女A」【Cluster想定】\n- These models are made by ろーてく\n - 【CC0】オリジナルアバター「シャペル」【VRChat想定】", "### Pose and motions\n- Our original poses.\n- Free edition pose subset in Unity Humanoid AnimationClip - PoseCollection made by かんな久＠ねここや様 (NOT CC0)\n - We have obtained permission directly from the author for training or distributing the AI model.\n - This dataset uses only a subset of the \"Free edition (ポーズ詰め合わせ（無料版）in Japanese)\", which is allowed to use for AI training.\n - We have confirmed directly from the author that an exact equivalent license is not necesserily needed to distribute the trained model or to generate images.\n - Therefore, to avoid harmful content generation, the Creative Open Rail++-M license is applied to this dataset, and an equivalent or more restrictive license must be applied to its derivatives.", "### Shader\n- MToon (MIT) with some modification by dev team.", "### Other Textures for Skybox / Ground\n- Poly Haven (CC0)\n- ambientCG (CC0)", "## Metadata Description\nThe final caption is not provided in this dataset, but you can create complete caption from metadata.", "### Color Shifting\nColor shift is used to create more diverse images. It is applied to skin/hair/eye/cloth/accesories independently.\n- Parameter xyz = (H_Shift, S_Factor, V_Factor)\n- New Color HSV = (H + H_Shift, S * S_Factor, V * V_Factor)", "### Metadata Items\n- vrm_name : VRoid model name\n- clip_name : Pose Clip Number\n- camera_profile\n- facial_expression\n- lighting\n- lighting_color\n- outline\n- shade_toony\n- skin_profile\n- looking_label\n- camera_position : 3D position in meter\n- camera_rotation : Pitch/Yaw/Roll in degree\n- camera_fov : in degree\n- hair_color_shift : HSV color shift of hair\n- eye_color_shift : HSV color shift of eye\n- color_shift : HSV color shift of cloth and accesories\n- ground_plane_material\n- left_hand_sign\n- right_hand_sign\n- skybox", "## Full Dataset\nThis is a subset of full dataset consisting of approx. 600k images.\n\nFull dataset would be available upon request only for non-commercial research purposes. \n\nYou will need to provide 1 TB of online storage so that we could upload the data set or send us an empty 1 TB physical hard drive to our office located in Tokyo Japan.\n\nContact : info [at] URL", "## Developed by\n- Abstract Engine dev team\n- Special Thanks to Mitsua Contributors\n\n- VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions." ]
[ "TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #language-Japanese #license-openrail++ #region-us \n", "# VRoid Image Dataset Lite\n\nThis is a dataset to train text-to-image or other models without any copyright issue.\nAll materials used in this dataset are CC0 or properly licensed.\n\nThis dataset is also used to train Mitsua Diffusion One, which is a latent text-to-image diffusion model, whose VAE and U-Net are trained from scratch using only public domain/CC0 or copyright images with permission for use. \n\nVarious parameters such as camera angle, pose, skin color and facial expression were randomly set and the images were output.", "## Dataset License\nCreative Open-Rail++-M License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL++-M license further specifying rights and usage. The CreativeML OpenRAIL++-M License specifies:\n\n1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL++-M to all your users (please read the license entirely and carefully) Please read the full license here", "## Materials used in this dataset and their licenses", "### VRoid Models\n- VRM models used in this dataset are all CC0.\n- These models are made by VRoid Project\n - HairSample_Male\n - HairSample_Female\n - AvatarSample-D\n - AvatarSample-E\n - AvatarSample-F\n - AvatarSample-G\n - Sakurada Fumiriya\n - Sendagaya Shino\n- These models are made by pastelskies\n - 015\n - 009\n - 008\n- These models are made by yomox9\n - Qi\n- These models are made by くつした\n - 【CC0】オリジナルアバター「少女A」【Cluster想定】\n- These models are made by ろーてく\n - 【CC0】オリジナルアバター「シャペル」【VRChat想定】", "### Pose and motions\n- Our original poses.\n- Free edition pose subset in Unity Humanoid AnimationClip - PoseCollection made by かんな久＠ねここや様 (NOT CC0)\n - We have obtained permission directly from the author for training or distributing the AI model.\n - This dataset uses only a subset of the \"Free edition (ポーズ詰め合わせ（無料版）in Japanese)\", which is allowed to use for AI training.\n - We have confirmed directly from the author that an exact equivalent license is not necesserily needed to distribute the trained model or to generate images.\n - Therefore, to avoid harmful content generation, the Creative Open Rail++-M license is applied to this dataset, and an equivalent or more restrictive license must be applied to its derivatives.", "### Shader\n- MToon (MIT) with some modification by dev team.", "### Other Textures for Skybox / Ground\n- Poly Haven (CC0)\n- ambientCG (CC0)", "## Metadata Description\nThe final caption is not provided in this dataset, but you can create complete caption from metadata.", "### Color Shifting\nColor shift is used to create more diverse images. It is applied to skin/hair/eye/cloth/accesories independently.\n- Parameter xyz = (H_Shift, S_Factor, V_Factor)\n- New Color HSV = (H + H_Shift, S * S_Factor, V * V_Factor)", "### Metadata Items\n- vrm_name : VRoid model name\n- clip_name : Pose Clip Number\n- camera_profile\n- facial_expression\n- lighting\n- lighting_color\n- outline\n- shade_toony\n- skin_profile\n- looking_label\n- camera_position : 3D position in meter\n- camera_rotation : Pitch/Yaw/Roll in degree\n- camera_fov : in degree\n- hair_color_shift : HSV color shift of hair\n- eye_color_shift : HSV color shift of eye\n- color_shift : HSV color shift of cloth and accesories\n- ground_plane_material\n- left_hand_sign\n- right_hand_sign\n- skybox", "## Full Dataset\nThis is a subset of full dataset consisting of approx. 600k images.\n\nFull dataset would be available upon request only for non-commercial research purposes. \n\nYou will need to provide 1 TB of online storage so that we could upload the data set or send us an empty 1 TB physical hard drive to our office located in Tokyo Japan.\n\nContact : info [at] URL", "## Developed by\n- Abstract Engine dev team\n- Special Thanks to Mitsua Contributors\n\n- VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions." ]
[ 47, 124, 195, 12, 168, 177, 17, 23, 25, 85, 159, 85, 42 ]
[ "passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #language-Japanese #license-openrail++ #region-us \n# VRoid Image Dataset Lite\n\nThis is a dataset to train text-to-image or other models without any copyright issue.\nAll materials used in this dataset are CC0 or properly licensed.\n\nThis dataset is also used to train Mitsua Diffusion One, which is a latent text-to-image diffusion model, whose VAE and U-Net are trained from scratch using only public domain/CC0 or copyright images with permission for use. \n\nVarious parameters such as camera angle, pose, skin color and facial expression were randomly set and the images were output.## Dataset License\nCreative Open-Rail++-M License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL++-M license further specifying rights and usage. The CreativeML OpenRAIL++-M License specifies:\n\n1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL++-M to all your users (please read the license entirely and carefully) Please read the full license here## Materials used in this dataset and their licenses", "passage: ### VRoid Models\n- VRM models used in this dataset are all CC0.\n- These models are made by VRoid Project\n - HairSample_Male\n - HairSample_Female\n - AvatarSample-D\n - AvatarSample-E\n - AvatarSample-F\n - AvatarSample-G\n - Sakurada Fumiriya\n - Sendagaya Shino\n- These models are made by pastelskies\n - 015\n - 009\n - 008\n- These models are made by yomox9\n - Qi\n- These models are made by くつした\n - 【CC0】オリジナルアバター「少女A」【Cluster想定】\n- These models are made by ろーてく\n - 【CC0】オリジナルアバター「シャペル」【VRChat想定】### Pose and motions\n- Our original poses.\n- Free edition pose subset in Unity Humanoid AnimationClip - PoseCollection made by かんな久＠ねここや様 (NOT CC0)\n - We have obtained permission directly from the author for training or distributing the AI model.\n - This dataset uses only a subset of the \"Free edition (ポーズ詰め合わせ（無料版）in Japanese)\", which is allowed to use for AI training.\n - We have confirmed directly from the author that an exact equivalent license is not necesserily needed to distribute the trained model or to generate images.\n - Therefore, to avoid harmful content generation, the Creative Open Rail++-M license is applied to this dataset, and an equivalent or more restrictive license must be applied to its derivatives.### Shader\n- MToon (MIT) with some modification by dev team.### Other Textures for Skybox / Ground\n- Poly Haven (CC0)\n- ambientCG (CC0)## Metadata Description\nThe final caption is not provided in this dataset, but you can create complete caption from metadata.### Color Shifting\nColor shift is used to create more diverse images. It is applied to skin/hair/eye/cloth/accesories independently.\n- Parameter xyz = (H_Shift, S_Factor, V_Factor)\n- New Color HSV = (H + H_Shift, S * S_Factor, V * V_Factor)### Metadata Items\n- vrm_name : VRoid model name\n- clip_name : Pose Clip Number\n- camera_profile\n- facial_expression\n- lighting\n- lighting_color\n- outline\n- shade_toony\n- skin_profile\n- looking_label\n- camera_position : 3D position in meter\n- camera_rotation : Pitch/Yaw/Roll in degree\n- camera_fov : in degree\n- hair_color_shift : HSV color shift of hair\n- eye_color_shift : HSV color shift of eye\n- color_shift : HSV color shift of cloth and accesories\n- ground_plane_material\n- left_hand_sign\n- right_hand_sign\n- skybox" ]
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befda45d8d4e8b8082bb8a1912d1f9eb9483991c
![GLAMI-1M Image](https://raw.githubusercontent.com/glami/glami-1m/main/media/glami-1m-dataset-examples.png) GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem. Each sample contains an image, country code, name in corresponding language, description, target category and source of the label which can be of multiple types, it can be human or rule-based but most of the samples are human-based labels. Read more on [GLAMI-1M home page at GitHub](https://github.com/glami/glami-1m)
glami/glami-1m
[ "license:apache-2.0", "region:us" ]
2023-02-09T12:47:17+00:00
{"license": "apache-2.0"}
2023-02-10T08:11:55+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
!GLAMI-1M Image GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem. Each sample contains an image, country code, name in corresponding language, description, target category and source of the label which can be of multiple types, it can be human or rule-based but most of the samples are human-based labels. Read more on GLAMI-1M home page at GitHub
[]
[ "TAGS\n#license-apache-2.0 #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
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51dadf57bfdf335e71dee740bdca4c0d3d288df1
# Dataset Card for "Hansard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nadav/Hansard
[ "region:us" ]
2023-02-09T13:32:10+00:00
{"dataset_info": {"features": [{"name": "sentence_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "decade", "dtype": "string"}, {"name": "speechdate", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3568239600, "num_examples": 8074848}, {"name": "test", "num_bytes": 398954434, "num_examples": 897205}], "download_size": 1234278574, "dataset_size": 3967194034}}
2023-02-09T13:34:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hansard" More Information needed
[ "# Dataset Card for \"Hansard\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hansard\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hansard\"\n\nMore Information needed" ]
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294441fae4c22275c33e3fa368e694b4e71d7e2a
# Dataset Card for "Clemt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nadav/Clemt
[ "region:us" ]
