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train · 7.36k rows

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bobfu	null	null	null	false	null	false	bobfu/cats	2022-10-31T06:27:06.000Z	null	false	aa7407539ed836835ed51916fd092c02ce1dea1b	[]	[ "license:cc0-1.0" ]	https://huggingface.co/datasets/bobfu/cats/resolve/main/README.md	--- license: cc0-1.0 ---
nrajsubramanian	null	null	null	false	null	false	nrajsubramanian/usfaq	2022-10-31T06:57:45.000Z	null	false	c9369bf40a8f0788c3d438e9998d161d7f183910	[]	[ "license:mit" ]	https://huggingface.co/datasets/nrajsubramanian/usfaq/resolve/main/README.md	--- license: mit ---
KETI-AIR	null	There is no citation information	# 전문분야 영-한·중-한 번역 말뭉치 (식품) ## Usage ```python from datasets import load_dataset raw_datasets = load_dataset( "aihub_koenzh_food_translation.py", "base", cache_dir="huggingface_datasets", data_dir="data", ignore_verifications=True, ) dataset_train = raw_datasets["train"] for item in dataset_train: print(item) exit() ``` ## 데이터 관련 문의처 \| 담당자명 \| 전화번호 \| 이메일 \| \| ------------- \| ------------- \| ------------- \| \| 최규동 \| 1833-5926 \| ken.choi@twigfarm.net \| ## Copyright ### 데이터 소개 AI 허브에서 제공되는 인공지능 학습용 데이터(이하 ‘AI데이터’라고 함)는 과학기술정보통신부와 한국지능정보사회진흥원의 「지능정보산업 인프라 조성」 사업의 일환으로 구축되었으며, 본 사업의 유‧무형적 결과물인 데이터, AI 응용모델 및 데이터 저작도구의 소스, 각종 매뉴얼 등(이하 ‘AI데이터 등’)에 대한 일체의 권리는 AI데이터 등의 구축 수행기관 및 참여기관(이하 ‘수행기관 등’)과 한국지능정보사회진흥원에 있습니다. 본 AI데이터 등은 인공지능 기술 및 제품·서비스 발전을 위하여 구축하였으며, 지능형 제품・서비스, 챗봇 등 다양한 분야에서 영리적・비영리적 연구・개발 목적으로 활용할 수 있습니다. ### 데이터 이용정책 - 본 AI데이터 등을 이용하기 위해서 다음 사항에 동의하며 준수해야 함을 고지합니다. 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다. 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다. 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다. 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다. 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다. 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다. 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다. ### 데이터 다운로드 신청방법 1. AI 허브를 통해 제공 중인 AI데이터 등을 다운로드 받기 위해서는 별도의 신청자 본인 확인과 정보 제공, 목적을 밝히는 절차가 필요합니다. 2. AI데이터를 제외한 데이터 설명, 저작 도구 등은 별도의 신청 절차나 로그인 없이 이용이 가능합니다. 3. 한국지능정보사회진흥원이 권리자가 아닌 AI데이터 등은 해당 기관의 이용정책과 다운로드 절차를 따라야 하며 이는 AI 허브와 관련이 없음을 알려 드립니다.	false	66	false	KETI-AIR/aihub_koenzh_food_translation	2022-10-31T07:24:55.000Z	null	false	7b51ba33f0c9b9420b5706367a9a1b388ae51edb	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/KETI-AIR/aihub_koenzh_food_translation/resolve/main/README.md	--- license: apache-2.0 ---
KETI-AIR	null	There is no citation information	# 한국어-영어 번역 말뭉치(기술과학) ## 소개 - 기술과학(인공지능, 빅데이터, IT, SNS, 의학, 특허 등) 분야 등 한-영 번역 정확도가 상대적으로 낮은 분야의 데이터 구축을 통해 AI 기반 번역 기술 개발에 활용할 수 있는 학습 데이터셋을 구축하여 보다 원활한 기술과학 분야 관련 정보 소통 도모 ## 구축목적 - 기술 과학 분야 (ICT, 전기/전자/기계, 의학) 한-영 말뭉치 150만 문장 구축. 인공지능 번역 학습에 활용되기 위한 데이터셋 ## Usage ```python from datasets import load_dataset raw_datasets = load_dataset( "aihub_scitech20_translation.py", "base", cache_dir="huggingface_datasets", data_dir="data", ignore_verifications=True, ) dataset_train = raw_datasets["train"] for item in dataset_train: print(item) exit() ``` ## 데이터 관련 문의처 \| 담당자명 \| 전화번호 \| 이메일 \| \| ------------- \| ------------- \| ------------- \| \| 백선호(트위그팜) \| 02-1833-5926 \| ceo@twigfarm.net \| ## Copyright ### 데이터 소개 AI 허브에서 제공되는 인공지능 학습용 데이터(이하 ‘AI데이터’라고 함)는 과학기술정보통신부와 한국지능정보사회진흥원의 「지능정보산업 인프라 조성」 사업의 일환으로 구축되었으며, 본 사업의 유‧무형적 결과물인 데이터, AI 응용모델 및 데이터 저작도구의 소스, 각종 매뉴얼 등(이하 ‘AI데이터 등’)에 대한 일체의 권리는 AI데이터 등의 구축 수행기관 및 참여기관(이하 ‘수행기관 등’)과 한국지능정보사회진흥원에 있습니다. 본 AI데이터 등은 인공지능 기술 및 제품·서비스 발전을 위하여 구축하였으며, 지능형 제품・서비스, 챗봇 등 다양한 분야에서 영리적・비영리적 연구・개발 목적으로 활용할 수 있습니다. ### 데이터 이용정책 - 본 AI데이터 등을 이용하기 위해서 다음 사항에 동의하며 준수해야 함을 고지합니다. 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다. 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다. 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다. 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다. 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다. 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다. 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다. ### 데이터 다운로드 신청방법 1. AI 허브를 통해 제공 중인 AI데이터 등을 다운로드 받기 위해서는 별도의 신청자 본인 확인과 정보 제공, 목적을 밝히는 절차가 필요합니다. 2. AI데이터를 제외한 데이터 설명, 저작 도구 등은 별도의 신청 절차나 로그인 없이 이용이 가능합니다. 3. 한국지능정보사회진흥원이 권리자가 아닌 AI데이터 등은 해당 기관의 이용정책과 다운로드 절차를 따라야 하며 이는 AI 허브와 관련이 없음을 알려 드립니다.	false	73	false	KETI-AIR/aihub_scitech20_translation	2022-10-31T08:12:50.000Z	null	false	442f8c4c00aae04c37fcb44e7ecb44023af2b9ee	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/KETI-AIR/aihub_scitech20_translation/resolve/main/README.md	--- license: apache-2.0 ---
KETI-AIR	null	There is no citation information	# 한국어-영어 번역 말뭉치(사회과학) ## 소개 - 사회과학(정치, 경제, 금융, 행정, 교육, 법률 등) 분야 등 한-영 번역 정확도가 상대적으로 낮은 분야의 데이터 구축을 통해 AI 기반 번역 기술 개발에 활용할 수 있는 학습 데이터셋을 구축하여 보다 원활한 사회과학 분야 관련 정보 소통 도모 ## 구축목적 - 사회 과학 분야(법률, 교육, 경제, 문화/관광/예술)의 한-영 말뭉치 150만 문장 구축. 인공지능 번역 학습에 활용되기 위한 데이터셋 ## Usage ```python from datasets import load_dataset raw_datasets = load_dataset( "aihub_socialtech20_translation.py", "base", cache_dir="huggingface_datasets", data_dir="data", ignore_verifications=True, ) dataset_train = raw_datasets["train"] for item in dataset_train: print(item) exit() ``` ## 데이터 관련 문의처 \| 담당자명 \| 전화번호 \| 이메일 \| \| ------------- \| ------------- \| ------------- \| \| 백선호(트위그팜) \| 02-1833-5926 \| ceo@twigfarm.net \| ## Copyright ### 데이터 소개 AI 허브에서 제공되는 인공지능 학습용 데이터(이하 ‘AI데이터’라고 함)는 과학기술정보통신부와 한국지능정보사회진흥원의 「지능정보산업 인프라 조성」 사업의 일환으로 구축되었으며, 본 사업의 유‧무형적 결과물인 데이터, AI 응용모델 및 데이터 저작도구의 소스, 각종 매뉴얼 등(이하 ‘AI데이터 등’)에 대한 일체의 권리는 AI데이터 등의 구축 수행기관 및 참여기관(이하 ‘수행기관 등’)과 한국지능정보사회진흥원에 있습니다. 본 AI데이터 등은 인공지능 기술 및 제품·서비스 발전을 위하여 구축하였으며, 지능형 제품・서비스, 챗봇 등 다양한 분야에서 영리적・비영리적 연구・개발 목적으로 활용할 수 있습니다. ### 데이터 이용정책 - 본 AI데이터 등을 이용하기 위해서 다음 사항에 동의하며 준수해야 함을 고지합니다. 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다. 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다. 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다. 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다. 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다. 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다. 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다. ### 데이터 다운로드 신청방법 1. AI 허브를 통해 제공 중인 AI데이터 등을 다운로드 받기 위해서는 별도의 신청자 본인 확인과 정보 제공, 목적을 밝히는 절차가 필요합니다. 2. AI데이터를 제외한 데이터 설명, 저작 도구 등은 별도의 신청 절차나 로그인 없이 이용이 가능합니다. 3. 한국지능정보사회진흥원이 권리자가 아닌 AI데이터 등은 해당 기관의 이용정책과 다운로드 절차를 따라야 하며 이는 AI 허브와 관련이 없음을 알려 드립니다.	false	73	false	KETI-AIR/aihub_socialtech20_translation	2022-10-31T08:13:36.000Z	null	false	0ba2c99b0dde16ac5fe281bba5c99b4203039ea2	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/KETI-AIR/aihub_socialtech20_translation/resolve/main/README.md	--- license: apache-2.0 ---
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-staging-eval-project-083d71a4-50b6-4074-aa7d-a46eddb83f06-42	2022-10-31T09:11:37.000Z	null	false	892faabeccc027ec862b3889a6cb232ea04d4558	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:emotion" ]	https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-083d71a4-50b6-4074-aa7d-a46eddb83f06-42/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: ['matthews_correlation'] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-staging-eval-project-fe056b5c-7e36-4094-b3f2-84d1fbaaf77c-53	2022-10-31T09:25:45.000Z	null	false	50549635611eefc47cc7852b05fa7838e6b32ea3	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:emotion" ]	https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-fe056b5c-7e36-4094-b3f2-84d1fbaaf77c-53/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: ['matthews_correlation'] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
Sombredems	null	null	null	false	null	false	Sombredems/sags	2022-10-31T14:06:08.000Z	null	false	51ebe9dbdca6c10696c926181cea1f5e339d9aaa	[]	[ "license:other" ]	https://huggingface.co/datasets/Sombredems/sags/resolve/main/README.md	--- license: other ---
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-staging-eval-project-6da44258-8968-4823-8933-3375e1cfee89-64	2022-10-31T10:45:45.000Z	null	false	08d5a56fbbfbd8f8e7c6372cfb2f43159388f872	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:emotion" ]	https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-6da44258-8968-4823-8933-3375e1cfee89-64/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: ['matthews_correlation'] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-staging-eval-project-0d3aacb2-653b-459b-af2f-2d90d5362791-75	2022-10-31T11:00:48.000Z	null	false	35619762a828711029111dac816e3be6bfb33059	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:glue" ]	https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-0d3aacb2-653b-459b-af2f-2d90d5362791-75/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: autoevaluate/binary-classification metrics: ['matthews_correlation'] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: autoevaluate/binary-classification * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
bankawat	null	null	null	false	6	false	bankawat/ASR	2022-11-01T01:23:00.000Z	null	false	b940c76750ef805c687e0e49d274edcfb00e7214	[]	[ "license:unknown" ]	https://huggingface.co/datasets/bankawat/ASR/resolve/main/README.md	--- license: unknown ---