2023-02-09T13:56:46+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 180028095, "num_examples": 300}, {"name": "test", "num_bytes": 18814514, "num_examples": 33}], "download_size": 117182541, "dataset_size": 198842609}}
2023-02-09T13:57:06+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Clemt" More Information needed
[ "# Dataset Card for \"Clemt\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Clemt\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Clemt\"\n\nMore Information needed" ]
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e062bf9ffba7bb193cdba2802c557fe8090401d6
# Dataset Card for "testimdb1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
abhishek/testimdb1
[ "region:us" ]
2023-02-09T14:09:03+00:00
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 66283508, "num_examples": 50000}], "download_size": 42961480, "dataset_size": 66283508}}
2023-02-09T14:09:29+00:00
[]
[]
TAGS #region-us
# Dataset Card for "testimdb1" More Information needed
[ "# Dataset Card for \"testimdb1\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"testimdb1\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"testimdb1\"\n\nMore Information needed" ]
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37fed152b283cc3dc1dc9ab313d4ea9bfc92cd60
# Dataset Card for "davinci-vs-lit-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/davinci-vs-lit-pairwise
[ "region:us" ]
2023-02-09T14:10:08+00:00
{"dataset_info": {"features": [{"name": "davinci", "dtype": "string"}, {"name": "lit", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "api_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1845380427, "num_examples": 47954}], "download_size": 809346083, "dataset_size": 1845380427}}
2023-02-10T12:02:21+00:00
[]
[]
TAGS #region-us
# Dataset Card for "davinci-vs-lit-pairwise" More Information needed
[ "# Dataset Card for \"davinci-vs-lit-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"davinci-vs-lit-pairwise\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"davinci-vs-lit-pairwise\"\n\nMore Information needed" ]
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25eac818b32ac9657363088400cab47ff019d7ac
# Dataset Card for "whisper_mix_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thanhduycao/whisper_mix_data
[ "region:us" ]
2023-02-09T14:31:01+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1287134674.425454, "num_examples": 8373}, {"name": "test", "num_bytes": 540858435.8587947, "num_examples": 1903}], "download_size": 1785316365, "dataset_size": 1827993110.2842486}}
2023-02-09T14:32:44+00:00
[]
[]
TAGS #region-us
# Dataset Card for "whisper_mix_data" More Information needed
[ "# Dataset Card for \"whisper_mix_data\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"whisper_mix_data\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"whisper_mix_data\"\n\nMore Information needed" ]
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b2fbc10431b721dc9b0409b716d56a759d1cf332
# Dataset Card for "vivos_ng_only" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thanhduycao/vivos_ng_only
[ "region:us" ]
2023-02-09T14:41:51+00:00
{"dataset_info": {"features": [{"name": "speaker_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8858003.732418524, "num_examples": 60}, {"name": "test", "num_bytes": 453063.74736842106, "num_examples": 4}], "download_size": 717197, "dataset_size": 9311067.479786946}}
2023-02-09T14:42:53+00:00
[]
[]
TAGS #region-us
# Dataset Card for "vivos_ng_only" More Information needed
[ "# Dataset Card for \"vivos_ng_only\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"vivos_ng_only\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"vivos_ng_only\"\n\nMore Information needed" ]
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ad2a0fdfdffed33ec5e6424728addc6bedfede6a
# Dataset Card for "QADatasetForPatho" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Galahad3x/QADatasetForPatho
[ "region:us" ]
2023-02-09T14:43:19+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 35253904, "num_examples": 1567}, {"name": "test", "num_bytes": 9123066, "num_examples": 339}], "download_size": 655278, "dataset_size": 44376970}}
2023-02-09T15:13:03+00:00
[]
[]
TAGS #region-us
# Dataset Card for "QADatasetForPatho" More Information needed
[ "# Dataset Card for \"QADatasetForPatho\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"QADatasetForPatho\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"QADatasetForPatho\"\n\nMore Information needed" ]
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e0194203f77ab05bdccca4f1b197ced598100206
# Satvision Pretraining Dataset - Small - **Developed by:** NASA GSFC CISTO Data Science Group - **Model type:** Pre-trained visual transformer model - **License:** Apache license 2.0 This dataset repository houses the pretraining data for the Satvision pretrained transformers. This dataset was constructed using [webdatasets](https://github.com/webdataset/webdataset) to limit the number of inodes used in HPC systems with limited shared storage. Each file has 100000 tiles, with pairs of image input and annotation. The data has been further compressed to ease the download from HuggingFace. SatelliteVision-Base (SatVis-B) is a pre-trained vision transformer based on the SwinV2 mode architecture. The model is pre-trained on global MODIS surface reflectance data from which 1.99 million image chips were used. SatVis-B is pre-trained using the masked-image-modeling (MIM) contrastive pre-training strategy. The MIM pre-training approach utilizes random masking of the input geospatial image chip, using a linear layer to regress the raw pixel values of the masked area with an l1 loss serving as the loss function. Resolution of the pre-training MODIS chips was `128x128` with a window size of `16x16`. SatViz-B was pre-trained for `800` epochs on 8x A100 GPUs and 12x V100 GPUs. ### SatVision Transformer **Pre-trained models pre-trained on MODIS-Small dataset** | name | pre-train epochs | pre-train resolution | #params | pre-trained model | | :---: | :---: | :---: | :---: | :---: | | SatVision-Base | 800 | 128x128 | 84.5m | [checkpoint](https://huggingface.co/nasa-cisto-data-science-group/satvision-base/blob/main/ckpt_epoch_800.pth)/[config](https://github.com/nasa-nccs-hpda/pytorch-caney/blob/develop/examples/satvision/mim_pretrain_swinv2_satvision_base_192_window12_800ep.yaml) | ## Getting Started with SatVision-Base - **Training repository:** https://github.com/nasa-nccs-hpda/pytorch-caney - **Pre-training dataset repository:** https://huggingface.co/datasets/nasa-cisto-data-science-group/satvision-pretrain-small ### Installation If you have singularity installed ```bash $ git clone [email protected]:nasa-nccs-hpda/pytorch-caney.git $ singularity build --sandbox pytorch-caney.sif docker://nasanccs/pytorch-caney:latest # To shell into the container $ singularity shell --nv -B <mounts> pytorch-caney.sif ``` Anaconda installation ```bash $ git clone [email protected]:nasa-nccs-hpda/pytorch-caney.git $ conda create -n satvision-env python==3.9 ``` ### Fine-tuning Satvision-Base - Create config file [example config](https://github.com/nasa-nccs-hpda/pytorch-caney/blob/finetuning/examples/satvision/finetune_satvision_base_landcover5class_192_window12_100ep.yaml) - Download checkpoint from this HF model repo - `$ git clone [email protected]:nasa-nccs-hpda/pytorch-caney.git` - Add a new pytorch dataset in pytorch-caney/pytorch_caney/data/datasets/ - Add new pytorch dataset to dict in pytorch-caney/pytorch_caney/data/datamodules/finetune_datamodule.py ```bash torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/finetuning/finetune.py --cfg <config-file> --pretrained <path-to-pretrained> --dataset <dataset-name (key for new dataset)> --data-paths <path-to-data-dir> --batch-size <batch-size> --output <output-dir> --enable-amp ``` ### Pre-training with pytorch-caney ## Pre-training with SatVision-Base with Masked Image Modeling and pytorch-caney To pre-train the swinv2 base model with masked image modeling pre-training, run: ```bash torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg <config-file> --dataset <dataset-name> --data-paths <path-to-data-subfolder-1> --batch-size <batch-size> --output <output-dir> --enable-amp ``` For example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run: ```bash singularity shell --nv -B <mounts> /path/to/container/pytorch-caney-container Singularity> export PYTHONPATH=$PWD:$PWD/pytorch-caney Singularity> torchrun --nproc_per_node 4 pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg pytorch-caney/examples/satvision/mim_pretrain_swinv2_satvision_base_192_window12_800ep.yaml --dataset MODIS --data-paths /explore/nobackup/projects/ilab/data/satvision/pretraining/training_* --batch-size 128 --output . --enable-amp ``` ## SatVision-Base Pre-Training Datasets | name | bands | resolution | #chips | meters-per-pixel | | :---: | :---: | :---: | :---: | :---: | | MODIS-Small | 7 | 128x128 | 1,994,131 | 500m | ## Citing SatVision-Base If this model helped your research, please cite `satvision-base` in your publications. ``` @misc{satvision-base, author = {Carroll, Mark and Li, Jian and Spradlin, Caleb and Caraballo-Vega, Jordan}, doi = {10.57967/hf/1017}, month = aug, title = {{satvision-base}}, url = {https://huggingface.co/nasa-cisto-data-science-group/satvision-base}, repository-code = {https://github.com/nasa-nccs-hpda/pytorch-caney} year = {2023} } ```
nasa-cisto-data-science-group/satvision-pretrain-small
[ "language:en", "license:apache-2.0", "region:us" ]
2023-02-09T14:55:44+00:00
{"language": ["en"], "license": "apache-2.0"}
2023-08-31T00:38:48+00:00
[]
[ "en" ]
TAGS #language-English #license-apache-2.0 #region-us
Satvision Pretraining Dataset - Small ===================================== * Developed by: NASA GSFC CISTO Data Science Group * Model type: Pre-trained visual transformer model * License: Apache license 2.0 This dataset repository houses the pretraining data for the Satvision pretrained transformers. This dataset was constructed using webdatasets to limit the number of inodes used in HPC systems with limited shared storage. Each file has 100000 tiles, with pairs of image input and annotation. The data has been further compressed to ease the download from HuggingFace. SatelliteVision-Base (SatVis-B) is a pre-trained vision transformer based on the SwinV2 mode architecture. The model is pre-trained on global MODIS surface reflectance data from which 1.99 million image chips were used. SatVis-B is pre-trained using the masked-image-modeling (MIM) contrastive pre-training strategy. The MIM pre-training approach utilizes random masking of the input geospatial image chip, using a linear layer to regress the raw pixel values of the masked area with an l1 loss serving as the loss function. Resolution of the pre-training MODIS chips was '128x128' with a window size of '16x16'. SatViz-B was pre-trained for '800' epochs on 8x A100 GPUs and 12x V100 GPUs. ### SatVision Transformer Pre-trained models pre-trained on MODIS-Small dataset Getting Started with SatVision-Base ----------------------------------- * Training repository: URL * Pre-training dataset repository: URL ### Installation If you have singularity installed Anaconda installation ### Fine-tuning Satvision-Base * Create config file example config * Download checkpoint from this HF model repo * '$ git clone git@URL:nasa-nccs-hpda/URL' * Add a new pytorch dataset in pytorch-caney/pytorch\_caney/data/datasets/ * Add new pytorch dataset to dict in pytorch-caney/pytorch\_caney/data/datamodules/finetune\_datamodule.py ### Pre-training with pytorch-caney Pre-training with SatVision-Base with Masked Image Modeling and pytorch-caney ----------------------------------------------------------------------------- To pre-train the swinv2 base model with masked image modeling pre-training, run: For example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run: SatVision-Base Pre-Training Datasets ------------------------------------ Citing SatVision-Base --------------------- If this model helped your research, please cite 'satvision-base' in your publications.
[ "### SatVision Transformer\n\n\nPre-trained models pre-trained on MODIS-Small dataset\n\n\n\nGetting Started with SatVision-Base\n-----------------------------------\n\n\n* Training repository: URL\n* Pre-training dataset repository: URL", "### Installation\n\n\nIf you have singularity installed\n\n\nAnaconda installation", "### Fine-tuning Satvision-Base\n\n\n* Create config file example config\n* Download checkpoint from this HF model repo\n* '$ git clone git@URL:nasa-nccs-hpda/URL'\n* Add a new pytorch dataset in pytorch-caney/pytorch\\_caney/data/datasets/\n* Add new pytorch dataset to dict in pytorch-caney/pytorch\\_caney/data/datamodules/finetune\\_datamodule.py", "### Pre-training with pytorch-caney\n\n\nPre-training with SatVision-Base with Masked Image Modeling and pytorch-caney\n-----------------------------------------------------------------------------\n\n\nTo pre-train the swinv2 base model with masked image modeling pre-training, run:\n\n\nFor example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run:\n\n\nSatVision-Base Pre-Training Datasets\n------------------------------------\n\n\n\nCiting SatVision-Base\n---------------------\n\n\nIf this model helped your research, please cite 'satvision-base' in your publications." ]
[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "### SatVision Transformer\n\n\nPre-trained models pre-trained on MODIS-Small dataset\n\n\n\nGetting Started with SatVision-Base\n-----------------------------------\n\n\n* Training repository: URL\n* Pre-training dataset repository: URL", "### Installation\n\n\nIf you have singularity installed\n\n\nAnaconda installation", "### Fine-tuning Satvision-Base\n\n\n* Create config file example config\n* Download checkpoint from this HF model repo\n* '$ git clone git@URL:nasa-nccs-hpda/URL'\n* Add a new pytorch dataset in pytorch-caney/pytorch\\_caney/data/datasets/\n* Add new pytorch dataset to dict in pytorch-caney/pytorch\\_caney/data/datamodules/finetune\\_datamodule.py", "### Pre-training with pytorch-caney\n\n\nPre-training with SatVision-Base with Masked Image Modeling and pytorch-caney\n-----------------------------------------------------------------------------\n\n\nTo pre-train the swinv2 base model with masked image modeling pre-training, run:\n\n\nFor example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run:\n\n\nSatVision-Base Pre-Training Datasets\n------------------------------------\n\n\n\nCiting SatVision-Base\n---------------------\n\n\nIf this model helped your research, please cite 'satvision-base' in your publications." ]
[ 18, 53, 14, 124, 152 ]
[ "passage: TAGS\n#language-English #license-apache-2.0 #region-us \n### SatVision Transformer\n\n\nPre-trained models pre-trained on MODIS-Small dataset\n\n\n\nGetting Started with SatVision-Base\n-----------------------------------\n\n\n* Training repository: URL\n* Pre-training dataset repository: URL### Installation\n\n\nIf you have singularity installed\n\n\nAnaconda installation### Fine-tuning Satvision-Base\n\n\n* Create config file example config\n* Download checkpoint from this HF model repo\n* '$ git clone git@URL:nasa-nccs-hpda/URL'\n* Add a new pytorch dataset in pytorch-caney/pytorch\\_caney/data/datasets/\n* Add new pytorch dataset to dict in pytorch-caney/pytorch\\_caney/data/datamodules/finetune\\_datamodule.py### Pre-training with pytorch-caney\n\n\nPre-training with SatVision-Base with Masked Image Modeling and pytorch-caney\n-----------------------------------------------------------------------------\n\n\nTo pre-train the swinv2 base model with masked image modeling pre-training, run:\n\n\nFor example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run:\n\n\nSatVision-Base Pre-Training Datasets\n------------------------------------\n\n\n\nCiting SatVision-Base\n---------------------\n\n\nIf this model helped your research, please cite 'satvision-base' in your publications." ]
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0a63f6f8433496978efefb90416376832f25fbb7
# Dataset Card for "whisper_mix_data_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thanhduycao/whisper_mix_data_v2
[ "region:us" ]
2023-02-09T15:10:51+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1287134674.425454, "num_examples": 8373}, {"name": "test", "num_bytes": 540858435.8587947, "num_examples": 1903}], "download_size": 1785351931, "dataset_size": 1827993110.2842486}}
2023-02-09T15:12:28+00:00
[]
[]
TAGS #region-us
# Dataset Card for "whisper_mix_data_v2" More Information needed
[ "# Dataset Card for \"whisper_mix_data_v2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"whisper_mix_data_v2\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"whisper_mix_data_v2\"\n\nMore Information needed" ]
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e827cf62747fe23bd20759e7f2be8b7fec78c573
# Dataset Card for "binhvq-news-corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hieunguyen1053/binhvq-news-corpus
[ "region:us" ]
2023-02-09T15:33:41+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51179763136, "num_examples": 13954498}], "download_size": 19065155948, "dataset_size": 51179763136}}
2023-02-09T15:49:42+00:00
[]
[]
TAGS #region-us
# Dataset Card for "binhvq-news-corpus" More Information needed
[ "# Dataset Card for \"binhvq-news-corpus\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"binhvq-news-corpus\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"binhvq-news-corpus\"\n\nMore Information needed" ]
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12ee32c1971c11b429da8e4eb2099941847e83c8
# Indonesian Instructions Dataset
cahya/instructions_indonesian
[ "license:mit", "region:us" ]
2023-02-09T16:34:47+00:00
{"license": "mit"}
2023-02-09T17:03:53+00:00
[]
[]
TAGS #license-mit #region-us
# Indonesian Instructions Dataset
[ "# Indonesian Instructions Dataset" ]
[ "TAGS\n#license-mit #region-us \n", "# Indonesian Instructions Dataset" ]
[ 11, 8 ]
[ "passage: TAGS\n#license-mit #region-us \n# Indonesian Instructions Dataset" ]
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3347fc072b366e468feaa42b5ef9b99012b0b0d4
# Dataset Card for "binhvq-news-corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ademax/binhvq-news-corpus
[ "region:us" ]
2023-02-09T17:34:08+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 50813003038.15213, "num_examples": 13854498}, {"name": "test", "num_bytes": 366761776.84786654, "num_examples": 100000}], "download_size": 27332182633, "dataset_size": 51179764815.0}}
2023-02-09T18:49:26+00:00
[]
[]
TAGS #region-us
# Dataset Card for "binhvq-news-corpus" More Information needed
[ "# Dataset Card for \"binhvq-news-corpus\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"binhvq-news-corpus\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"binhvq-news-corpus\"\n\nMore Information needed" ]
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bd7e0dc3256a31e3dae03c41b756dddf3b947f20
The NEG-1500-SIMP-TEMP is the extension of NEG-136-SIMP. It is created using a template derived from original papers. If this dataset is useful to you please cite our work @article{shivagunde2023larger, title={Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning}, author={Shivagunde, Namrata and Lialin, Vladislav and Rumshisky, Anna}, journal={arXiv preprint arXiv:2303.16445}, year={2023} }
text-machine-lab/NEG-1500-SIMP-TEMP
[ "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]
2023-02-09T18:18:15+00:00
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"]}
2023-04-20T17:09:50+00:00
[]
[ "en" ]
TAGS #size_categories-1K<n<10K #language-English #license-mit #region-us
The NEG-1500-SIMP-TEMP is the extension of NEG-136-SIMP. It is created using a template derived from original papers. If this dataset is useful to you please cite our work @article{shivagunde2023larger, title={Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning}, author={Shivagunde, Namrata and Lialin, Vladislav and Rumshisky, Anna}, journal={arXiv preprint arXiv:2303.16445}, year={2023} }
[]
[ "TAGS\n#size_categories-1K<n<10K #language-English #license-mit #region-us \n" ]
[ 27 ]
[ "passage: TAGS\n#size_categories-1K<n<10K #language-English #license-mit #region-us \n" ]
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89da87baea593e32704df80a2776bb26916dc772
NEG-1500-SIMP-GEN is an extended version of NEG-136-SIMP. The dataset is extended using GPT-3. If this dataset is useful to you please cite our work @article{shivagunde2023larger, title={Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning}, author={Shivagunde, Namrata and Lialin, Vladislav and Rumshisky, Anna}, journal={arXiv preprint arXiv:2303.16445}, year={2023} }
text-machine-lab/NEG-1500-SIMP-GEN
[ "license:mit", "region:us" ]
2023-02-09T18:30:16+00:00
{"license": "mit"}
2023-04-20T17:09:28+00:00
[]
[]
TAGS #license-mit #region-us
NEG-1500-SIMP-GEN is an extended version of NEG-136-SIMP. The dataset is extended using GPT-3. If this dataset is useful to you please cite our work @article{shivagunde2023larger, title={Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning}, author={Shivagunde, Namrata and Lialin, Vladislav and Rumshisky, Anna}, journal={arXiv preprint arXiv:2303.16445}, year={2023} }
[]
[ "TAGS\n#license-mit #region-us \n" ]
[ 11 ]
[ "passage: TAGS\n#license-mit #region-us \n" ]
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5d964f929efa2a106edae5894c6c2f03d4b8d127
ROLE-1500 is the extended version of ROLE-88. The dataset is extended using GPT-3. If this dataset is useful to you please cite our work @article{shivagunde2023larger, title={Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning}, author={Shivagunde, Namrata and Lialin, Vladislav and Rumshisky, Anna}, journal={arXiv preprint arXiv:2303.16445}, year={2023} }
text-machine-lab/ROLE-1500
[ "license:mit", "region:us" ]
2023-02-09T18:33:21+00:00
{"license": "mit"}
2023-04-20T17:08:47+00:00
[]
[]
TAGS #license-mit #region-us
ROLE-1500 is the extended version of ROLE-88. The dataset is extended using GPT-3. If this dataset is useful to you please cite our work @article{shivagunde2023larger, title={Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning}, author={Shivagunde, Namrata and Lialin, Vladislav and Rumshisky, Anna}, journal={arXiv preprint arXiv:2303.16445}, year={2023} }
[]
[ "TAGS\n#license-mit #region-us \n" ]
[ 11 ]
[ "passage: TAGS\n#license-mit #region-us \n" ]
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b8888b24d84d93c82a0a407d2d72962796f05846
Basically used in Correctness Chorus to train T5 model to predict grammar correction.
Owishiboo/grammar-correction
[ "language:en", "region:us" ]
2023-02-09T19:18:01+00:00
{"language": ["en"]}
2023-02-09T19:19:44+00:00
[]
[ "en" ]
TAGS #language-English #region-us
Basically used in Correctness Chorus to train T5 model to predict grammar correction.
[]
[ "TAGS\n#language-English #region-us \n" ]
[ 10 ]
[ "passage: TAGS\n#language-English #region-us \n" ]
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71dfae610327807488396d01b519baf9aae483a3
## Dataset Description A dataset of pairs of TypeScript code to appropriate type declarations. ## Language TypeScript only. ## To Load ```python from datasets import load_dataset load_dataset("noahshinn024/ts-code2td") ``` ## Distribution of type declaration code lengths - uses the tokenizer from [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) ![](./media/declaration_token_distr.png)
noahshinn/ts-code2td
[ "license:mit", "region:us" ]
2023-02-09T19:26:58+00:00
{"license": "mit"}
2023-02-13T23:58:20+00:00
[]
[]
TAGS #license-mit #region-us
## Dataset Description A dataset of pairs of TypeScript code to appropriate type declarations. ## Language TypeScript only. ## To Load ## Distribution of type declaration code lengths - uses the tokenizer from bigcode/santacoder ![](./media/declaration_token_distr.png)
[ "## Dataset Description\nA dataset of pairs of TypeScript code to appropriate type declarations.", "## Language\nTypeScript only.", "## To Load", "## Distribution of type declaration code lengths\n- uses the tokenizer from bigcode/santacoder\n![](./media/declaration_token_distr.png)" ]
[ "TAGS\n#license-mit #region-us \n", "## Dataset Description\nA dataset of pairs of TypeScript code to appropriate type declarations.", "## Language\nTypeScript only.", "## To Load", "## Distribution of type declaration code lengths\n- uses the tokenizer from bigcode/santacoder\n![](./media/declaration_token_distr.png)" ]
[ 11, 20, 6, 4, 44 ]
[ "passage: TAGS\n#license-mit #region-us \n## Dataset Description\nA dataset of pairs of TypeScript code to appropriate type declarations.## Language\nTypeScript only.## To Load## Distribution of type declaration code lengths\n- uses the tokenizer from bigcode/santacoder\n![](./media/declaration_token_distr.png)" ]
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1a770059d5f6f6014fa414f65556f65f6c85c7fe
# Dataset Card for "arxiv-abstract-matching" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
karukas/arxiv-abstract-matching
[ "region:us" ]
2023-02-09T20:46:50+00:00
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7119340064, "num_examples": 203037}, {"name": "validation", "num_bytes": 216202656, "num_examples": 6436}, {"name": "test", "num_bytes": 216585242, "num_examples": 6440}], "download_size": 3635681697, "dataset_size": 7552127962}}
2023-02-09T20:48:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "arxiv-abstract-matching" More Information needed
[ "# Dataset Card for \"arxiv-abstract-matching\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"arxiv-abstract-matching\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"arxiv-abstract-matching\"\n\nMore Information needed" ]
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97a19fd85201ce3a0cc3627b6485457bc5285eb8
# Dataset Card for "DilbertDiffusionDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CSAle/DilbertDiffusionDataset
[ "region:us" ]
2023-02-09T21:13:21+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 530433.0, "num_examples": 7}], "download_size": 531640, "dataset_size": 530433.0}}
2023-02-09T22:57:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DilbertDiffusionDataset" More Information needed
[ "# Dataset Card for \"DilbertDiffusionDataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DilbertDiffusionDataset\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DilbertDiffusionDataset\"\n\nMore Information needed" ]
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9ae784d84163e9e45b5c8c54690f9c8f4db80179
# Dataset Card for "pubmed-abstract-matching" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
karukas/pubmed-abstract-matching
[ "region:us" ]
2023-02-09T21:18:08+00:00
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2237510856, "num_examples": 119924}, {"name": "validation", "num_bytes": 126574623, "num_examples": 6633}, {"name": "test", "num_bytes": 126357120, "num_examples": 6658}], "download_size": 1156008015, "dataset_size": 2490442599}}
2023-02-09T21:18:46+00:00
[]
[]
TAGS #region-us
# Dataset Card for "pubmed-abstract-matching" More Information needed
[ "# Dataset Card for \"pubmed-abstract-matching\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"pubmed-abstract-matching\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"pubmed-abstract-matching\"\n\nMore Information needed" ]
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7dbfdf54ffb75f10401e10c87319db5bad444415
# Dataset Card for "stormfront-small-textonly" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kastan/stormfront-small-textonly
[ "region:us" ]
2023-02-10T00:48:19+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8544511, "num_examples": 10000}, {"name": "test", "num_bytes": 352602, "num_examples": 791}], "download_size": 5539239, "dataset_size": 8897113}}
2023-02-10T00:48:28+00:00
[]
[]
TAGS #region-us
# Dataset Card for "stormfront-small-textonly" More Information needed
[ "# Dataset Card for \"stormfront-small-textonly\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"stormfront-small-textonly\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"stormfront-small-textonly\"\n\nMore Information needed" ]
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487e912c023414c19256cbae821a3dfa43a14478
# Dataset Card for "instructions-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cahya/instructions-test
[ "region:us" ]
2023-02-10T01:11:36+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16048, "num_examples": 22}], "download_size": 15127, "dataset_size": 16048}}
2023-02-10T01:11:45+00:00
[]
[]
TAGS #region-us