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-staging-eval-project-95ce44b7-7684-4cf4-b396-d486367937e4-86	2022-10-31T11:29:54.000Z	null	false	66839876b5ad5337aa11c89d71db04f3e1e2ff15	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:glue" ]	https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-95ce44b7-7684-4cf4-b396-d486367937e4-86/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: autoevaluate/binary-classification metrics: ['matthews_correlation'] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: autoevaluate/binary-classification * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-staging-eval-project-f69c187c-a1f8-462d-8272-41a77bd1f8ed-97	2022-10-31T11:32:57.000Z	null	false	4e0cf3f26014b3ececa0fe89260099593caeb3c0	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:glue" ]	https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-f69c187c-a1f8-462d-8272-41a77bd1f8ed-97/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: autoevaluate/binary-classification metrics: ['matthews_correlation'] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: autoevaluate/binary-classification * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
dominguesm	null	null	null	false	4	false	dominguesm/positive-reframing-ptbr-dataset	2022-10-31T12:43:59.000Z	null	false	c8287a1fdc3bb36bdbc84293a1a34cf4ee5384c5	[]	[ "arxiv:2204.02952" ]	https://huggingface.co/datasets/dominguesm/positive-reframing-ptbr-dataset/resolve/main/README.md	--- dataset_info: features: - name: original_text dtype: string - name: reframed_text dtype: string - name: strategy dtype: string - name: strategy_original_text dtype: string splits: - name: dev num_bytes: 318805 num_examples: 835 - name: test num_bytes: 321952 num_examples: 835 - name: train num_bytes: 2586935 num_examples: 6679 download_size: 1845244 dataset_size: 3227692 --- # positive-reframing-ptbr-dataset Version translated into pt-br of the dataset available in the work ["Inducing Positive Perspectives with Text Reframing"](https://arxiv.org/abs/2204.02952). Used in model [positive-reframing-ptbr](https://huggingface.co/dominguesm/positive-reframing-ptbr). Citation: > Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_. BibTeX: ```tex @inproceedings{ziems-etal-2022-positive-frames, title = "Inducing Positive Perspectives with Text Reframing", author = "Ziems, Caleb and Li, Minzhi and Zhang, Anthony and Yang, Diyi", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics", month = may, year = "2022", address = "Online and Dublin, Ireland", publisher = "Association for Computational Linguistics" } ```
ChristianOrr	null	null	null	false	null	false	ChristianOrr/mnist	2022-11-01T13:09:41.000Z	null	false	230d88b7e15e1fd2b0df276cb236559be413bff8	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/ChristianOrr/mnist/resolve/main/README.md	--- license: apache-2.0 ---
Dizex	null	null	null	false	120	false	Dizex/FoodBase	2022-10-31T12:48:53.000Z	null	false	eb792fb79d79a7e3b3b12eaea26dfb5a6ec23deb	[]	[]	https://huggingface.co/datasets/Dizex/FoodBase/resolve/main/README.md	--- dataset_info: features: - name: nltk_tokens sequence: string - name: iob_tags sequence: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2040036 num_examples: 600 - name: val num_bytes: 662190 num_examples: 200 download_size: 353747 dataset_size: 2702226 --- # Dataset Card for "FoodBase" Dataset for FoodBase corpus introduced in [this paper](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291). [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
idamarinella	null	null	null	false	1	false	idamarinella/portrait	2022-10-31T13:17:36.000Z	null	false	ba2198ba8d43324b6a3f3a6c0781465378d17944	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/idamarinella/portrait/resolve/main/README.md	--- license: afl-3.0 ---
rufimelo	null	null	null	false	null	false	rufimelo/PortugueseLegalSentences-v2	2022-11-01T13:14:38.000Z	null	false	5f56df48ab1ed088c122e2d73cd696e66e22e8e2	[]	[ "annotations_creators:no-annotation", "language_creators:found", "language:pt", "license:apache-2.0", "multilinguality:monolingual", "source_datasets:original" ]	https://huggingface.co/datasets/rufimelo/PortugueseLegalSentences-v2/resolve/main/README.md	--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - apache-2.0 multilinguality: - monolingual source_datasets: - original --- # Portuguese Legal Sentences Collection of Legal Sentences from the Portuguese Supreme Court of Justice The goal of this dataset was to be used for MLM and TSDAE Extended version of rufimelo/PortugueseLegalSentences-v1 200000/200000/100000 ### Contributions [@rufimelo99](https://github.com/rufimelo99)
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-eval-subjqa-grocery-9dee2c-1945965520	2022-10-31T14:45:47.000Z	null	false	3bddddbe0ef0f314a548753b200ec3e681492a8e	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:subjqa" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-subjqa-grocery-9dee2c-1945965520/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - subjqa eval_info: task: extractive_question_answering model: SiraH/bert-finetuned-squad metrics: [] dataset_name: subjqa dataset_config: grocery dataset_split: train col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: SiraH/bert-finetuned-squad * Dataset: subjqa * Config: grocery * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@sushant-joshi](https://huggingface.co/sushant-joshi) for evaluating this model.
Norod78	null	null	null	false	24	false	Norod78/cartoon-blip-captions	2022-11-09T16:27:57.000Z	null	false	db95ae658758c7b2337a54a2facabefe3af9698a	[]	[ "size_categories:n<1K", "task_categories:text-to-image", "license:cc-by-nc-sa-4.0", "annotations_creators:machine-generated", "language:en", "language_creators:other", "multilinguality:monolingual" ]	https://huggingface.co/datasets/Norod78/cartoon-blip-captions/resolve/main/README.md	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 190959102.953 num_examples: 3141 download_size: 190279356 dataset_size: 190959102.953 pretty_name: 'Cartoon BLIP captions' size_categories: - n<1K tags: [] task_categories: - text-to-image license: cc-by-nc-sa-4.0 annotations_creators: - machine-generated language: - en language_creators: - other multilinguality: - monolingual --- # Dataset Card for "cartoon-blip-captions"
LiveEvil	null	null	null	false	14	false	LiveEvil/lucyrev1	2022-10-31T15:40:56.000Z	null	false	607724cf2959d50f0a171e8ff42a7233f96dcd19	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/LiveEvil/lucyrev1/resolve/main/README.md	--- license: apache-2.0 ---
Mostafa3zazi	null	null	null	false	13	false	Mostafa3zazi/Arabic_SQuAD	2022-10-31T19:32:25.000Z	null	false	17d5b9dafdaa266f17aedfaa0154fe56411cdb44	[]	[]	https://huggingface.co/datasets/Mostafa3zazi/Arabic_SQuAD/resolve/main/README.md	--- dataset_info: features: - name: index dtype: string - name: question dtype: string - name: context dtype: string - name: text dtype: string - name: answer_start dtype: int64 - name: c_id dtype: int64 splits: - name: train num_bytes: 61868003 num_examples: 48344 download_size: 10512179 dataset_size: 61868003 --- # Dataset Card for "Arabic_SQuAD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- # Citation ``` @inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", } ``` ---
Jirui	null	null	null	false	null	false	Jirui/testing	2022-10-31T19:42:52.000Z	null	false	60d116ecea74a9d94acfbebd19dd061ab42f627a	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/Jirui/testing/resolve/main/README.md	--- license: afl-3.0 ---
ProGamerGov	null	null	null	false	5	false	ProGamerGov/StableDiffusion-v1-5-Regularization-Images	2022-11-15T16:34:59.000Z	null	false	76d4499ddfce3e6c4d0ebeadee1fb3d19d5677bf	[]	[ "license:mit" ]	https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images/resolve/main/README.md	--- license: mit --- A collection of regularization / class instance datasets for the [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model to use for DreamBooth prior preservation loss training. Files labeled with "mse vae" used the [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) VAE. For ease of use, datasets are stored as zip files containing 512x512 PNG images. The number of images in each zip file is specified at the end of the filename. There is currently a bug where HuggingFace is incorrectly reporting that the datasets are pickled. They are not picked, they are simple ZIP files containing the images. Currently this repository contains the following datasets (datasets are named after the prompt they used): Art Styles * "artwork style": 4125 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "illustration style": 3050 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "erotic photography": 2760 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. People * "person": 2115 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "woman": 4420 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "guy": 4820 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "supermodel": 4411 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "bikini model": 4260 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "sexy athlete": 5020 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "femme fatale": 4725 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. Animals * "kitty": 5100 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "cat": 2050 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. Vehicles * "fighter jet": 1600 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "train": 2669 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. * "car": 3150 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. Themes * "cyberpunk": 3040 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. I used the "Generate Forever" feature in [AUTOMATIC1111's WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to create thousands of images for each dataset. Every image in a particular dataset uses the exact same settings, with only the seed value being different. You can use my regularization / class image datasets with: https://github.com/ShivamShrirao/diffusers, https://github.com/JoePenna/Dreambooth-Stable-Diffusion, https://github.com/TheLastBen/fast-stable-diffusion, and any other DreamBooth projects that have support for prior preservation loss.
FAERS-PubMed	null	null	null	false	9	false	FAERS-PubMed/FAERS-filenames-latest	2022-11-07T18:39:11.000Z	null	false	985d299a0bd9817d6d0dba79f732a37883bbda1b	[]	[]	https://huggingface.co/datasets/FAERS-PubMed/FAERS-filenames-latest/resolve/main/README.md	--- dataset_info: features: - name: filenames dtype: string splits: - name: train num_bytes: 1590 num_examples: 60 download_size: 0 dataset_size: 1590 --- # Dataset Card for "FAERS-filenames-latest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FAERS-PubMed	null	null	null	false	null	false	FAERS-PubMed/FAERS-filenames-2022-10-31	2022-10-31T23:12:27.000Z	null	false	ca2858e018bf1d532e71be25b14fdc669356045a	[]	[]	https://huggingface.co/datasets/FAERS-PubMed/FAERS-filenames-2022-10-31/resolve/main/README.md	--- dataset_info: features: - name: filenames dtype: string splits: - name: train num_bytes: 1590 num_examples: 60 download_size: 1039 dataset_size: 1590 --- # Dataset Card for "FAERS-filenames-2022-10-31" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FAERS-PubMed	null	null	null	false	5	false	FAERS-PubMed/PubMed-filenames-latest	2022-11-11T22:51:22.000Z	null	false	be0a531dd08a41da4763966dc217be6f4b1d8e9d	[]	[]	https://huggingface.co/datasets/FAERS-PubMed/PubMed-filenames-latest/resolve/main/README.md	--- dataset_info: features: - name: filenames dtype: string splits: - name: train num_bytes: 72410 num_examples: 1114 download_size: 0 dataset_size: 72410 --- # Dataset Card for "PubMed-filenames-latest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FAERS-PubMed	null	null	null	false	null	false	FAERS-PubMed/PubMed-filenames-2022-10-31	2022-10-31T23:26:40.000Z	null	false	a6c9202839ffeaefe3ccb747d4801397b524c584	[]	[]	https://huggingface.co/datasets/FAERS-PubMed/PubMed-filenames-2022-10-31/resolve/main/README.md	--- dataset_info: features: - name: filenames dtype: string splits: - name: train num_bytes: 72410 num_examples: 1114 download_size: 8582 dataset_size: 72410 --- # Dataset Card for "PubMed-filenames-2022-10-31" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
digiSilk	null	null	null	false	null	false	digiSilk/real_ruby	2022-11-01T00:06:52.000Z	null	false	3bc134f4be0eb287bca607e529ef11f06b7cea62	[]	[]	https://huggingface.co/datasets/digiSilk/real_ruby/resolve/main/README.md	My initial attempt at creating a dataset intended to create a customized model to include Ruby.
Onur-Ozbek-Crafty-Apes-VFX	null	null	null	false	10	false	Onur-Ozbek-Crafty-Apes-VFX/CAVFX-LAION	2022-11-01T10:24:40.000Z	null	false	4845af940bf5042c1ddd28df29cf32d12c88b1d3	[]	[ "license:mit" ]	https://huggingface.co/datasets/Onur-Ozbek-Crafty-Apes-VFX/CAVFX-LAION/resolve/main/README.md	--- license: mit ---
shahidul034	null	null	null	false	56	false	shahidul034/text_summarization_dataset1	2022-11-01T02:13:08.000Z	null	false	4d005b3e1a5f1e558bf1e53ba4d4c6835c9fc667	[]	[]	https://huggingface.co/datasets/shahidul034/text_summarization_dataset1/resolve/main/README.md	--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 129017829 num_examples: 106525 download_size: 43557623 dataset_size: 129017829 --- # Dataset Card for "text_summarization_dataset1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shahidul034	null	null	null	false	28	false	shahidul034/text_summarization_dataset2	2022-11-01T02:14:47.000Z	null	false	55b0bfdf562703f905a60e4522bb56547c7406e8	[]	[]	https://huggingface.co/datasets/shahidul034/text_summarization_dataset2/resolve/main/README.md	--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 125954432 num_examples: 105252 download_size: 42217690 dataset_size: 125954432 --- # Dataset Card for "text_summarization_dataset2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shahidul034	null	null	null	false	26	false	shahidul034/text_summarization_dataset3	2022-11-01T02:15:51.000Z	null	false	01b5203a600c3bde5dbf229adee63962608e0714	[]	[]	https://huggingface.co/datasets/shahidul034/text_summarization_dataset3/resolve/main/README.md	--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 123296943 num_examples: 103365 download_size: 41220771 dataset_size: 123296943 --- # Dataset Card for "text_summarization_dataset3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shahidul034	null	null	null	false	18	false	shahidul034/text_summarization_dataset4	2022-11-01T02:16:16.000Z	null	false	a4910c6c1646eacfcb88f7703e2e0bd7fdee559c	[]	[]	https://huggingface.co/datasets/shahidul034/text_summarization_dataset4/resolve/main/README.md	--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 111909333 num_examples: 87633 download_size: 38273895 dataset_size: 111909333 --- # Dataset Card for "text_summarization_dataset4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-cadd10-1947965536	2022-11-01T02:41:47.000Z	null	false	945ac8484e1efc07ad26996071343822dad8dc3b	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:adversarial_qa" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-cadd10-1947965536/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - adversarial_qa eval_info: task: extractive_question_answering model: 123tarunanand/roberta-base-finetuned metrics: [] dataset_name: adversarial_qa dataset_config: adversarialQA dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: 123tarunanand/roberta-base-finetuned * Dataset: adversarial_qa * Config: adversarialQA * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MHassanSaleem](https://huggingface.co/MHassanSaleem) for evaluating this model.
n1ghtf4l1	null	null	null	false	null	false	n1ghtf4l1/super-collider	2022-11-01T04:23:41.000Z	null	false	5c0ac9c4b877a715105c979c30e06e6e15dd4754	[]	[ "license:mit" ]	https://huggingface.co/datasets/n1ghtf4l1/super-collider/resolve/main/README.md	--- license: mit ---
Poison413	null	null	null	false	null	false	Poison413/Installation01	2022-11-01T07:21:12.000Z	null	false	abd91a59bfb0d76131319a2a5288dee5cb26bf58	[]	[ "doi:10.57967/hf/0080", "license:unknown" ]	https://huggingface.co/datasets/Poison413/Installation01/resolve/main/README.md	--- license: unknown ---
hakancam	null	null	null	false	null	false	hakancam/avats	2022-11-01T06:15:11.000Z	null	false	c499f832e3b97fec8889ddd10ec8765f7386474a	[]	[ "license:bigscience-openrail-m" ]	https://huggingface.co/datasets/hakancam/avats/resolve/main/README.md	--- license: bigscience-openrail-m ---
ctu-aic	null	null	null	false	null	false	ctu-aic/ctkfacts	2022-11-01T06:47:03.000Z	null	false	16a66c3fda4c2dbb68195d70bf51148d3edb86cf	[]	[ "arxiv:2201.11115", "license:cc-by-sa-3.0" ]	https://huggingface.co/datasets/ctu-aic/ctkfacts/resolve/main/README.md	--- license: cc-by-sa-3.0 --- # CTKFacts dataset for Document retrieval Czech Natural Language Inference dataset of ~3K evidence-claim pairs labelled with SUPPORTS, REFUTES or NOT ENOUGH INFO veracity labels. Extracted from a round of fact-checking experiments concluded and described within the [CsFEVER andCTKFacts: Acquiring Czech data for Fact Verification](https://arxiv.org/abs/2201.11115) paper currently being revised for publication in LREV journal. ## NLI version Can be found at https://huggingface.co/datasets/ctu-aic/ctkfacts_nli
qanastek	null	@article{baker2015automatic, title={Automatic semantic classification of scientific literature according to the hallmarks of cancer}, author={Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and H{\"o}gberg, Johan and Stenius, Ulla and Korhonen, Anna}, journal={Bioinformatics}, volume={32}, number={3}, pages={432--440}, year={2015}, publisher={Oxford University Press} } @article{baker2017cancer, title={Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer}, author={Baker, Simon and Ali, Imran and Silins, Ilona and Pyysalo, Sampo and Guo, Yufan and H{\"o}gberg, Johan and Stenius, Ulla and Korhonen, Anna}, journal={Bioinformatics}, volume={33}, number={24}, pages={3973--3981}, year={2017}, publisher={Oxford University Press} } @article{baker2017cancer, title={Cancer hallmark text classification using convolutional neural networks}, author={Baker, Simon and Korhonen, Anna-Leena and Pyysalo, Sampo}, year={2016} } @article{baker2017initializing, title={Initializing neural networks for hierarchical multi-label text classification}, author={Baker, Simon and Korhonen, Anna}, journal={BioNLP 2017}, pages={307--315}, year={2017} }	The Hallmarks of Cancer Corpus for text classification The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to a taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus. The labels are found under the "labels" directory, while the tokenized text can be found under "text" directory. The filenames are the corresponding PubMed IDs (PMID). In addition to the HOC corpus, we also have the [Cancer Hallmarks Analytics Tool](http://chat.lionproject.net/) which classifes all of PubMed according to the HoC taxonomy.	