# Dataset Card for "instructions-test" More Information needed
[ "# Dataset Card for \"instructions-test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"instructions-test\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"instructions-test\"\n\nMore Information needed" ]
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c80d11d88ebb46b4717020638d345cc45a56a2df
# Dataset Card for "instructions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cahya/instructions
[ "region:us" ]
2023-02-10T01:24:43+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 71483925.44051038, "num_examples": 170485}, {"name": "test", "num_bytes": 3971585.428468864, "num_examples": 9472}, {"name": "validation", "num_bytes": 3971166.1310207574, "num_examples": 9471}], "download_size": 45997378, "dataset_size": 79426677.0}}
2023-02-10T21:02:35+00:00
[]
[]
TAGS #region-us
# Dataset Card for "instructions" More Information needed
[ "# Dataset Card for \"instructions\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"instructions\"\n\nMore Information needed" ]
[ 6, 12 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"instructions\"\n\nMore Information needed" ]
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aa7608a8de1c6bb71a1a7234e42d4f5d62e55e29
# Dataset Card for "stormfront-full-textonly" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kastan/stormfront-full-textonly
[ "region:us" ]
2023-02-10T02:06:48+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7021007551, "num_examples": 10458223}, {"name": "test", "num_bytes": 352602, "num_examples": 791}], "download_size": 4432257231, "dataset_size": 7021360153}}
2023-02-10T02:09:18+00:00
[]
[]
TAGS #region-us
# Dataset Card for "stormfront-full-textonly" More Information needed
[ "# Dataset Card for \"stormfront-full-textonly\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"stormfront-full-textonly\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"stormfront-full-textonly\"\n\nMore Information needed" ]
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04fe889ec1d92c80b92e38cc8252c6ae11e22207
# Dataset Card for "VALUE_wikitext2_been_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_been_done
[ "language:en", "region:us" ]
2023-02-10T03:53:01+00:00
{"language": "en", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 614492, "num_examples": 720}, {"name": "train", "num_bytes": 5110338, "num_examples": 5991}, {"name": "validation", "num_bytes": 558461, "num_examples": 673}], "download_size": 3699279, "dataset_size": 6283291}}
2023-08-02T23:08:33+00:00
[]
[ "en" ]
TAGS #language-English #region-us
# Dataset Card for "VALUE_wikitext2_been_done" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_been_done\"\n\nMore Information needed" ]
[ "TAGS\n#language-English #region-us \n", "# Dataset Card for \"VALUE_wikitext2_been_done\"\n\nMore Information needed" ]
[ 10, 21 ]
[ "passage: TAGS\n#language-English #region-us \n# Dataset Card for \"VALUE_wikitext2_been_done\"\n\nMore Information needed" ]
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1103f6fcec810fafe4862064dd496c176e462bee
# Dataset Card for "VALUE_wikitext2_dey_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_dey_it
[ "region:us" ]
2023-02-10T03:59:18+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 121545, "num_examples": 139}, {"name": "train", "num_bytes": 1066585, "num_examples": 1212}, {"name": "validation", "num_bytes": 102624, "num_examples": 126}], "download_size": 809771, "dataset_size": 1290754}}
2023-02-10T03:59:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_dey_it" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_dey_it\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_dey_it\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_dey_it\"\n\nMore Information needed" ]
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93d41921cd54a117d6d539a00d30bcffe4c04971
# Dataset Card for "VALUE_wikitext2_drop_aux" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_drop_aux
[ "region:us" ]
2023-02-10T04:05:59+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 287459, "num_examples": 386}, {"name": "train", "num_bytes": 2899414, "num_examples": 3888}, {"name": "validation", "num_bytes": 235138, "num_examples": 340}], "download_size": 2054815, "dataset_size": 3422011}}
2023-02-10T04:06:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_drop_aux" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_drop_aux\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_drop_aux\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_drop_aux\"\n\nMore Information needed" ]
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7fefb2dc5f3a3c44b3e3fa5c795fd66f9230b39e
# Dataset Card for "VALUE_wikitext2_got" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_got
[ "region:us" ]
2023-02-10T04:12:14+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 96781, "num_examples": 118}, {"name": "train", "num_bytes": 947742, "num_examples": 1195}, {"name": "validation", "num_bytes": 78369, "num_examples": 91}], "download_size": 705607, "dataset_size": 1122892}}
2023-02-10T04:12:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_got" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_got\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_got\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_got\"\n\nMore Information needed" ]
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5177335a6d6f3f2879a06f4db778bbe08ea4c6ec
# Dataset Card for "VALUE_wikitext2_negative_concord" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_negative_concord
[ "region:us" ]
2023-02-10T04:36:14+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 165495, "num_examples": 178}, {"name": "train", "num_bytes": 1546197, "num_examples": 1691}, {"name": "validation", "num_bytes": 152679, "num_examples": 173}], "download_size": 1160295, "dataset_size": 1864371}}
2023-02-10T04:36:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_negative_concord" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_negative_concord\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_negative_concord\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_negative_concord\"\n\nMore Information needed" ]
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8a1eca48c9ab3699cba0f6540320e4aeb691885b
# Dataset Card for "VALUE_wikitext2_null_genetive" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_null_genetive
[ "region:us" ]
2023-02-10T04:48:50+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 631888, "num_examples": 784}, {"name": "train", "num_bytes": 6202950, "num_examples": 7653}, {"name": "validation", "num_bytes": 625986, "num_examples": 793}], "download_size": 4380528, "dataset_size": 7460824}}
2023-02-10T04:48:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_null_genetive" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_null_genetive\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_null_genetive\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_null_genetive\"\n\nMore Information needed" ]
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b5c405468ced93af422b8120e8566cd6e808d07c
# Dataset Card for "VALUE_wikitext2_null_relcl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_null_relcl
[ "region:us" ]
2023-02-10T04:55:10+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 455656, "num_examples": 507}, {"name": "train", "num_bytes": 3975445, "num_examples": 4605}, {"name": "validation", "num_bytes": 393700, "num_examples": 465}], "download_size": 2915183, "dataset_size": 4824801}}
2023-02-10T04:55:15+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_null_relcl" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_null_relcl\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_null_relcl\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_null_relcl\"\n\nMore Information needed" ]
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f7c9a7bfcb4d15412c89c98b0e1e5bbd564a1b77
# Dataset Card for "VALUE_wikitext2_uninflect" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_uninflect
[ "region:us" ]
2023-02-10T05:01:49+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 826318, "num_examples": 1068}, {"name": "train", "num_bytes": 7759024, "num_examples": 9991}, {"name": "validation", "num_bytes": 804972, "num_examples": 1053}], "download_size": 5470745, "dataset_size": 9390314}}
2023-02-10T05:01:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_uninflect" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_uninflect\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_uninflect\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_uninflect\"\n\nMore Information needed" ]
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9ff52cae9c6c8c40e28d78f38cc5b8bc8cc47f4a
# Dataset Card for SentiCoref ### Dataset Summary SentiCoref is a Slovenian coreference resolution dataset containing **391962** tokens inside **756** documents*. Also contains automatically (?) annotated named entities and manually verified lemmas and morphosyntactic tags (MSD). \* This is the latest version of SentiCoref, contained in SUK: Slovenian training corpus. ### Supported Tasks and Leaderboards Coreference resolution. ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the dataset, with most actual data truncated for the purpose of clarity: ``` { "id_doc": "senticoref3408", "words": [ [ ["Ljubljana", "-", "Upravi", "trgovske", "družbe", "Mercator", "se", "je", "z", "letom", "2010", ...], ... ], ... ], "lemmas": [ [ ["Ljubljana", "-", "uprava", "trgovski", "družba", "Mercator", "se", "biti", "z", "leto", "2010", ...], ... ], ... ], "msds": [ [ ["mte:Slzei", "mte:U", "mte:Sozed", "mte:Ppnzer", "mte:Sozer", "mte:Slmei", "mte:Zp------k", ...], ... ], ... ], "ne_tags": [ [ ["B-LOC", "O", "O", "O", "O", "B-ORG", "O", "O", ...], ... ], ... ], "mentions": [ { "id_mention": "senticoref3408.1.1.ne1", "mention_data": { "idx_par": 0, "idx_sent": 0, "word_indices": [0], "global_word_indices": [0] } }, ... ], "coref_clusters": [ ["senticoref3408.1.1.phr17-1", "senticoref3408.1.2.t7", "senticoref3408.1.2.ne2", "senticoref3408.1.4.ne3"], ... ] } ``` ### Data Fields Please note that documents are represented as lists of paragraphs, which are in turn lists of words. This means that `words`, `lemmas`, `msds`, and `ne_tags` are of type List[List[List[string]]]. This is done because it is easier to discard the segmentation information than re-obtain it. - `id_doc`: the identifier of the document; - `words`: words in the document; - `lemmas`: lemmas in the document; - `msds`: [morphosyntactic tags](https://nl.ijs.si/ME/V6/msd/) in the document; - `ne_tags`: named entity annotations in IOB2 format; - `mentions`: list of entity mentions in the document. Includes named entities, phrases, and single words (e.g., pronouns). Each mention is represented with its ID and the indices of contained words: either (1) the index of the paragraph, the sentence inside the paragraph, and the positions inside the sentence, or (2) the global word index that can be used on a flattened list of document words; - `coref_clusters`: coreference clusters present in the document. Each list represents one cluster of entity mentions, represented by their IDs ## Additional Information ### Using the dataset 1. Unless you are doing something more sophisticated, feel free to drop the paragraph and sentence segmentation information by flattening the document words, lemmas, MSDs, and named entity tags: ```python import datasets data = datasets.load_dataset("cjvt/senticoref", split="train") doc = data[0] flattened_words = [w for par in doc["words"] for sent in par for w in sent] # ... Do the same for other fields ``` 2. To get a better understanding of the entity mentions and coreference clusters in the document, you can convert the mention information into a dictionary and link the mentions and clusters to the actual words. ```python import datasets data = datasets.load_dataset("cjvt/senticoref", split="train") doc = data[0] flattened_words = [w for par in doc["words"] for sent in par for w in sent] id2mentiondata = {} for mention in doc["mentions"]: id2mentiondata[mention['id_mention']] = mention['mention_data'] # Display the entity mention clusters in the string format # (1) Using the flattened document structure and global word indices for cluster in doc["coref_clusters"]: print("{") for id_mention in cluster: print(f"\t{[flattened_words[_i] for _i in id2mentiondata[id_mention]['global_word_indices']]}") print("}") print("") # (2) Using the initial document structure and local word indices for cluster in doc["coref_clusters"]: print("{") for id_mention in cluster: _mention_data = id2mentiondata[id_mention] idx_par, idx_sent = _mention_data["idx_par"], _mention_data["idx_sent"] print(f"\t{[doc['words'][idx_par][idx_sent][_i] for _i in _mention_data['word_indices']]}") print("}") print("") ``` **Output:** ``` ... { ['trgovske', 'družbe', 'Mercator'] ['družbe'] ['Mercator'] ['Mercatorja'] } { ['letom', '2010'] ['leta', '2010'] } ... (truncated) ``` ### Dataset Curators Špela Arhar Holdt; et al. (please see http://hdl.handle.net/11356/1747 for the full list) ### Licensing Information CC BY-SA 4.0. ### Citation Information ``` @misc{suk, title = {Training corpus {SUK} 1.0}, author = {Arhar Holdt, {\v S}pela and Krek, Simon and Dobrovoljc, Kaja and Erjavec, Toma{\v z} and Gantar, Polona and {\v C}ibej, Jaka and Pori, Eva and Ter{\v c}on, Luka and Munda, Tina and {\v Z}itnik, Slavko and Robida, Nejc and Blagus, Neli and Mo{\v z}e, Sara and Ledinek, Nina and Holz, Nanika and Zupan, Katja and Kuzman, Taja and Kav{\v c}i{\v c}, Teja and {\v S}krjanec, Iza and Marko, Dafne and Jezer{\v s}ek, Lucija and Zajc, Anja}, url = {http://hdl.handle.net/11356/1747}, note = {Slovenian language resource repository {CLARIN}.{SI}}, year = {2022} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
cjvt/senticoref
[ "task_categories:token-classification", "size_categories:n<1K", "language:sl", "license:cc-by-sa-4.0", "coreference resolution", "region:us" ]
2023-02-10T10:07:38+00:00
{"language": ["sl"], "license": "cc-by-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["token-classification"], "pretty_name": "SentiCoref", "tags": ["coreference resolution"]}
2023-02-10T18:30:01+00:00
[]
[ "sl" ]
TAGS #task_categories-token-classification #size_categories-n<1K #language-Slovenian #license-cc-by-sa-4.0 #coreference resolution #region-us
# Dataset Card for SentiCoref ### Dataset Summary SentiCoref is a Slovenian coreference resolution dataset containing 391962 tokens inside 756 documents*. Also contains automatically (?) annotated named entities and manually verified lemmas and morphosyntactic tags (MSD). \* This is the latest version of SentiCoref, contained in SUK: Slovenian training corpus. ### Supported Tasks and Leaderboards Coreference resolution. ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the dataset, with most actual data truncated for the purpose of clarity: ### Data Fields Please note that documents are represented as lists of paragraphs, which are in turn lists of words. This means that 'words', 'lemmas', 'msds', and 'ne_tags' are of type List[List[List[string]]]. This is done because it is easier to discard the segmentation information than re-obtain it. - 'id_doc': the identifier of the document; - 'words': words in the document; - 'lemmas': lemmas in the document; - 'msds': morphosyntactic tags in the document; - 'ne_tags': named entity annotations in IOB2 format; - 'mentions': list of entity mentions in the document. Includes named entities, phrases, and single words (e.g., pronouns). Each mention is represented with its ID and the indices of contained words: either (1) the index of the paragraph, the sentence inside the paragraph, and the positions inside the sentence, or (2) the global word index that can be used on a flattened list of document words; - 'coref_clusters': coreference clusters present in the document. Each list represents one cluster of entity mentions, represented by their IDs ## Additional Information ### Using the dataset 1. Unless you are doing something more sophisticated, feel free to drop the paragraph and sentence segmentation information by flattening the document words, lemmas, MSDs, and named entity tags: 2. To get a better understanding of the entity mentions and coreference clusters in the document, you can convert the mention information into a dictionary and link the mentions and clusters to the actual words. Output: ### Dataset Curators Špela Arhar Holdt; et al. (please see URL for the full list) ### Licensing Information CC BY-SA 4.0. ### Contributions Thanks to @matejklemen for adding this dataset.
[ "# Dataset Card for SentiCoref", "### Dataset Summary\n\nSentiCoref is a Slovenian coreference resolution dataset containing 391962 tokens inside 756 documents*. \nAlso contains automatically (?) annotated named entities and manually verified lemmas and morphosyntactic tags (MSD). \n\n\\* This is the latest version of SentiCoref, contained in SUK: Slovenian training corpus.", "### Supported Tasks and Leaderboards\n\nCoreference resolution.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset, with most actual data truncated for the purpose of clarity:", "### Data Fields\n\nPlease note that documents are represented as lists of paragraphs, which are in turn lists of words. \nThis means that 'words', 'lemmas', 'msds', and 'ne_tags' are of type List[List[List[string]]].\nThis is done because it is easier to discard the segmentation information than re-obtain it.\n\n- 'id_doc': the identifier of the document; \n- 'words': words in the document;\n- 'lemmas': lemmas in the document;\n- 'msds': morphosyntactic tags in the document;\n- 'ne_tags': named entity annotations in IOB2 format;\n- 'mentions': list of entity mentions in the document. Includes named entities, phrases, and single words (e.g., pronouns). Each mention is represented with its ID and the\n indices of contained words: either (1) the index of the paragraph, the sentence inside the paragraph, and the positions inside the sentence, or\n (2) the global word index that can be used on a flattened list of document words;\n- 'coref_clusters': coreference clusters present in the document. Each list represents one cluster of entity mentions, represented by their IDs", "## Additional Information", "### Using the dataset\n\n1. Unless you are doing something more sophisticated, feel free to drop the paragraph and sentence segmentation information by flattening the document words, lemmas, MSDs, and named entity tags:\n\n\n2. To get a better understanding of the entity mentions and coreference clusters in the document, you can convert the mention information into a dictionary and\n link the mentions and clusters to the actual words.\n\nOutput:", "### Dataset Curators\n \nŠpela Arhar Holdt; et al. (please see URL for the full list)", "### Licensing Information\n\nCC BY-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]
[ "TAGS\n#task_categories-token-classification #size_categories-n<1K #language-Slovenian #license-cc-by-sa-4.0 #coreference resolution #region-us \n", "# Dataset Card for SentiCoref", "### Dataset Summary\n\nSentiCoref is a Slovenian coreference resolution dataset containing 391962 tokens inside 756 documents*. \nAlso contains automatically (?) annotated named entities and manually verified lemmas and morphosyntactic tags (MSD). \n\n\\* This is the latest version of SentiCoref, contained in SUK: Slovenian training corpus.", "### Supported Tasks and Leaderboards\n\nCoreference resolution.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset, with most actual data truncated for the purpose of clarity:", "### Data Fields\n\nPlease note that documents are represented as lists of paragraphs, which are in turn lists of words. \nThis means that 'words', 'lemmas', 'msds', and 'ne_tags' are of type List[List[List[string]]].\nThis is done because it is easier to discard the segmentation information than re-obtain it.\n\n- 'id_doc': the identifier of the document; \n- 'words': words in the document;\n- 'lemmas': lemmas in the document;\n- 'msds': morphosyntactic tags in the document;\n- 'ne_tags': named entity annotations in IOB2 format;\n- 'mentions': list of entity mentions in the document. Includes named entities, phrases, and single words (e.g., pronouns). Each mention is represented with its ID and the\n indices of contained words: either (1) the index of the paragraph, the sentence inside the paragraph, and the positions inside the sentence, or\n (2) the global word index that can be used on a flattened list of document words;\n- 'coref_clusters': coreference clusters present in the document. Each list represents one cluster of entity mentions, represented by their IDs", "## Additional Information", "### Using the dataset\n\n1. Unless you are doing something more sophisticated, feel free to drop the paragraph and sentence segmentation information by flattening the document words, lemmas, MSDs, and named entity tags:\n\n\n2. To get a better understanding of the entity mentions and coreference clusters in the document, you can convert the mention information into a dictionary and\n link the mentions and clusters to the actual words.\n\nOutput:", "### Dataset Curators\n \nŠpela Arhar Holdt; et al. (please see URL for the full list)", "### Licensing Information\n\nCC BY-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]
[ 48, 8, 85, 14, 7, 6, 28, 296, 5, 102, 26, 12, 18 ]
[ "passage: TAGS\n#task_categories-token-classification #size_categories-n<1K #language-Slovenian #license-cc-by-sa-4.0 #coreference resolution #region-us \n# Dataset Card for SentiCoref### Dataset Summary\n\nSentiCoref is a Slovenian coreference resolution dataset containing 391962 tokens inside 756 documents*. \nAlso contains automatically (?) annotated named entities and manually verified lemmas and morphosyntactic tags (MSD). \n\n\\* This is the latest version of SentiCoref, contained in SUK: Slovenian training corpus.### Supported Tasks and Leaderboards\n\nCoreference resolution.### Languages\n\nSlovenian.## Dataset Structure### Data Instances\n\nA sample instance from the dataset, with most actual data truncated for the purpose of clarity:### Data Fields\n\nPlease note that documents are represented as lists of paragraphs, which are in turn lists of words. \nThis means that 'words', 'lemmas', 'msds', and 'ne_tags' are of type List[List[List[string]]].\nThis is done because it is easier to discard the segmentation information than re-obtain it.\n\n- 'id_doc': the identifier of the document; \n- 'words': words in the document;\n- 'lemmas': lemmas in the document;\n- 'msds': morphosyntactic tags in the document;\n- 'ne_tags': named entity annotations in IOB2 format;\n- 'mentions': list of entity mentions in the document. Includes named entities, phrases, and single words (e.g., pronouns). Each mention is represented with its ID and the\n indices of contained words: either (1) the index of the paragraph, the sentence inside the paragraph, and the positions inside the sentence, or\n (2) the global word index that can be used on a flattened list of document words;\n- 'coref_clusters': coreference clusters present in the document. Each list represents one cluster of entity mentions, represented by their IDs## Additional Information" ]
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18aaf5d6a1ad1e63f872a29c2c00c8840947659d
# Dataset Card for "aesthetic-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
torphix/aesthetic-v2
[ "region:us" ]
2023-02-10T11:00:24+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5854280397.36, "num_examples": 14712}], "download_size": 491617940, "dataset_size": 5854280397.36}}
2023-02-27T01:14:20+00:00
[]
[]
TAGS #region-us
# Dataset Card for "aesthetic-v2" More Information needed
[ "# Dataset Card for \"aesthetic-v2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"aesthetic-v2\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"aesthetic-v2\"\n\nMore Information needed" ]
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d959675dc4344284e05d0fa7aaf62d64267e76a3
# Dataset Card for "weakly_labelled_dataset_mentions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/weakly_labelled_dataset_mentions
[ "region:us" ]
2023-02-10T11:12:10+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "dtype": "null"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "split", "dtype": "string"}]}, {"name": "status", "dtype": "string"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}, {"name": "label", "dtype": {"class_label": {"names": {"0": "dataset_mention", "1": "no_dataset_mention"}}}}], "splits": [{"name": "train", "num_bytes": 56713799, "num_examples": 30274}], "download_size": 24899276, "dataset_size": 56713799}}
2023-02-16T08:29:39+00:00
[]
[]
TAGS #region-us
# Dataset Card for "weakly_labelled_dataset_mentions" More Information needed
[ "# Dataset Card for \"weakly_labelled_dataset_mentions\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"weakly_labelled_dataset_mentions\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"weakly_labelled_dataset_mentions\"\n\nMore Information needed" ]
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7e01043924240627ce6d6f2f56d51d898e0bfba9
# AutoTrain Dataset for project: dataset-mentions ## Dataset Description This dataset has been automatically processed by AutoTrain for project dataset-mentions. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": " How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\") model = AutoModel.from_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ", "target": 0 }, { "text": " Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ", "target": 1 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['dataset_mention', 'no_dataset_mention'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 7428 | | valid | 1858 |
davanstrien/autotrain-data-dataset-mentions
[ "task_categories:text-classification", "language:en", "region:us" ]
2023-02-10T11:17:13+00:00
{"language": ["en"], "task_categories": ["text-classification"]}
2023-02-10T11:17:37+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #language-English #region-us
AutoTrain Dataset for project: dataset-mentions =============================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project dataset-mentions. ### Languages The BCP-47 code for the dataset's language is en. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") model = AutoModel.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:\n\n\npython from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\\_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\") model = AutoModel.from\\_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\")", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:\n\n\npython from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\\_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\") model = AutoModel.from\\_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\")", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 21, 26, 102, 23, 27 ]
[ "passage: TAGS\n#task_categories-text-classification #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:\n\n\npython from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\\_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\") model = AutoModel.from\\_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\")### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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e782ebf35c7e4cafccb08ca680b0a76706533067
## Dataset Description A small subset of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, with 87 programming languages, each has 10,000 random samples from the original dataset. ## Languages The dataset contains 87 programming languages: ```` 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', 'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java', 'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig' ````` ## Dataset Structure ```python # to load go: from datasets import load_dataset load_dataset("bigcode/the-stack-smol-xl", data_dir="data/go") ```
bigcode/the-stack-smol-xl
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "language:code", "region:us" ]
2023-02-10T11:17:22+00:00
{"annotations_creators": [], "language_creators": ["crowdsourced"], "language": ["code"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"]}
2023-02-10T17:22:38+00:00
[]
[ "code" ]
TAGS #task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us
## Dataset Description A small subset of the-stack dataset, with 87 programming languages, each has 10,000 random samples from the original dataset. ## Languages The dataset contains 87 programming languages: '' ## Dataset Structure
[ "## Dataset Description\nA small subset of the-stack dataset, with 87 programming languages, each has 10,000 random samples from the original dataset.", "## Languages\n\nThe dataset contains 87 programming languages:\n''", "## Dataset Structure" ]