false	1	false	qanastek/HoC	2022-11-01T15:03:11.000Z	null	false	6f8ce801f8cf4cc9d58c08f61f3424ad612f2f67	[]	[ "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "language:en", "size_categories:1K<n<10K", "source_datasets:original", "task_categories:text-classification", "task_ids:multi-class-classification", "language_bcp47:en-US" ]	https://huggingface.co/datasets/qanastek/HoC/resolve/main/README.md	--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - en size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: HoC language_bcp47: - en-US --- # HoC : Hallmarks of Cancer Corpus ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [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) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [No Warranty](#no-warranty) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://s-baker.net/resource/hoc/ - Repository: https://github.com/sb895/Hallmarks-of-Cancer - Paper: https://academic.oup.com/bioinformatics/article/32/3/432/1743783 - Leaderboard: https://paperswithcode.com/dataset/hoc-1 - Point of Contact: [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr) ### Dataset Summary The Hallmarks of Cancer Corpus for text classification The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to a taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus. The labels are found under the "labels" directory, while the tokenized text can be found under "text" directory. The filenames are the corresponding PubMed IDs (PMID). In addition to the HOC corpus, we also have the [Cancer Hallmarks Analytics Tool](http://chat.lionproject.net/) which classifes all of PubMed according to the HoC taxonomy. ### Supported Tasks and Leaderboards The dataset can be used to train a model for `multi-class-classification`. ### Languages The corpora consists of PubMed article only in english: - `English - United States (en-US)` ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("qanastek/HoC") validation = dataset["validation"] print("First element of the validation set : ", validation[0]) ``` ## Dataset Structure ### Data Instances ```json { "document_id": "12634122_5", "text": "Genes that were overexpressed in OM3 included oncogenes , cell cycle regulators , and those involved in signal transduction , whereas genes for DNA repair enzymes and inhibitors of transformation and metastasis were suppressed .", "label": [9, 5, 0, 6] } ``` ### Data Fields `document_id`: Unique identifier of the document. `text`: Raw text of the PubMed abstracts. `label`: One of the 10 currently known hallmarks of cancer. \| Hallmark \| Search term \| \|:-------------------------------------------:\|:-------------------------------------------:\| \| 1. Sustaining proliferative signaling (PS) \| Proliferation Receptor Cancer \| \| \| 'Growth factor' Cancer \| \| \| 'Cell cycle' Cancer \| \| 2. Evading growth suppressors (GS) \| 'Cell cycle' Cancer \| \| \| 'Contact inhibition' \| \| 3. Resisting cell death (CD) \| Apoptosis Cancer \| \| \| Necrosis Cancer \| \| \| Autophagy Cancer \| \| 4. Enabling replicative immortality (RI) \| Senescence Cancer \| \| \| Immortalization Cancer \| \| 5. Inducing angiogenesis (A) \| Angiogenesis Cancer \| \| \| 'Angiogenic factor' \| \| 6. Activating invasion & metastasis (IM) \| Metastasis Invasion Cancer \| \| 7. Genome instability & mutation (GI) \| Mutation Cancer \| \| \| 'DNA repair' Cancer \| \| \| Adducts Cancer \| \| \| 'Strand breaks' Cancer \| \| \| 'DNA damage' Cancer \| \| 8. Tumor-promoting inflammation (TPI) \| Inflammation Cancer \| \| \| 'Oxidative stress' Cancer \| \| \| Inflammation 'Immune response' Cancer \| \| 9. Deregulating cellular energetics (CE) \| Glycolysis Cancer; 'Warburg effect' Cancer \| \| 10. Avoiding immune destruction (ID) \| 'Immune system' Cancer \| \| \| Immunosuppression Cancer \| ### Data Splits Distribution of data for the 10 hallmarks: \| Hallmark \| No. abstracts \| No. sentences \| \|:------------:\|:-----------------:\|:-----------------:\| \| 1. PS \| 462 \| 993 \| \| 2. GS \| 242 \| 468 \| \| 3. CD \| 430 \| 883 \| \| 4. RI \| 115 \| 295 \| \| 5. A \| 143 \| 357 \| \| 6. IM \| 291 \| 667 \| \| 7. GI \| 333 \| 771 \| \| 8. TPI \| 194 \| 437 \| \| 9. CE \| 105 \| 213 \| \| 10. ID \| 108 \| 226 \| ## Dataset Creation ### Source Data #### Who are the source language producers? The corpus has been produced and uploaded by Baker Simon and Silins Ilona and Guo Yufan and Ali Imran and Hogberg Johan and Stenius Ulla and Korhonen Anna. ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators __HoC__: Baker Simon and Silins Ilona and Guo Yufan and Ali Imran and Hogberg Johan and Stenius Ulla and Korhonen Anna __Hugging Face__: Labrak Yanis (Not affiliated with the original corpus) ### Licensing Information ```plain GNU General Public License v3.0 ``` ```plain Permissions - Commercial use - Modification - Distribution - Patent use - Private use Limitations - Liability - Warranty Conditions - License and copyright notice - State changes - Disclose source - Same license ``` ### Citation Information We would very much appreciate it if you cite our publications: [Automatic semantic classification of scientific literature according to the hallmarks of cancer](https://academic.oup.com/bioinformatics/article/32/3/432/1743783) ```bibtex @article{baker2015automatic, title={Automatic semantic classification of scientific literature according to the hallmarks of cancer}, author={Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and H{\"o}gberg, Johan and Stenius, Ulla and Korhonen, Anna}, journal={Bioinformatics}, volume={32}, number={3}, pages={432--440}, year={2015}, publisher={Oxford University Press} } ``` [Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer](https://www.repository.cam.ac.uk/bitstream/handle/1810/265268/btx454.pdf?sequence=8&isAllowed=y) ```bibtex @article{baker2017cancer, title={Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer}, author={Baker, Simon and Ali, Imran and Silins, Ilona and Pyysalo, Sampo and Guo, Yufan and H{\"o}gberg, Johan and Stenius, Ulla and Korhonen, Anna}, journal={Bioinformatics}, volume={33}, number={24}, pages={3973--3981}, year={2017}, publisher={Oxford University Press} } ``` [Cancer hallmark text classification using convolutional neural networks](https://www.repository.cam.ac.uk/bitstream/handle/1810/270037/BIOTXTM2016.pdf?sequence=1&isAllowed=y) ```bibtex @article{baker2017cancer, title={Cancer hallmark text classification using convolutional neural networks}, author={Baker, Simon and Korhonen, Anna-Leena and Pyysalo, Sampo}, year={2016} } ``` [Initializing neural networks for hierarchical multi-label text classification](http://www.aclweb.org/anthology/W17-2339) ```bibtex @article{baker2017initializing, title={Initializing neural networks for hierarchical multi-label text classification}, author={Baker, Simon and Korhonen, Anna}, journal={BioNLP 2017}, pages={307--315}, year={2017} } ```
ellabettison	null	null	null	false	2	false	ellabettison/processed_bert_dataset_padded_med	2022-11-01T11:04:13.000Z	null	false	fc0003ddab02485923b6daf58c6288773c752036	[]	[]	https://huggingface.co/datasets/ellabettison/processed_bert_dataset_padded_med/resolve/main/README.md	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: test num_bytes: 12801600.0 num_examples: 100000 - name: train num_bytes: 115214400.0 num_examples: 900000 download_size: 17728113 dataset_size: 128016000.0 --- # Dataset Card for "processed_bert_dataset_padded_med" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ellabettison	null	null	null	false	20	false	ellabettison/processed_gpt2_dataset_padded_med	2022-11-01T12:00:28.000Z	null	false	742779c2c744cf24c656739d35fc5897e262ee07	[]	[]	https://huggingface.co/datasets/ellabettison/processed_gpt2_dataset_padded_med/resolve/main/README.md	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: test num_bytes: 10801200.0 num_examples: 100000 - name: train num_bytes: 97210800.0 num_examples: 900000 download_size: 16878257 dataset_size: 108012000.0 --- # Dataset Card for "processed_gpt2_dataset_padded_med" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bond005	null	null	null	false	29	false	bond005/sova_rudevices	2022-11-01T15:59:30.000Z	null	false	d9197eacfb0afff29d90a2d4e7d0d98a5dfb54bc	[]	[ "annotations_creators:expert-generated", "language_creators:crowdsourced", "language:ru", "license:cc-by-4.0", "multilinguality:monolingual", "size_categories:10K<n<100k", "source_datasets:extended", "task_categories:automatic-speech-recognition", "task_categories:audio-classification" ]	https://huggingface.co/datasets/bond005/sova_rudevices/resolve/main/README.md	--- pretty_name: RuDevices annotations_creators: - expert-generated language_creators: - crowdsourced language: - ru license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: size_categories: - 10K<n<100k source_datasets: - extended task_categories: - automatic-speech-recognition - audio-classification --- # Dataset Card for sova_rudevices ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [SOVA RuDevices](https://github.com/sovaai/sova-dataset) - Repository: [SOVA Dataset](https://github.com/sovaai/sova-dataset) - Leaderboard: [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - Point of Contact: [SOVA.ai](mailto:support@sova.ai) ### Dataset Summary SOVA Dataset is free public STT/ASR dataset. It consists of several parts, one of them is SOVA RuDevices. This part is an acoustic corpus of approximately 100 hours of 16kHz Russian live speech with manual annotating, prepared by [SOVA.ai team](https://github.com/sovaai). Authors do not divide the dataset into train, validation and test subsets. Therefore, I was compelled to prepare this splitting. The training subset includes more than 82 hours, the validation subset includes approximately 6 hours, and the test subset includes approximately 6 hours too. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. ### Languages The audio is in Russian. ## Dataset Structure ### Data Instances A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided. ``` {'audio': {'path': '/home/bond005/datasets/sova_rudevices/data/train/00003ec0-1257-42d1-b475-db1cd548092e.wav', 'array': array([ 0.00787354, 0.00735474, 0.00714111, ..., -0.00018311, -0.00015259, -0.00018311]), dtype=float32), 'sampling_rate': 16000}, 'transcription': 'мне получше стало'} ``` ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - transcription: the transcription of the audio file. ### Data Splits This dataset consists of three splits: training, validation, and test. This splitting was realized with accounting of internal structure of SOVA RuDevices (the validation split is based on the subdirectory `0`, and the test split is based on the subdirectory `1` of the original dataset), but audio recordings of the same speakers can be in different splits at the same time (the opposite is not guaranteed). \| \| Train \| Validation \| Test \| \| ----- \| ------ \| ---------- \| ----- \| \| examples \| 81607 \| 5835 \| 5799 \| \| hours \| 82.4h \| 5.9h \| 5.8h \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process All recorded audio files were manually annotated. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Egor Zubarev, Timofey Moskalets, and SOVA.ai team. ### Licensing Information [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @misc{sova2021rudevices, author = {Zubarev, Egor and Moskalets, Timofey and SOVA.ai}, title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech}, publisher = {GitHub}, journal = {GitHub repository}, year = {2021}, howpublished = {\url{https://github.com/sovaai/sova-dataset}}, } ``` ### Contributions Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
Deepak2846	null	null	null	false	83	false	Deepak2846/name	2022-11-04T15:38:27.000Z	null	false	95ea49255399ff8095f00fd10776858582deec6d	[]	[ "license:unknown" ]	https://huggingface.co/datasets/Deepak2846/name/resolve/main/README.md	--- license: unknown ---
rufimelo	null	null	null	false	null	false	rufimelo/PortugueseLegalSentences-v3	2022-11-01T13:15:47.000Z	null	false	d278dfd8a801d43f5f3ce23228118d8d53faca81	[]	[ "annotations_creators:no-annotation", "language_creators:found", "language:pt", "license:apache-2.0", "multilinguality:monolingual", "source_datasets:original" ]	https://huggingface.co/datasets/rufimelo/PortugueseLegalSentences-v3/resolve/main/README.md	--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - apache-2.0 multilinguality: - monolingual source_datasets: - original --- # Portuguese Legal Sentences Collection of Legal Sentences from the Portuguese Supreme Court of Justice The goal of this dataset was to be used for MLM and TSDAE Extended version of rufimelo/PortugueseLegalSentences-v1 400000/50000/50000 ### Contributions [@rufimelo99](https://github.com/rufimelo99)
KETI-AIR	null	There is no citation information	# 뉴스 기사 기계독해 데이터 ## 소개 국내 종합일간지 및 지역신문의 뉴스기사를 지문으로 활용, 자연어 질의 응답으로 이루어진 인공지능 학습 데이터 ## 구축목적 국내 언론사(중앙일보 등 종합일간지 및 지방지)의 뉴스기사를 지문으로 활용하여 4가지 유형의 질문-답변 세트를 생성, 인공지능을 훈련하기 위한 데이터셋 ## Usage ```python from datasets import load_dataset raw_datasets = load_dataset( "aihub_news_mrc.py", cache_dir="huggingface_datasets", data_dir="data", ignore_verifications=True, ) dataset_train = raw_datasets["train"] for item in dataset_train: print(item) exit() ``` ## 데이터 관련 문의처 \| 담당자명 \| 전화번호 \| 이메일 \| \| ------------- \| ------------- \| ------------- \| \| 김민경 \| 02-6952-9201 \| mkgenie@42maru.ai \| ## Copyright ### 데이터 소개 AI 허브에서 제공되는 인공지능 학습용 데이터(이하 ‘AI데이터’라고 함)는 과학기술정보통신부와 한국지능정보사회진흥원의 「지능정보산업 인프라 조성」 사업의 일환으로 구축되었으며, 본 사업의 유‧무형적 결과물인 데이터, AI 응용모델 및 데이터 저작도구의 소스, 각종 매뉴얼 등(이하 ‘AI데이터 등’)에 대한 일체의 권리는 AI데이터 등의 구축 수행기관 및 참여기관(이하 ‘수행기관 등’)과 한국지능정보사회진흥원에 있습니다. 본 AI데이터 등은 인공지능 기술 및 제품·서비스 발전을 위하여 구축하였으며, 지능형 제품・서비스, 챗봇 등 다양한 분야에서 영리적・비영리적 연구・개발 목적으로 활용할 수 있습니다. ### 데이터 이용정책 - 본 AI데이터 등을 이용하기 위해서 다음 사항에 동의하며 준수해야 함을 고지합니다. 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다. 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다. 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다. 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다. 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다. 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다. 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다. ### 데이터 다운로드 신청방법 1. AI 허브를 통해 제공 중인 AI데이터 등을 다운로드 받기 위해서는 별도의 신청자 본인 확인과 정보 제공, 목적을 밝히는 절차가 필요합니다. 2. AI데이터를 제외한 데이터 설명, 저작 도구 등은 별도의 신청 절차나 로그인 없이 이용이 가능합니다. 3. 한국지능정보사회진흥원이 권리자가 아닌 AI데이터 등은 해당 기관의 이용정책과 다운로드 절차를 따라야 하며 이는 AI 허브와 관련이 없음을 알려 드립니다.	false	277	false	KETI-AIR/aihub_news_mrc	2022-11-02T07:43:03.000Z	null	false	44c359b77af23165acac3dfe32a092aa7a9c00fb	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/KETI-AIR/aihub_news_mrc/resolve/main/README.md	--- license: apache-2.0 ---
kmeng	null	null	null	false	null	false	kmeng/CEUSN	2022-11-03T18:11:24.000Z	null	false	40b3da0d325bf3f45c998f874e4ac5b35d4d92ae	[]	[ "license:unknown" ]	https://huggingface.co/datasets/kmeng/CEUSN/resolve/main/README.md	--- license: unknown ---
LiveEvil	null	null	null	false	2	false	LiveEvil/TestText	2022-11-01T18:53:47.000Z	null	false	24d4cac8c5b21c7396382d6cc6952dabe95c8dcb	[]	[ "license:openrail" ]	https://huggingface.co/datasets/LiveEvil/TestText/resolve/main/README.md	--- license: openrail ---
ashraq	null	null	null	false	12	false	ashraq/fashion-product-images-small	2022-11-01T20:25:52.000Z	null	false	3859c76db2f6f3d3b9a3863345e3ccdbff75879d	[]	[]	https://huggingface.co/datasets/ashraq/fashion-product-images-small/resolve/main/README.md	--- dataset_info: features: - name: id dtype: int64 - name: gender dtype: string - name: masterCategory dtype: string - name: subCategory dtype: string - name: articleType dtype: string - name: baseColour dtype: string - name: season dtype: string - name: year dtype: float64 - name: usage dtype: string - name: productDisplayName dtype: string - name: image dtype: image splits: - name: train num_bytes: 546202015.44 num_examples: 44072 download_size: 271496441 dataset_size: 546202015.44 --- # Dataset Card for "fashion-product-images-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Data was obtained from [here](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small)
Valentingmz	null	null	null	false	null	false	Valentingmz/Repositor	2022-11-01T20:39:51.000Z	null	false	caf62a8694ff3c9fa6523dc1f74d446569fded46	[]	[]	https://huggingface.co/datasets/Valentingmz/Repositor/resolve/main/README.md	![Luz_life_in_the_process_of_flooding_a_coastal_city_photorealist_04bf00d0-1618-48d2-abc3-2811e92b39d8.png](https://s3.amazonaws.com/moonup/production/uploads/1667335085896-636035d18fb9c2420ffcb872.png) ![Luz_life_in_the_process_of_flooding_a_coastal_city_photorealist_405f00d5-fa48-4825-b7b9-0f6f35312c61.png](https://s3.amazonaws.com/moonup/production/uploads/1667335127099-636035d18fb9c2420ffcb872.png) ![Luz_life_in_the_process_of_flooding_a_coastal_city_photorealist_04bf00d0-1618-48d2-abc3-2811e92b39d8.png](https://s3.amazonaws.com/moonup/production/uploads/1667335139452-636035d18fb9c2420ffcb872.png)
LiveEvil	null	null	null	false	2	false	LiveEvil/mysheet	2022-11-01T20:54:32.000Z	null	false	031c7b7df6f699fdcd5041c2810bab60907dc354	[]	[ "license:openrail" ]	https://huggingface.co/datasets/LiveEvil/mysheet/resolve/main/README.md	--- license: openrail ---
LiveEvil	null	null	null	false	null	false	LiveEvil/autotrain-data-mysheet	2022-11-01T20:55:52.000Z	null	false	a311ec1ad64e5e5a005e8759b8dde88acecc42eb	[]	[ "language:en" ]	https://huggingface.co/datasets/LiveEvil/autotrain-data-mysheet/resolve/main/README.md	--- language: - en --- # AutoTrain Dataset for project: mysheet ## Dataset Description This dataset has been automatically processed by AutoTrain for project mysheet. ### 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 [ { "context": "The term \u201cpseudocode\u201d refers to writing code in a humanly understandable language such as English, and breaking it down to its core concepts.", "question": "What is pseudocode?", "answers.text": [ "Pseudocode is breaking down your code in English." ], "answers.answer_start": [ 33 ] }, { "context": "Python is an interactive programming language designed for API and Machine Learning use.", "question": "What is Python?", "answers.text": [ "Python is an interactive programming language." ], "answers.answer_start": [ 0 ] } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "context": "Value(dtype='string', id=None)", "question": "Value(dtype='string', id=None)", "answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)", "answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, 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 \| 3 \| \| valid \| 1 \|
LiveEvil	null	null	null	false	null	false	LiveEvil/EsCheck-Paragraph	2022-11-02T15:15:44.000Z	null	false	36cf8a781bf9396d6b7e7fb536ef635571fbec77	[]	[ "license:openrail" ]	https://huggingface.co/datasets/LiveEvil/EsCheck-Paragraph/resolve/main/README.md	--- license: openrail --- This is a ParaModeler, for rating hook/grabbers of an introduction paragraph.