[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us \n", "## Dataset Description\nA small subset of the-stack dataset, with 87 programming languages, each has 10,000 random samples from the original dataset.", "## Languages\n\nThe dataset contains 87 programming languages:\n''", "## Dataset Structure" ]
[ 59, 35, 15, 6 ]
[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us \n## Dataset Description\nA small subset of the-stack dataset, with 87 programming languages, each has 10,000 random samples from the original dataset.## Languages\n\nThe dataset contains 87 programming languages:\n''## Dataset Structure" ]
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f18b0a70359ebfb41f658fd564208d0355b013f4
# Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://scienceqa.github.io/index.html#home](https://scienceqa.github.io/index.html#home) - **Repository:** [https://github.com/lupantech/ScienceQA](https://github.com/lupantech/ScienceQA) - **Paper:** [https://arxiv.org/abs/2209.09513](https://arxiv.org/abs/2209.09513) - **Leaderboard:** [https://paperswithcode.com/dataset/scienceqa](https://paperswithcode.com/dataset/scienceqa) - **Point of Contact:** [Pan Lu](https://lupantech.github.io/) or file an issue on [Github](https://github.com/lupantech/ScienceQA/issues) ### Dataset Summary Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering ### Supported Tasks and Leaderboards Multi-modal Multiple Choice ### Languages English ## Dataset Structure ### Data Instances Explore more samples [here](https://scienceqa.github.io/explore.html). ``` json {'image': Image, 'question': 'Which of these states is farthest north?', 'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'], 'answer': 0, 'hint': '', 'task': 'closed choice', 'grade': 'grade2', 'subject': 'social science', 'topic': 'geography', 'category': 'Geography', 'skill': 'Read a map: cardinal directions', 'lecture': 'Maps have four cardinal directions, or main directions. Those directions are north, south, east, and west.\nA compass rose is a set of arrows that point to the cardinal directions. A compass rose usually shows only the first letter of each cardinal direction.\nThe north arrow points to the North Pole. On most maps, north is at the top of the map.', 'solution': 'To find the answer, look at the compass rose. Look at which way the north arrow is pointing. West Virginia is farthest north.'} ``` Some records might be missing any or all of image, lecture, solution. ### Data Fields - `image` : Contextual image - `question` : Prompt relating to the `lecture` - `choices` : Multiple choice answer with 1 correct to the `question` - `answer` : Index of choices corresponding to the correct answer - `hint` : Hint to help answer the `question` - `task` : Task description - `grade` : Grade level from K-12 - `subject` : High level - `topic` : natural-sciences, social-science, or language-science - `category` : A subcategory of `topic` - `skill` : A description of the task required - `lecture` : A relevant lecture that a `question` is generated from - `solution` : Instructions on how to solve the `question` Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [Datasets Tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), you will then only need to refine the generated descriptions. ### Data Splits - name: train - num_bytes: 16416902 - num_examples: 12726 - name: validation - num_bytes: 5404896 - num_examples: 4241 - name: test - num_bytes: 5441676 - num_examples: 4241 ## Dataset Creation ### Curation Rationale When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA). ### Source Data ScienceQA is collected from elementary and high school science curricula. #### Initial Data Collection and Normalization See Below #### Who are the source language producers? See Below ### Annotations Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning, an online learning platform curated by experts in the field of K-12 education. The dataset includes problems that align with California Common Core Content Standards. To construct ScienceQA, we downloaded the original science problems and then extracted individual components (e.g. questions, hints, images, options, answers, lectures, and solutions) from them based on heuristic rules. We manually removed invalid questions, such as questions that have only one choice, questions that contain faulty data, and questions that are duplicated, to comply with fair use and transformative use of the law. If there were multiple correct answers that applied, we kept only one correct answer. Also, we shuffled the answer options of each question to ensure the choices do not follow any specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets, ScienceQA is available for non-commercial research purposes only and the copyright belongs to the original authors. To ensure data quality, we developed a data exploration tool to review examples in the collected dataset, and incorrect annotations were further manually revised by experts. The tool can be accessed at https://scienceqa.github.io/explore.html. #### Annotation process See above #### Who are the annotators? See above ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators - Pan Lu1,3 - Swaroop Mishra2,3 - Tony Xia1 - Liang Qiu1 - Kai-Wei Chang1 - Song-Chun Zhu1 - Oyvind Tafjord3 - Peter Clark3 - Ashwin Kalyan3 From: 1. University of California, Los Angeles 2. Arizona State University 3. Allen Institute for AI ### Licensing Information [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ](https://creativecommons.org/licenses/by-nc-sa/4.0/) ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @inproceedings{lu2022learn, title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering}, author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan}, booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)}, year={2022} } ``` ### Contributions Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) [@datavistics](https://github.com/datavistics) for adding this dataset.
derek-thomas/ScienceQA
[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:other", "task_categories:visual-question-answering", "task_categories:text-classification", "task_ids:multiple-choice-qa", "task_ids:closed-domain-qa", "task_ids:open-domain-qa", "task_ids:visual-question-answering", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "multi-modal-qa", "science", "chemistry", "biology", "physics", "earth-science", "engineering", "geography", "history", "world-history", "civics", "economics", "global-studies", "grammar", "writing", "vocabulary", "natural-science", "language-science", "social-science", "arxiv:2209.09513", "region:us" ]
2023-02-10T11:28:58+00:00
{"annotations_creators": ["expert-generated", "found"], "language_creators": ["expert-generated", "found"], "language": ["en"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering", "other", "visual-question-answering", "text-classification"], "task_ids": ["multiple-choice-qa", "closed-domain-qa", "open-domain-qa", "visual-question-answering", "multi-class-classification"], "paperswithcode_id": "scienceqa", "pretty_name": "ScienceQA", "tags": ["multi-modal-qa", "science", "chemistry", "biology", "physics", "earth-science", "engineering", "geography", "history", "world-history", "civics", "economics", "global-studies", "grammar", "writing", "vocabulary", "natural-science", "language-science", "social-science"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int8"}, {"name": "hint", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "grade", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "skill", "dtype": "string"}, {"name": "lecture", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16416902, "num_examples": 12726}, {"name": "validation", "num_bytes": 5404896, "num_examples": 4241}, {"name": "test", "num_bytes": 5441676, "num_examples": 4241}], "download_size": 0, "dataset_size": 27263474}}
2023-02-25T04:23:01+00:00
[ "2209.09513" ]
[ "en" ]
TAGS #task_categories-multiple-choice #task_categories-question-answering #task_categories-other #task_categories-visual-question-answering #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-closed-domain-qa #task_ids-open-domain-qa #task_ids-visual-question-answering #task_ids-multi-class-classification #annotations_creators-expert-generated #annotations_creators-found #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-sa-4.0 #multi-modal-qa #science #chemistry #biology #physics #earth-science #engineering #geography #history #world-history #civics #economics #global-studies #grammar #writing #vocabulary #natural-science #language-science #social-science #arxiv-2209.09513 #region-us
# Dataset Card Creation Guide ## Table of Contents - Dataset Card Creation Guide - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Initial Data Collection and Normalization - Who are the source language producers? - Annotations - Annotation process - Who are the annotators? - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: URL - Point of Contact: Pan Lu or file an issue on Github ### Dataset Summary Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering ### Supported Tasks and Leaderboards Multi-modal Multiple Choice ### Languages English ## Dataset Structure ### Data Instances Explore more samples here. Some records might be missing any or all of image, lecture, solution. ### Data Fields - 'image' : Contextual image - 'question' : Prompt relating to the 'lecture' - 'choices' : Multiple choice answer with 1 correct to the 'question' - 'answer' : Index of choices corresponding to the correct answer - 'hint' : Hint to help answer the 'question' - 'task' : Task description - 'grade' : Grade level from K-12 - 'subject' : High level - 'topic' : natural-sciences, social-science, or language-science - 'category' : A subcategory of 'topic' - 'skill' : A description of the task required - 'lecture' : A relevant lecture that a 'question' is generated from - 'solution' : Instructions on how to solve the 'question' Note that the descriptions can be initialized with the Show Markdown Data Fields output of the Datasets Tagging app, you will then only need to refine the generated descriptions. ### Data Splits - name: train - num_bytes: 16416902 - num_examples: 12726 - name: validation - num_bytes: 5404896 - num_examples: 4241 - name: test - num_bytes: 5441676 - num_examples: 4241 ## Dataset Creation ### Curation Rationale When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA). ### Source Data ScienceQA is collected from elementary and high school science curricula. #### Initial Data Collection and Normalization See Below #### Who are the source language producers? See Below ### Annotations Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning, an online learning platform curated by experts in the field of K-12 education. The dataset includes problems that align with California Common Core Content Standards. To construct ScienceQA, we downloaded the original science problems and then extracted individual components (e.g. questions, hints, images, options, answers, lectures, and solutions) from them based on heuristic rules. We manually removed invalid questions, such as questions that have only one choice, questions that contain faulty data, and questions that are duplicated, to comply with fair use and transformative use of the law. If there were multiple correct answers that applied, we kept only one correct answer. Also, we shuffled the answer options of each question to ensure the choices do not follow any specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets, ScienceQA is available for non-commercial research purposes only and the copyright belongs to the original authors. To ensure data quality, we developed a data exploration tool to review examples in the collected dataset, and incorrect annotations were further manually revised by experts. The tool can be accessed at URL #### Annotation process See above #### Who are the annotators? See above ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators - Pan Lu1,3 - Swaroop Mishra2,3 - Tony Xia1 - Liang Qiu1 - Kai-Wei Chang1 - Song-Chun Zhu1 - Oyvind Tafjord3 - Peter Clark3 - Ashwin Kalyan3 From: 1. University of California, Los Angeles 2. Arizona State University 3. Allen Institute for AI ### Licensing Information Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Provide the BibTex-formatted reference for the dataset. For example: ### Contributions Thanks to Derek Thomas @datavistics for adding this dataset.