learningbot	null	null	null	false	null	false	learningbot/hadoop	2022-11-01T23:24:51.000Z	null	false	3393491a7c997952b11efaa843193f618d82f6cb	[]	[ "license:gpl-3.0" ]	https://huggingface.co/datasets/learningbot/hadoop/resolve/main/README.md	--- license: gpl-3.0 ---
henryscheible	null	null	null	false	22	false	henryscheible/crows_pairs	2022-11-02T02:25:56.000Z	null	false	7c394b430826ee4b382c888e833699dffaea5423	[]	[]	https://huggingface.co/datasets/henryscheible/crows_pairs/resolve/main/README.md	--- dataset_info: features: - name: label dtype: int64 - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: test num_bytes: 146765.59151193633 num_examples: 302 - name: train num_bytes: 586090.4084880636 num_examples: 1206 download_size: 113445 dataset_size: 732856.0 --- # Dataset Card for "crows_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
beyond	null	null	null	false	2	false	beyond/chinese_clean_passages_80m	2022-11-02T05:32:57.000Z	null	false	ae53e77f172e94cca3bb9d685eb6660c7917f35d	[]	[]	https://huggingface.co/datasets/beyond/chinese_clean_passages_80m/resolve/main/README.md	--- dataset_info: features: - name: passage dtype: string splits: - name: train num_bytes: 18979214734 num_examples: 88328203 download_size: 1025261393 dataset_size: 18979214734 --- # `chinese_clean_passages_80m` 包含8千余万（88328203）个纯净中文段落，不包含任何字母、数字。\ Containing more than 80 million pure \& clean Chinese passages, without any letters/digits/special tokens. 文本长度大部分介于50\~200个汉字之间。\ The passage length is approximately 50\~200 Chinese characters. 通过`datasets.load_dataset()`下载数据，会产生38个大小约340M的数据包，共约12GB，所以请确保有足够空间。\ Downloading the dataset will result in 38 data shards each of which is about 340M and 12GB in total. Make sure there's enough space in your device:) ``` >>> passage_dataset = load_dataset('beyond/chinese_clean_passages_80m') <<< Downloading data: 100%\|█\| 341M/341M [00:06<00:00, 52.0MB Downloading data: 100%\|█\| 342M/342M [00:06<00:00, 54.4MB Downloading data: 100%\|█\| 341M/341M [00:06<00:00, 49.1MB Downloading data: 100%\|█\| 341M/341M [00:14<00:00, 23.5MB Downloading data: 100%\|█\| 341M/341M [00:10<00:00, 33.6MB Downloading data: 100%\|█\| 342M/342M [00:07<00:00, 43.1MB ...(38 data shards) ``` --- Acknowledgment:\ 数据是基于[CLUE中文预训练语料集](https://github.com/CLUEbenchmark/CLUE)进行处理、过滤得到的。\ This dataset is processed/filtered from the [CLUE pre-training corpus](https://github.com/CLUEbenchmark/CLUE).
pseeej	null	null	null	false	null	false	pseeej/animal-crossing-data	2022-11-02T03:31:55.000Z	null	false	0701ea3fa42db65b7237cab8e916a35659c5b845	[]	[]	https://huggingface.co/datasets/pseeej/animal-crossing-data/resolve/main/README.md	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 7209776.0 num_examples: 389 download_size: 7181848 dataset_size: 7209776.0 --- # Dataset Card for "animal-crossing-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gary109	null	null	null	false	3	false	gary109/onset-drums_corpora_parliament_processed	2022-11-07T09:06:30.000Z	null	false	b56c72916faa2075b017047087a8285da099683d	[]	[]	https://huggingface.co/datasets/gary109/onset-drums_corpora_parliament_processed/resolve/main/README.md	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 292227 num_examples: 1068 download_size: 87028 dataset_size: 292227 --- # Dataset Card for "onset-drums_corpora_parliament_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dhmeltzer	null	null	null	false	4	false	dhmeltzer/goodreads_test	2022-11-02T04:14:57.000Z	null	false	0179bb2c085b52b01ca23991c7581c136b76e0e6	[]	[]	https://huggingface.co/datasets/dhmeltzer/goodreads_test/resolve/main/README.md	--- dataset_info: features: - name: review_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1010427121 num_examples: 478033 download_size: 496736771 dataset_size: 1010427121 --- # Dataset Card for "goodreads_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dhmeltzer	null	null	null	false	3	false	dhmeltzer/goodreads_train	2022-11-02T04:16:00.000Z	null	false	dfefc099c175c50fa26da17038a2970fc6808171	[]	[]	https://huggingface.co/datasets/dhmeltzer/goodreads_train/resolve/main/README.md	--- dataset_info: features: - name: rating dtype: int64 - name: review_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1893978314 num_examples: 900000 download_size: 928071460 dataset_size: 1893978314 --- # Dataset Card for "goodreads_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Harmony22	null	null	null	false	null	false	Harmony22/The-stonks	2022-11-02T07:38:24.000Z	null	false	f03ddd3203868f65e565b39d1af1cf5e1df228f8	[]	[ "license:cc-by-nc-nd-4.0" ]	https://huggingface.co/datasets/Harmony22/The-stonks/resolve/main/README.md	--- license: cc-by-nc-nd-4.0 ---
annabelng	null	null	null	false	null	false	annabelng/nymemes	2022-11-02T08:02:09.000Z	null	false	6fd649a5748873d108c8a785a38a55ddca291260	[]	[]	https://huggingface.co/datasets/annabelng/nymemes/resolve/main/README.md	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 3760740114.362 num_examples: 32933 download_size: 4007130292 dataset_size: 3760740114.362 --- # Dataset Card for "nymemes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lewtun	null	null	null	false	60	false	lewtun/music_genres	2022-11-02T10:27:30.000Z	null	false	1fafac00f14590feb94984ee7dc1adc861179fc7	[]	[]	https://huggingface.co/datasets/lewtun/music_genres/resolve/main/README.md	--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: int64 - name: genre_id dtype: int64 - name: genre dtype: string splits: - name: test num_bytes: 1978321742.996 num_examples: 5076 - name: train num_bytes: 7844298868.902 num_examples: 19909 download_size: 9793244255 dataset_size: 9822620611.898 --- # Dataset Card for "music_genres" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KETI-AIR	null	There is no citation information	# 행정 문서 대상 기계독해 데이터 ## 소개 행정문서를 활용하여 기계독해 모델 생성을 위한 지문-질문-답변으로 구성된 인공지능 학습 데이터 ## 구축목적 기계독해 모델 개발, 질의응답 서비스 구축 등에 활용 가능한 대규모 인공지능 학습용 데이터셋으로서 비정형 텍스트인 행정문서를 이용하여 표와 일반 텍스트 데이터에 대한 다양한 형식의 질의응답 구축 ## Usage ```python from datasets import load_dataset raw_datasets = load_dataset( "aihub_admin_docs_mrc.py", cache_dir="huggingface_datasets", data_dir="data", ignore_verifications=True, ) dataset_train = raw_datasets["train"] for item in dataset_train: print(item) exit() ``` ## 데이터 관련 문의처 \| 담당자명 \| 전화번호 \| 이메일 \| \| ------------- \| ------------- \| ------------- \| \| 김민경 \| 02-6952-9201 \| mkgenie@42maru.ai \| ## Copyright ### 데이터 소개 AI 허브에서 제공되는 인공지능 학습용 데이터(이하 ‘AI데이터’라고 함)는 과학기술정보통신부와 한국지능정보사회진흥원의 「지능정보산업 인프라 조성」 사업의 일환으로 구축되었으며, 본 사업의 유‧무형적 결과물인 데이터, AI 응용모델 및 데이터 저작도구의 소스, 각종 매뉴얼 등(이하 ‘AI데이터 등’)에 대한 일체의 권리는 AI데이터 등의 구축 수행기관 및 참여기관(이하 ‘수행기관 등’)과 한국지능정보사회진흥원에 있습니다. 본 AI데이터 등은 인공지능 기술 및 제품·서비스 발전을 위하여 구축하였으며, 지능형 제품・서비스, 챗봇 등 다양한 분야에서 영리적・비영리적 연구・개발 목적으로 활용할 수 있습니다. ### 데이터 이용정책 - 본 AI데이터 등을 이용하기 위해서 다음 사항에 동의하며 준수해야 함을 고지합니다. 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다. 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다. 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다. 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다. 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다. 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다. 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다. 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다. ### 데이터 다운로드 신청방법 1. AI 허브를 통해 제공 중인 AI데이터 등을 다운로드 받기 위해서는 별도의 신청자 본인 확인과 정보 제공, 목적을 밝히는 절차가 필요합니다. 2. AI데이터를 제외한 데이터 설명, 저작 도구 등은 별도의 신청 절차나 로그인 없이 이용이 가능합니다. 3. 한국지능정보사회진흥원이 권리자가 아닌 AI데이터 등은 해당 기관의 이용정책과 다운로드 절차를 따라야 하며 이는 AI 허브와 관련이 없음을 알려 드립니다.	false	230	false	KETI-AIR/aihub_admin_docs_mrc	2022-11-02T10:19:12.000Z	null	false	e92eef786328238456e467d116c53d7d914c1e0e	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/KETI-AIR/aihub_admin_docs_mrc/resolve/main/README.md	--- license: apache-2.0 ---
lmvasque	null	null	null	false	null	false	lmvasque/caes	2022-11-11T18:09:24.000Z	null	false	6fd41bb2494326e92dd46a92a1aeff50fbce4fdd	[]	[ "license:cc-by-4.0" ]	https://huggingface.co/datasets/lmvasque/caes/resolve/main/README.md	--- license: cc-by-4.0 --- ## About this dataset The [CAES](http://galvan.usc.es/caes/) [(Parodi, 2015)](https://www.tandfonline.com/doi/full/10.1080/23247797.2015.1084685?cookieSet=1) dataset, also referred as the “Corpus de Aprendices del Español” (CAES), is a collection of texts created by Spanish L2 learners from Spanish learning centres and universities. These students had different learning levels, different backgrounds (11 native languages) and various levels of experience with the language. We used web scraping techniques to download a portion of the full dataset since its current website only provides content filtered by categories that have to be manually selected. The readability level of each text in CAES follows the [Common European Framework of Reference for Languages (CEFR)](https://www.coe.int/en/web/common-european-framework-reference-languages). The [raw version](https://huggingface.co/datasets/lmvasque/caes/blob/main/caes.raw.csv) of this corpus also contains information about the learners and the type of assignments with which they were assigned to create each text. We have downloaded this dataset from its original [website](https://galvan.usc.es/caes/search) to make it available to the community. If you use this data, please credit the original author and our work as well (see citations below). ## About the splits We have uploaded two versions of the CAES corpus: - caes.raw.csv: raw data from the website with no further filtering. It includes information about the learners and the type/topic of their assignments. - caes.jsonl: this data is limited to the text samples, the original levels of readability and our standardised category according to these: simple/complex and basic/intermediate/advanced. You can check for more details about these splits in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link). ## Citation If you use our splits in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)" ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ``` We have extracted the CAES corpus from their [website](https://galvan.usc.es/caes/search). If you use this corpus, please also cite their work as follows: ``` @article{Parodi2015, author = "Giovanni Parodi", title = "Corpus de aprendices de español (CAES)", journal = "Journal of Spanish Language Teaching", volume = "2", number = "2", pages = "194-200", year = "2015", publisher = "Routledge", doi = "10.1080/23247797.2015.1084685", URL = "https://doi.org/10.1080/23247797.2015.1084685", eprint = "https://doi.org/10.1080/23247797.2015.1084685" } ``` You can also find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