[ "# Dataset Card Creation Guide", "## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\n - Personal and Sensitive Information\n - Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Pan Lu or file an issue on Github", "### Dataset Summary\n\nLearn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering", "### Supported Tasks and Leaderboards\n\nMulti-modal Multiple Choice", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nExplore more samples here.\n\n\n\nSome records might be missing any or all of image, lecture, solution.", "### Data Fields\n\n- 'image' : Contextual image\n- 'question' : Prompt relating to the 'lecture'\n- 'choices' : Multiple choice answer with 1 correct to the 'question'\n- 'answer' : Index of choices corresponding to the correct answer\n- 'hint' : Hint to help answer the 'question'\n- 'task' : Task description\n- 'grade' : Grade level from K-12\n- 'subject' : High level \n- 'topic' : natural-sciences, social-science, or language-science\n- 'category' : A subcategory of 'topic'\n- 'skill' : A description of the task required\n- 'lecture' : A relevant lecture that a 'question' is generated from\n- 'solution' : Instructions on how to solve the 'question'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the Datasets Tagging app, you will then only need to refine the generated descriptions.", "### Data Splits\n- name: train\n - num_bytes: 16416902\n - num_examples: 12726\n- name: validation\n - num_bytes: 5404896\n - num_examples: 4241\n- name: test\n - num_bytes: 5441676\n - num_examples: 4241", "## Dataset Creation", "### Curation Rationale\n\nWhen answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).", "### Source Data\n\nScienceQA is collected from elementary and high school science curricula.", "#### Initial Data Collection and Normalization\n\nSee Below", "#### Who are the source language producers?\n\nSee Below", "### Annotations\n\nQuestions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,\nan online learning platform curated by experts in the field of K-12 education. The dataset includes\nproblems that align with California Common Core Content Standards. To construct ScienceQA, we\ndownloaded the original science problems and then extracted individual components (e.g. questions,\nhints, images, options, answers, lectures, and solutions) from them based on heuristic rules.\nWe manually removed invalid questions, such as questions that have only one choice, questions that\ncontain faulty data, and questions that are duplicated, to comply with fair use and transformative\nuse of the law. If there were multiple correct answers that applied, we kept only one correct answer.\nAlso, we shuffled the answer options of each question to ensure the choices do not follow any\nspecific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat\nthe lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are\neasily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,\nScienceQA is available for non-commercial research purposes only and the copyright belongs to\nthe original authors. To ensure data quality, we developed a data exploration tool to review examples\nin the collected dataset, and incorrect annotations were further manually revised by experts. The tool\ncan be accessed at URL", "#### Annotation process\n\nSee above", "#### Who are the annotators?\n\nSee above", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\n- Pan Lu1,3\n- Swaroop Mishra2,3\n- Tony Xia1\n- Liang Qiu1\n- Kai-Wei Chang1\n- Song-Chun Zhu1\n- Oyvind Tafjord3\n- Peter Clark3\n- Ashwin Kalyan3\n \nFrom: \n1. University of California, Los Angeles \n2. Arizona State University \n3. Allen Institute for AI", "### Licensing Information\n\nAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)\n\n\n\n\nProvide the BibTex-formatted reference for the dataset. For example:", "### Contributions\n\nThanks to Derek Thomas @datavistics for adding this dataset." ]
[ "TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_categories-other #task_categories-visual-question-answering #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-closed-domain-qa #task_ids-open-domain-qa #task_ids-visual-question-answering #task_ids-multi-class-classification #annotations_creators-expert-generated #annotations_creators-found #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-sa-4.0 #multi-modal-qa #science #chemistry #biology #physics #earth-science #engineering #geography #history #world-history #civics #economics #global-studies #grammar #writing #vocabulary #natural-science #language-science #social-science #arxiv-2209.09513 #region-us \n", "# Dataset Card Creation Guide", "## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\n - Personal and Sensitive Information\n - Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Pan Lu or file an issue on Github", "### Dataset Summary\n\nLearn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering", "### Supported Tasks and Leaderboards\n\nMulti-modal Multiple Choice", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nExplore more samples here.\n\n\n\nSome records might be missing any or all of image, lecture, solution.", "### Data Fields\n\n- 'image' : Contextual image\n- 'question' : Prompt relating to the 'lecture'\n- 'choices' : Multiple choice answer with 1 correct to the 'question'\n- 'answer' : Index of choices corresponding to the correct answer\n- 'hint' : Hint to help answer the 'question'\n- 'task' : Task description\n- 'grade' : Grade level from K-12\n- 'subject' : High level \n- 'topic' : natural-sciences, social-science, or language-science\n- 'category' : A subcategory of 'topic'\n- 'skill' : A description of the task required\n- 'lecture' : A relevant lecture that a 'question' is generated from\n- 'solution' : Instructions on how to solve the 'question'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the Datasets Tagging app, you will then only need to refine the generated descriptions.", "### Data Splits\n- name: train\n - num_bytes: 16416902\n - num_examples: 12726\n- name: validation\n - num_bytes: 5404896\n - num_examples: 4241\n- name: test\n - num_bytes: 5441676\n - num_examples: 4241", "## Dataset Creation", "### Curation Rationale\n\nWhen answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).", "### Source Data\n\nScienceQA is collected from elementary and high school science curricula.", "#### Initial Data Collection and Normalization\n\nSee Below", "#### Who are the source language producers?\n\nSee Below", "### Annotations\n\nQuestions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,\nan online learning platform curated by experts in the field of K-12 education. The dataset includes\nproblems that align with California Common Core Content Standards. To construct ScienceQA, we\ndownloaded the original science problems and then extracted individual components (e.g. questions,\nhints, images, options, answers, lectures, and solutions) from them based on heuristic rules.\nWe manually removed invalid questions, such as questions that have only one choice, questions that\ncontain faulty data, and questions that are duplicated, to comply with fair use and transformative\nuse of the law. If there were multiple correct answers that applied, we kept only one correct answer.\nAlso, we shuffled the answer options of each question to ensure the choices do not follow any\nspecific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat\nthe lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are\neasily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,\nScienceQA is available for non-commercial research purposes only and the copyright belongs to\nthe original authors. To ensure data quality, we developed a data exploration tool to review examples\nin the collected dataset, and incorrect annotations were further manually revised by experts. The tool\ncan be accessed at URL", "#### Annotation process\n\nSee above", "#### Who are the annotators?\n\nSee above", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\n- Pan Lu1,3\n- Swaroop Mishra2,3\n- Tony Xia1\n- Liang Qiu1\n- Kai-Wei Chang1\n- Song-Chun Zhu1\n- Oyvind Tafjord3\n- Peter Clark3\n- Ashwin Kalyan3\n \nFrom: \n1. University of California, Los Angeles \n2. Arizona State University \n3. Allen Institute for AI", "### Licensing Information\n\nAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)\n\n\n\n\nProvide the BibTex-formatted reference for the dataset. For example:", "### Contributions\n\nThanks to Derek Thomas @datavistics for adding this dataset." ]
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[ "passage: TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_categories-other #task_categories-visual-question-answering #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-closed-domain-qa #task_ids-open-domain-qa #task_ids-visual-question-answering #task_ids-multi-class-classification #annotations_creators-expert-generated #annotations_creators-found #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-sa-4.0 #multi-modal-qa #science #chemistry #biology #physics #earth-science #engineering #geography #history #world-history #civics #economics #global-studies #grammar #writing #vocabulary #natural-science #language-science #social-science #arxiv-2209.09513 #region-us \n# Dataset Card Creation Guide## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\n - Personal and Sensitive Information\n - Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Pan Lu or file an issue on Github", "passage: ### Dataset Summary\n\nLearn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering### Supported Tasks and Leaderboards\n\nMulti-modal Multiple Choice### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nExplore more samples here.\n\n\n\nSome records might be missing any or all of image, lecture, solution.### Data Fields\n\n- 'image' : Contextual image\n- 'question' : Prompt relating to the 'lecture'\n- 'choices' : Multiple choice answer with 1 correct to the 'question'\n- 'answer' : Index of choices corresponding to the correct answer\n- 'hint' : Hint to help answer the 'question'\n- 'task' : Task description\n- 'grade' : Grade level from K-12\n- 'subject' : High level \n- 'topic' : natural-sciences, social-science, or language-science\n- 'category' : A subcategory of 'topic'\n- 'skill' : A description of the task required\n- 'lecture' : A relevant lecture that a 'question' is generated from\n- 'solution' : Instructions on how to solve the 'question'\n\n\nNote that the descriptions can be initialized with the Show Markdown Data Fields output of the Datasets Tagging app, you will then only need to refine the generated descriptions.### Data Splits\n- name: train\n - num_bytes: 16416902\n - num_examples: 12726\n- name: validation\n - num_bytes: 5404896\n - num_examples: 4241\n- name: test\n - num_bytes: 5441676\n - num_examples: 4241## Dataset Creation", "passage: ### Curation Rationale\n\nWhen answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).### Source Data\n\nScienceQA is collected from elementary and high school science curricula.#### Initial Data Collection and Normalization\n\nSee Below#### Who are the source language producers?\n\nSee Below### Annotations\n\nQuestions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,\nan online learning platform curated by experts in the field of K-12 education. The dataset includes\nproblems that align with California Common Core Content Standards. To construct ScienceQA, we\ndownloaded the original science problems and then extracted individual components (e.g. questions,\nhints, images, options, answers, lectures, and solutions) from them based on heuristic rules.\nWe manually removed invalid questions, such as questions that have only one choice, questions that\ncontain faulty data, and questions that are duplicated, to comply with fair use and transformative\nuse of the law. If there were multiple correct answers that applied, we kept only one correct answer.\nAlso, we shuffled the answer options of each question to ensure the choices do not follow any\nspecific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat\nthe lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are\neasily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,\nScienceQA is available for non-commercial research purposes only and the copyright belongs to\nthe original authors. To ensure data quality, we developed a data exploration tool to review examples\nin the collected dataset, and incorrect annotations were further manually revised by experts. The tool\ncan be accessed at URL#### Annotation process\n\nSee above#### Who are the annotators?\n\nSee above### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information" ]
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1e3dd39b39787bddb20d7008e4d71c330d99f55b
## Dataset Description A small subset of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, with 87 programming languages, each has 100 random samples from the original dataset for visualization. ## Languages The dataset contains 87 programming languages: ```` 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', 'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java', 'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig' ````` ## Dataset Structure You can specify which language you want to load, python is loaded by default: ```python # to load go: from datasets import load_dataset load_dataset("bigcode/the-stack-smol-xs", "go") DatasetDict({ train: Dataset({ features: ['content', 'lang', 'size', 'ext', 'max_stars_count', 'avg_line_length', 'max_line_length', 'alphanum_fraction'], num_rows: 100 }) }) ```
bigcode/the-stack-smol-xs
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "language:code", "region:us" ]
2023-02-10T11:47:50+00:00
{"annotations_creators": [], "language_creators": ["crowdsourced"], "language": ["code"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"]}
2023-02-13T09:05:23+00:00
[]
[ "code" ]
TAGS #task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us
## Dataset Description A small subset of the-stack dataset, with 87 programming languages, each has 100 random samples from the original dataset for visualization. ## Languages The dataset contains 87 programming languages: '' ## Dataset Structure You can specify which language you want to load, python is loaded by default:
[ "## Dataset Description\nA small subset of the-stack dataset, with 87 programming languages, each has 100 random samples from the original dataset for visualization.", "## Languages\n\nThe dataset contains 87 programming languages:\n''", "## Dataset Structure\nYou can specify which language you want to load, python is loaded by default:" ]
[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us \n", "## Dataset Description\nA small subset of the-stack dataset, with 87 programming languages, each has 100 random samples from the original dataset for visualization.", "## Languages\n\nThe dataset contains 87 programming languages:\n''", "## Dataset Structure\nYou can specify which language you want to load, python is loaded by default:" ]
[ 59, 38, 15, 25 ]
[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us \n## Dataset Description\nA small subset of the-stack dataset, with 87 programming languages, each has 100 random samples from the original dataset for visualization.## Languages\n\nThe dataset contains 87 programming languages:\n''## Dataset Structure\nYou can specify which language you want to load, python is loaded by default:" ]
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08658458a8137ddd744c6ba5b608557240b38268
# Dataset Card for "yoci_monkey" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Swatermelon/yoci_monkey
[ "region:us" ]
2023-02-10T12:08:57+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 594675.0, "num_examples": 43}], "download_size": 0, "dataset_size": 594675.0}}
2023-02-14T07:03:46+00:00
[]
[]
TAGS #region-us
# Dataset Card for "yoci_monkey" More Information needed
[ "# Dataset Card for \"yoci_monkey\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"yoci_monkey\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"yoci_monkey\"\n\nMore Information needed" ]
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1076853c2f3eefc47bba652dc00459018ce695d1
# Dataset Card for "stanford_plato" ## Description This is a collection of articles in the Stanford Encyclopedia of Philosophy (https://plato.stanford.edu/index.html). This dataset includes 1776 articles, each explaining one philosophy term/people/topic. It has 8 features: - shorturl: The shorturl for the article. For example, the shorturl 'abduction' correspond to the page https://plato.stanford.edu/entries/abduction/ - title: The title of the article. - pubinfo: The publication information. - **preamble**: The preface text of the article. The data is a list, each item of the list is a paragraph of the data. I choose not to break the paragraph structure. Certainly, you can merge them by, for example, ''.join(data['preamble']) - toc: Table of contents. Also represented as list. Each item is a dictionary, the 'content_title' is the main content title, and the 'sub_toc' is a list of subcontent titles. - **main_text**: The main text of the article. The data is also a list, each item represents a section of the article. Each item is a dictionary, 'section_title' is the title of the section, 'main_content' is a list of paragraphs before subsections, 'subsections' is a list of subsections, each item is also a dictionary, has its own title 'subsection_title' and list of paragraphs 'content'. - bibliography: list of bibliography. - related_entries: list of entries related to the current entry. ## Copyright and license See the information at the offical website: https://plato.stanford.edu/info.html#c This is not an official release. May be deleted later if violates copyright. The responsibility of not abusing is on the user.
hugfaceguy0001/stanford_plato
[ "region:us" ]
2023-02-10T12:47:16+00:00
{"dataset_info": {"features": [{"name": "shorturl", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "pubinfo", "dtype": "string"}, {"name": "preamble", "sequence": "string"}, {"name": "toc", "list": [{"name": "content_title", "dtype": "string"}, {"name": "sub_toc", "sequence": "string"}]}, {"name": "main_text", "list": [{"name": "main_content", "sequence": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "subsections", "list": [{"name": "content", "sequence": "string"}, {"name": "subsection_title", "dtype": "string"}]}]}, {"name": "bibliography", "sequence": "string"}, {"name": "related_entries", "list": [{"name": "href", "dtype": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 160405734, "num_examples": 1776}], "download_size": 90000475, "dataset_size": 160405734}}
2023-02-10T14:03:54+00:00
[]
[]
TAGS #region-us
# Dataset Card for "stanford_plato" ## Description This is a collection of articles in the Stanford Encyclopedia of Philosophy (URL This dataset includes 1776 articles, each explaining one philosophy term/people/topic. It has 8 features: - shorturl: The shorturl for the article. For example, the shorturl 'abduction' correspond to the page URL - title: The title of the article. - pubinfo: The publication information. - preamble: The preface text of the article. The data is a list, each item of the list is a paragraph of the data. I choose not to break the paragraph structure. Certainly, you can merge them by, for example, ''.join(data['preamble']) - toc: Table of contents. Also represented as list. Each item is a dictionary, the 'content_title' is the main content title, and the 'sub_toc' is a list of subcontent titles. - main_text: The main text of the article. The data is also a list, each item represents a section of the article. Each item is a dictionary, 'section_title' is the title of the section, 'main_content' is a list of paragraphs before subsections, 'subsections' is a list of subsections, each item is also a dictionary, has its own title 'subsection_title' and list of paragraphs 'content'. - bibliography: list of bibliography. - related_entries: list of entries related to the current entry. ## Copyright and license See the information at the offical website: URL This is not an official release. May be deleted later if violates copyright. The responsibility of not abusing is on the user.
[ "# Dataset Card for \"stanford_plato\"", "## Description\n\nThis is a collection of articles in the Stanford Encyclopedia of Philosophy (URL\n\nThis dataset includes 1776 articles, each explaining one philosophy term/people/topic. It has 8 features:\n\n- shorturl: The shorturl for the article. For example, the shorturl 'abduction' correspond to the page URL\n- title: The title of the article.\n- pubinfo: The publication information.\n- preamble: The preface text of the article. The data is a list, each item of the list is a paragraph of the data. I choose not to break the paragraph structure. Certainly, you can merge them by, for example, ''.join(data['preamble'])\n- toc: Table of contents. Also represented as list. Each item is a dictionary, the 'content_title' is the main content title, and the 'sub_toc' is a list of subcontent titles.\n- main_text: The main text of the article.\n The data is also a list, each item represents a section of the article.\n Each item is a dictionary, 'section_title' is the title of the section, 'main_content' is a list of paragraphs before subsections,\n 'subsections' is a list of subsections, each item is also a dictionary, has its own title 'subsection_title' and list of paragraphs 'content'.\n- bibliography: list of bibliography.\n- related_entries: list of entries related to the current entry.", "## Copyright and license\n\nSee the information at the offical website: URL\nThis is not an official release. May be deleted later if violates copyright. The responsibility of not abusing is on the user." ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"stanford_plato\"", "## Description\n\nThis is a collection of articles in the Stanford Encyclopedia of Philosophy (URL\n\nThis dataset includes 1776 articles, each explaining one philosophy term/people/topic. It has 8 features:\n\n- shorturl: The shorturl for the article. For example, the shorturl 'abduction' correspond to the page URL\n- title: The title of the article.\n- pubinfo: The publication information.\n- preamble: The preface text of the article. The data is a list, each item of the list is a paragraph of the data. I choose not to break the paragraph structure. Certainly, you can merge them by, for example, ''.join(data['preamble'])\n- toc: Table of contents. Also represented as list. Each item is a dictionary, the 'content_title' is the main content title, and the 'sub_toc' is a list of subcontent titles.\n- main_text: The main text of the article.\n The data is also a list, each item represents a section of the article.\n Each item is a dictionary, 'section_title' is the title of the section, 'main_content' is a list of paragraphs before subsections,\n 'subsections' is a list of subsections, each item is also a dictionary, has its own title 'subsection_title' and list of paragraphs 'content'.\n- bibliography: list of bibliography.\n- related_entries: list of entries related to the current entry.", "## Copyright and license\n\nSee the information at the offical website: URL\nThis is not an official release. May be deleted later if violates copyright. The responsibility of not abusing is on the user." ]
[ 6, 12, 342, 42 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"stanford_plato\"## Description\n\nThis is a collection of articles in the Stanford Encyclopedia of Philosophy (URL\n\nThis dataset includes 1776 articles, each explaining one philosophy term/people/topic. It has 8 features:\n\n- shorturl: The shorturl for the article. For example, the shorturl 'abduction' correspond to the page URL\n- title: The title of the article.\n- pubinfo: The publication information.\n- preamble: The preface text of the article. The data is a list, each item of the list is a paragraph of the data. I choose not to break the paragraph structure. Certainly, you can merge them by, for example, ''.join(data['preamble'])\n- toc: Table of contents. Also represented as list. Each item is a dictionary, the 'content_title' is the main content title, and the 'sub_toc' is a list of subcontent titles.\n- main_text: The main text of the article.\n The data is also a list, each item represents a section of the article.\n Each item is a dictionary, 'section_title' is the title of the section, 'main_content' is a list of paragraphs before subsections,\n 'subsections' is a list of subsections, each item is also a dictionary, has its own title 'subsection_title' and list of paragraphs 'content'.\n- bibliography: list of bibliography.\n- related_entries: list of entries related to the current entry.## Copyright and license\n\nSee the information at the offical website: URL\nThis is not an official release. May be deleted later if violates copyright. The responsibility of not abusing is on the user." ]
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82e842316742f503cc46a9e919bbc87b1ca6e150
# Dataset Card for "cities-suburbs-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
olly4/cities-suburbs-small
[ "region:us" ]
2023-02-10T12:55:10+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "Unnamed: 0", "dtype": "int64"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 872929816.432, "num_examples": 2202}], "download_size": 428529931, "dataset_size": 872929816.432}}
2023-02-10T13:06:21+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cities-suburbs-small" More Information needed
[ "# Dataset Card for \"cities-suburbs-small\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cities-suburbs-small\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cities-suburbs-small\"\n\nMore Information needed" ]
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3c119e7fba88fb20cf79910a7908d964baebda1d
# Dataset Card for "chai-synthetic-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/chai-synthetic-pairwise
[ "region:us" ]
2023-02-10T13:01:05+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1690616961, "num_examples": 41128}, {"name": "test", "num_bytes": 47839521, "num_examples": 4570}], "download_size": 781208088, "dataset_size": 1738456482}}
2023-02-10T13:03:25+00:00
[]
[]
TAGS #region-us
# Dataset Card for "chai-synthetic-pairwise" More Information needed
[ "# Dataset Card for \"chai-synthetic-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"chai-synthetic-pairwise\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"chai-synthetic-pairwise\"\n\nMore Information needed" ]
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78847d5c474df635a3575b2a819532e87cd35cdf
# AutoTrain Dataset for project: histopathological_image_classification ## Dataset Description This dataset has been automatically processed by AutoTrain for project histopathological_image_classification. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<700x460 RGB PIL image>", "target": 6 }, { "image": "<700x460 RGB PIL image>", "target": 2 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['1', '2', '3', '4', '5', '6', '7', '8'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 333 | | valid | 89 |
JoffreyMa/autotrain-data-histopathological_image_classification
[ "task_categories:image-classification", "region:us" ]
2023-02-10T13:05:16+00:00
{"task_categories": ["image-classification"]}
2023-02-10T13:25:34+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
AutoTrain Dataset for project: histopathological\_image\_classification ======================================================================= Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project histopathological\_image\_classification. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 17, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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f861285103b1ad536e964df4f2dd57323cd4256d
# Dataset Card for "chai-real-and-synthetic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/chai-real-and-synthetic
[ "region:us" ]
2023-02-10T13:21:26+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3208000491, "num_examples": 134668}, {"name": "test", "num_bytes": 171664726, "num_examples": 18961}], "download_size": 1180192490, "dataset_size": 3379665217}}
2023-02-10T13:22:05+00:00
[]
[]
TAGS #region-us
# Dataset Card for "chai-real-and-synthetic" More Information needed
[ "# Dataset Card for \"chai-real-and-synthetic\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"chai-real-and-synthetic\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"chai-real-and-synthetic\"\n\nMore Information needed" ]
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6ebc752130d09a397ccf15e5daf781c9905f98d3
### Dataset Summary This dataset will help you to develop a machine learning-based model to predict the pathogenic variants (Positive labels) by utilizing their amino acid sequences. **Used as an example to benchmark _biomerida_ as part of the Bio-Hakathon Mena region**
sequential-lab/TP53_protein_variants
[ "region:us" ]
2023-02-10T14:04:25+00:00
{}
2023-02-10T14:27:18+00:00
[]
[]
TAGS #region-us
### Dataset Summary This dataset will help you to develop a machine learning-based model to predict the pathogenic variants (Positive labels) by utilizing their amino acid sequences. Used as an example to benchmark _biomerida_ as part of the Bio-Hakathon Mena region
[ "### Dataset Summary\n\nThis dataset will help you to develop a machine learning-based model to predict the pathogenic variants (Positive labels) by utilizing their amino acid sequences.\n\nUsed as an example to benchmark _biomerida_ as part of the Bio-Hakathon Mena region" ]
[ "TAGS\n#region-us \n", "### Dataset Summary\n\nThis dataset will help you to develop a machine learning-based model to predict the pathogenic variants (Positive labels) by utilizing their amino acid sequences.\n\nUsed as an example to benchmark _biomerida_ as part of the Bio-Hakathon Mena region" ]
[ 6, 69 ]
[ "passage: TAGS\n#region-us \n### Dataset Summary\n\nThis dataset will help you to develop a machine learning-based model to predict the pathogenic variants (Positive labels) by utilizing their amino acid sequences.\n\nUsed as an example to benchmark _biomerida_ as part of the Bio-Hakathon Mena region" ]
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bfc78b0d405307cb2c6d48976a317f0a1d6fa106
# Dataset Card for "dataset-affecthqnet-fer2013" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Piro17/dataset-affecthqnet-fer2013
[ "region:us" ]
2023-02-10T14:07:09+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "anger", "1": "disgust", "2": "fear", "3": "happy", "4": "neutral", "5": "sad", "6": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 106887329.048, "num_examples": 56532}], "download_size": 7975090261, "dataset_size": 106887329.048}}
2023-02-10T14:13:09+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dataset-affecthqnet-fer2013" More Information needed
[ "# Dataset Card for \"dataset-affecthqnet-fer2013\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-affecthqnet-fer2013\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dataset-affecthqnet-fer2013\"\n\nMore Information needed" ]
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9bd05027eb3b7a8feda41dfab25afd158e9c250c
# Dataset Card for "SynthDog-RU_EN" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nyaaneet/SynthDog-RU_EN
[ "region:us" ]
2023-02-10T14:21:54+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 458147114.98, "num_examples": 9570}, {"name": "test", "num_bytes": 113499938.11, "num_examples": 2430}], "download_size": 558565516, "dataset_size": 571647053.09}}
2023-02-10T14:55:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "SynthDog-RU_EN" More Information needed
[ "# Dataset Card for \"SynthDog-RU_EN\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"SynthDog-RU_EN\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"SynthDog-RU_EN\"\n\nMore Information needed" ]
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08c7b0f394f7d7f863a6abca58c1f682496441b9
# instruction dataset for code bugfix ## TODO: - [ ] Add commit message as prompt or summary of the bug - [ ] Add source code repos & file & commit_sha
zirui3/ManySStuBs4J-instructions-v0
[ "license:cc-by-4.0", "region:us" ]
2023-02-10T15:07:19+00:00
{"license": "cc-by-4.0"}
2023-03-08T04:47:04+00:00
[]
[]
TAGS #license-cc-by-4.0 #region-us
# instruction dataset for code bugfix ## TODO: - [ ] Add commit message as prompt or summary of the bug - [ ] Add source code repos & file & commit_sha
[ "# instruction dataset for code bugfix", "## TODO:\n\n- [ ] Add commit message as prompt or summary of the bug\n- [ ] Add source code repos & file & commit_sha" ]
[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# instruction dataset for code bugfix", "## TODO:\n\n- [ ] Add commit message as prompt or summary of the bug\n- [ ] Add source code repos & file & commit_sha" ]
[ 15, 9, 32 ]
[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n# instruction dataset for code bugfix## TODO:\n\n- [ ] Add commit message as prompt or summary of the bug\n- [ ] Add source code repos & file & commit_sha" ]
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b562e57d6d9a3c5c4d67ddd334a969c67f93c005
# Dataset Card for "Europarl-ST-processed-mt-en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-en
[ "region:us" ]
2023-02-10T15:10:47+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 198087377, "num_examples": 602605}, {"name": "valid", "num_bytes": 27678568, "num_examples": 81968}, {"name": "test", "num_bytes": 29120332, "num_examples": 86170}], "download_size": 104863110, "dataset_size": 254886277}}
2023-02-10T16:04:39+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-en" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-en\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-en\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-en\"\n\nMore Information needed" ]
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7e5e15291fccfb528029621991762ec1941c740f
# Dataset Card for "Europarl-ST-processed-mt-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-es
[ "region:us" ]
2023-02-10T15:19:21+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 191389757, "num_examples": 553896}, {"name": "valid", "num_bytes": 26548844, "num_examples": 74770}, {"name": "test", "num_bytes": 27538253, "num_examples": 77952}], "download_size": 95321190, "dataset_size": 245476854}}
2023-02-10T16:12:33+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-es" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-es\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-es\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-es\"\n\nMore Information needed" ]
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