lmvasque	null	null	null	false	null	false	lmvasque/coh-metrix-esp	2022-11-11T17:44:04.000Z	null	false	189a95069a1544141fd9c21f638b979b106460f1	[]	[ "license:cc-by-sa-4.0" ]	https://huggingface.co/datasets/lmvasque/coh-metrix-esp/resolve/main/README.md	--- license: cc-by-sa-4.0 --- ## About this dataset The dataset Coh-Metrix-Esp (Cuentos) [(Quispesaravia et al., 2016)](https://aclanthology.org/L16-1745/) is a collection of 100 documents consisting of 50 children fables (“simple” texts) and 50 stories for adults (“complex” texts) scrapped from the web. If you use this data, please credit the original website and our work as well (see citations below). ## Citation If you use our splits in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)". ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ``` #### Coh-Metrix-Esp (Cuentos) ``` @inproceedings{quispesaravia-etal-2016-coh, title = "{C}oh-{M}etrix-{E}sp: A Complexity Analysis Tool for Documents Written in {S}panish", author = "Quispesaravia, Andre and Perez, Walter and Sobrevilla Cabezudo, Marco and Alva-Manchego, Fernando", booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)", month = may, year = "2016", address = "Portoro{\v{z}}, Slovenia", publisher = "European Language Resources Association (ELRA)", url = "https://aclanthology.org/L16-1745", pages = "4694--4698", } ``` You can also find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
lmvasque	null	null	null	false	null	false	lmvasque/hablacultura	2022-11-11T17:42:13.000Z	null	false	9a9ece7cc079929fb0902994f71e5c63f4284e11	[]	[ "license:cc-by-4.0" ]	https://huggingface.co/datasets/lmvasque/hablacultura/resolve/main/README.md	--- license: cc-by-4.0 --- ## About this dataset This dataset was collected from [HablaCultura.com](https://hablacultura.com/) a website with resources for Spanish students, labeled by instructors following the [Common European Framework of Reference for Languages (CEFR)](https://www.coe.int/en/web/common-european-framework-reference-languages). We have scraped the freely available articles from its original [website](https://hablacultura.com/) to make it available to the community. If you use this data, please credit the original [website](https://hablacultura.com/) and our work as well. ## Citation If you use our splits in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)". ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ``` You can also find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
lmvasque	null	null	null	false	null	false	lmvasque/kwiziq	2022-11-11T17:40:47.000Z	null	false	b8ec1babb569f217a0248fb05f8323539bf90d96	[]	[ "license:cc-by-4.0" ]	https://huggingface.co/datasets/lmvasque/kwiziq/resolve/main/README.md	--- license: cc-by-4.0 --- ## About this dataset This dataset was collected from [kwiziq.com](https://www.kwiziq.com/), a website dedicated to aid Spanish learning through automated methods. It also provides articles in different CEFR-based levels. We have scraped the freely available articles from its original [website](https://www.kwiziq.com/) to make it available to the community. If you use this data, please credit the original [website]((https://www.kwiziq.com/) and our work as well. ## Citation If you use our splits in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)". ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ``` You can also find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
bertcryss	null	null	null	false	4	false	bertcryss/my_dataset	2022-11-02T10:51:49.000Z	null	false	6d30a49b0ec390c1d2df97104bf7a0ae3c772434	[]	[]	https://huggingface.co/datasets/bertcryss/my_dataset/resolve/main/README.md	--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 89233.0 num_examples: 1 download_size: 84560 dataset_size: 89233.0 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Julie1901	null	null	null	false	null	false	Julie1901/pictures	2022-11-02T11:10:16.000Z	null	false	7b732531620accba4bbedd431b7f8a6100be6d41	[]	[]	https://huggingface.co/datasets/Julie1901/pictures/resolve/main/README.md
loubnabnl	null	null	null	false	6	false	loubnabnl/pii_labeling_dataset	2022-11-02T12:40:15.000Z	null	false	acc74af5096f2f78f2714cc780dafe46c40e9cb7	[]	[]	https://huggingface.co/datasets/loubnabnl/pii_labeling_dataset/resolve/main/README.md	--- dataset_info: features: - name: content dtype: string - name: licenses sequence: string - name: repository_name dtype: string - name: path dtype: string - name: size dtype: int64 - name: lang dtype: string splits: - name: train num_bytes: 8303808.681818182 num_examples: 1000 download_size: 3542729 dataset_size: 8303808.681818182 --- # Dataset Card for "pii_labeling_dataset" Dataset for PII annotation with 1000 random files from 11 different programming languages in [the-stack-smol](https://huggingface.co/datasets/bigcode/the-stack-smol). Below is the number of samples in each langauge. ```python {"python": 200, "c++": 200, "javascript": 100, "java": 100, "typescript": 100, "php": 100, "c": 40, "c-sharp": 40, "markdown": 40, "go":40, "ruby": 40} ```
lewtun	null	null	null	false	1	false	lewtun/audio-test-push	2022-11-02T11:36:48.000Z	null	false	9361d38c024c137755d8cefe9be826dc16be4885	[]	[]	https://huggingface.co/datasets/lewtun/audio-test-push/resolve/main/README.md	--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: int64 - name: genre_id dtype: int64 - name: genre dtype: string splits: - name: test num_bytes: 3994705.0 num_examples: 10 - name: train num_bytes: 3738678.0 num_examples: 10 download_size: 7730848 dataset_size: 7733383.0 --- # Dataset Card for "audio-test-push" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ghomasHudson	null	null	null	false	1	false	ghomasHudson/muld_OpenSubtitles	2022-11-02T11:56:13.000Z	null	false	a5e76a325594cc02dfb1cba47f07c497ab01bf60	[]	[]	https://huggingface.co/datasets/ghomasHudson/muld_OpenSubtitles/resolve/main/README.md	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: metadata dtype: string splits: - name: test num_bytes: 176793874 num_examples: 1385 - name: train num_bytes: 1389584660 num_examples: 27749 download_size: 967763941 dataset_size: 1566378534 --- # Dataset Card for "muld_OpenSubtitles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ghomasHudson	null	null	null	false	null	false	ghomasHudson/muld_AO3_Style_Change_Detection	2022-11-02T12:06:59.000Z	null	false	282a412b73478e5e843367c5ece3d3f8660f05b0	[]	[]	https://huggingface.co/datasets/ghomasHudson/muld_AO3_Style_Change_Detection/resolve/main/README.md	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: metadata dtype: string splits: - name: test num_bytes: 282915635 num_examples: 2352 - name: train num_bytes: 762370660 num_examples: 6354 - name: validation num_bytes: 83699681 num_examples: 705 download_size: 677671983 dataset_size: 1128985976 --- # Dataset Card for "muld_AO3_Style_Change_Detection" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
frankier	null	null	null	false	27	false	frankier/processed_multiscale_rt_critics	2022-11-07T07:45:06.000Z	null	false	7da6c071ef1567fe3af348832bbd12f379160ad0	[]	[]	https://huggingface.co/datasets/frankier/processed_multiscale_rt_critics/resolve/main/README.md	--- dataset_info: features: - name: movie_title dtype: string - name: publisher_name dtype: string - name: critic_name dtype: string - name: review_content dtype: string - name: review_score dtype: string - name: grade_type dtype: string - name: orig_num dtype: float32 - name: orig_denom dtype: float32 - name: label dtype: uint8 - name: scale_points dtype: uint8 - name: multiplier dtype: uint8 - name: group_id dtype: uint32 splits: - name: test num_bytes: 32106586 num_examples: 148289 - name: train num_bytes: 131588808 num_examples: 607259 download_size: 74091855 dataset_size: 163695394 --- # Dataset Card for "processed_multiscale_rt_critics" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ghomasHudson	null	null	null	false	null	false	ghomasHudson/muld_NarrativeQA	2022-11-02T12:24:41.000Z	null	false	63b6d26bb53a87c2b8ea9c9428bee6ab7a7532ef	[]	[]	https://huggingface.co/datasets/ghomasHudson/muld_NarrativeQA/resolve/main/README.md	--- dataset_info: features: - name: input dtype: string - name: output sequence: string splits: - name: test num_bytes: 3435452065 num_examples: 10143 - name: train num_bytes: 11253796383 num_examples: 32747 - name: validation num_bytes: 1176625993 num_examples: 3373 download_size: 8819172017 dataset_size: 15865874441 --- # Dataset Card for "muld_NarrativeQA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GEM	null	@misc{gehrmann2022TaTA, Author = {Sebastian Gehrmann and Sebastian Ruder and Vitaly Nikolaev and Jan A. Botha and Michael Chavinda and Ankur Parikh and Clara Rivera}, Title = {TaTa: A Multilingual Table-to-Text Dataset for African Languages}, Year = {2022}, Eprint = {arXiv:2211.00142}, }	Dataset loader for TaTA: A Multilingual Table-to-Text Dataset for African Languages	false	null	false	GEM/TaTA	2022-11-03T14:23:59.000Z	null	false	8df0b33afd830cd72656e23c6b1cedec2b285b37	[]	[ "arxiv:2211.00142", "arxiv:2112.12870", "annotations_creators:none", "language_creators:unknown", "language:ar", "language:en", "language:fr", "language:ha", "language:ig", "language:pt", "language:ru", "language:sw", "language:yo", "multilinguality:yes", "size_categories:unknown", "source_datasets:original", "task_categories:table-to-text", "tags:data-to-text", "license:cc-by-sa-4.0" ]	https://huggingface.co/datasets/GEM/TaTA/resolve/main/README.md	--- annotations_creators: - none language_creators: - unknown language: - ar - en - fr - ha - ig - pt - ru - sw - yo multilinguality: - yes size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: TaTA tags: - data-to-text license: cc-by-sa-4.0 dataset_info: features: - name: gem_id dtype: string - name: example_id dtype: string - name: title dtype: string - name: unit_of_measure dtype: string - name: chart_type dtype: string - name: was_translated dtype: string - name: table_data dtype: string - name: linearized_input dtype: string - name: table_text sequence: string - name: target dtype: string splits: - name: ru num_bytes: 308435 num_examples: 210 - name: test num_bytes: 1691383 num_examples: 763 - name: train num_bytes: 10019272 num_examples: 6962 - name: validation num_bytes: 1598442 num_examples: 754 download_size: 18543506 dataset_size: 13617532 --- # Dataset Card for GEM/TaTA ## Dataset Description - Homepage: https://github.com/google-research/url-nlp - Repository: https://github.com/google-research/url-nlp - Paper: https://arxiv.org/abs/2211.00142 - Leaderboard: https://github.com/google-research/url-nlp - Point of Contact: Sebastian Ruder ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/TaTA). ### Dataset Summary Existing data-to-text generation datasets are mostly limited to English. Table-to-Text in African languages (TaTA) addresses this lack of data as the first large multilingual table-to-text dataset with a focus on African languages. TaTA was created by transcribing figures and accompanying text in bilingual reports by the Demographic and Health Surveys Program, followed by professional translation to make the dataset fully parallel. TaTA includes 8,700 examples in nine languages including four African languages (Hausa, Igbo, Swahili, and Yorùbá) and a zero-shot test language (Russian). You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/TaTA') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/TaTA). #### website [Github](https://github.com/google-research/url-nlp) #### paper [ArXiv](https://arxiv.org/abs/2211.00142) #### authors Sebastian Gehrmann, Sebastian Ruder , Vitaly Nikolaev, Jan A. Botha, Michael Chavinda, Ankur Parikh, Clara Rivera ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Github](https://github.com/google-research/url-nlp) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Github](https://github.com/google-research/url-nlp) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ArXiv](https://arxiv.org/abs/2211.00142) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @misc{gehrmann2022TaTA, Author = {Sebastian Gehrmann and Sebastian Ruder and Vitaly Nikolaev and Jan A. Botha and Michael Chavinda and Ankur Parikh and Clara Rivera}, Title = {TaTa: A Multilingual Table-to-Text Dataset for African Languages}, Year = {2022}, Eprint = {arXiv:2211.00142}, } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Sebastian Ruder #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> ruder@google.com #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Github](https://github.com/google-research/url-nlp) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> The paper introduces a metric StATA which is trained on human ratings and which is used to rank approaches submitted to the leaderboard. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> yes #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English`, `Portuguese`, `Arabic`, `French`, `Hausa`, `Swahili (macrolanguage)`, `Igbo`, `Yoruba`, `Russian` #### Whose Language? <!-- info: Whose language is in the dataset? --> <!-- scope: periscope --> The language is taken from reports by the demographic and health surveys program. #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The dataset poses significant reasoning challenges and is thus meant as a way to asses the verbalization and reasoning capabilities of structure-to-text models. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Summarize key information from a table in a single sentence. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `industry` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Google Research #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Sebastian Gehrmann, Sebastian Ruder , Vitaly Nikolaev, Jan A. Botha, Michael Chavinda, Ankur Parikh, Clara Rivera #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> Google Research #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Sebastian Gehrmann (Google Research) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `example_id`: The ID of the example. Each ID (e.g., `AB20-ar-1`) consists of three parts: the document ID, the language ISO 639-1 code, and the index of the table within the document. - `title`: The title of the table. - `unit_of_measure`: A description of the numerical value of the data. E.g., percentage of households with clean water. - `chart_type`: The kind of chart associated with the data. We consider the following (normalized) types: horizontal bar chart, map chart, pie graph, tables, line chart, pie chart, vertical chart type, line graph, vertical bar chart, and other. - `was_translated`: Whether the table was transcribed in the original language of the report or translated. - `table_data`: The table content is a JSON-encoded string of a two-dimensional list, organized by row, from left to right, starting from the top of the table. Number of items varies per table. Empty cells are given as empty string values in the corresponding table cell. - `table_text`: The sentences forming the description of each table are encoded as a JSON object. In the case of more than one sentence, these are separated by commas. Number of items varies per table. - `linearized_input`: A single string that contains the table content separated by vertical bars, i.e., \|. Including title, unit of measurement, and the content of each cell including row and column headers in between brackets, i.e., (Medium Empowerment, Mali, 17.9). #### Reason for Structure <!-- info: How was the dataset structure determined? --> <!-- scope: microscope --> The structure includes all available information for the infographics on which the dataset is based. #### How were labels chosen? <!-- info: How were the labels chosen? --> <!-- scope: microscope --> Annotators looked through English text to identify sentences that describe an infographic. They then identified the corresponding location of the parallel non-English document. All sentences were extracted. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "example_id": "FR346-en-39", "title": "Trends in early childhood mortality rates", "unit_of_measure": "Deaths per 1,000 live births for the 5-year period before the survey", "chart_type": "Line chart", "was_translated": "False", "table_data": "[[\"\", \"Child mortality\", \"Neonatal mortality\", \"Infant mortality\", \"Under-5 mortality\"], [\"1990 JPFHS\", 5, 21, 34, 39], [\"1997 JPFHS\", 6, 19, 29, 34], [\"2002 JPFHS\", 5, 16, 22, 27], [\"2007 JPFHS\", 2, 14, 19, 21], [\"2009 JPFHS\", 5, 15, 23, 28], [\"2012 JPFHS\", 4, 14, 17, 21], [\"2017-18 JPFHS\", 3, 11, 17, 19]]", "table_text": [ "neonatal, infant, child, and under-5 mortality rates for the 5 years preceding each of seven JPFHS surveys (1990 to 2017-18).", "Under-5 mortality declined by half over the period, from 39 to 19 deaths per 1,000 live births.", "The decline in mortality was much greater between the 1990 and 2007 surveys than in the most recent period.", "Between 2012 and 2017-18, under-5 mortality decreased only modestly, from 21 to 19 deaths per 1,000 live births, and infant mortality remained stable at 17 deaths per 1,000 births." ], "linearized_input": "Trends in early childhood mortality rates \| Deaths per 1,000 live births for the 5-year period before the survey \| (Child mortality, 1990 JPFHS, 5) (Neonatal mortality, 1990 JPFHS, 21) (Infant mortality, 1990 JPFHS, 34) (Under-5 mortality, 1990 JPFHS, 39) (Child mortality, 1997 JPFHS, 6) (Neonatal mortality, 1997 JPFHS, 19) (Infant mortality, 1997 JPFHS, 29) (Under-5 mortality, 1997 JPFHS, 34) (Child mortality, 2002 JPFHS, 5) (Neonatal mortality, 2002 JPFHS, 16) (Infant mortality, 2002 JPFHS, 22) (Under-5 mortality, 2002 JPFHS, 27) (Child mortality, 2007 JPFHS, 2) (Neonatal mortality, 2007 JPFHS, 14) (Infant mortality, 2007 JPFHS, 19) (Under-5 mortality, 2007 JPFHS, 21) (Child mortality, 2009 JPFHS, 5) (Neonatal mortality, 2009 JPFHS, 15) (Infant mortality, 2009 JPFHS, 23) (Under-5 mortality, 2009 JPFHS, 28) (Child mortality, 2012 JPFHS, 4) (Neonatal mortality, 2012 JPFHS, 14) (Infant mortality, 2012 JPFHS, 17) (Under-5 mortality, 2012 JPFHS, 21) (Child mortality, 2017-18 JPFHS, 3) (Neonatal mortality, 2017-18 JPFHS, 11) (Infant mortality, 2017-18 JPFHS, 17) (Under-5 mortality, 2017-18 JPFHS, 19)" } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> - `Train`: Training set, includes examples with 0 or more references. - `Validation`: Validation set, includes examples with 3 or more references. - `Test`: Test set, includes examples with 3 or more references. - `Ru`: Russian zero-shot set. Includes English and Russian examples (Russian is not includes in any of the other splits). #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The same table across languages is always in the same split, i.e., if table X is in the test split in language A, it will also be in the test split in language B. In addition to filtering examples without transcribed table values, every example of the development and test splits has at least 3 references. From the examples that fulfilled these criteria, 100 tables were sampled for both development and test for a total of 800 examples each. A manual review process excluded a few tables in each set, resulting in a training set of 6,962 tables, a development set of 752 tables, and a test set of 763 tables. #### <!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? --> <!-- scope: microscope --> There are tables without references, without values, and others that are very large. The dataset is distributed as-is, but the paper describes multiple strategies to deal with data issues. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> There is no other multilingual data-to-text dataset that is parallel over languages. Moreover, over 70% of references in the dataset require reasoning and it is thus of very high quality and challenging for models. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> yes #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> More languages, parallel across languages, grounded in infographics, not centered on Western entities or source documents #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> reasoning, verbalization, content planning ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> no #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> The background section of the [paper](https://arxiv.org/abs/2211.00142) provides a list of related datasets. #### Technical Terms <!-- info: Technical terms used in this card and the dataset and their definitions --> <!-- scope: microscope --> - `data-to-text`: Term that refers to NLP tasks in which the input is structured information and the output is natural language. ## Previous Results ### Previous Results #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> `StATA`: A new metric associated with TaTA that is trained on human judgments and which has a much higher correlation with them. #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> The creators used a human evaluation that measured [attribution](https://arxiv.org/abs/2112.12870) and reasoning capabilities of various models. Based on these ratings, they trained a new metric and showed that existing metrics fail to measure attribution. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> no ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The curation rationale is to create a multilingual data-to-text dataset that is high-quality and challenging. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The communicative goal is to describe a table in a single sentence. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Single website` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The language was produced by USAID as part of the Demographic and Health Surveys program (https://dhsprogram.com/). #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The topics are related to fertility, family planning, maternal and child health, gender, and nutrition. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by crowdworker #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> expert created #### Number of Raters <!-- info: What is the number of raters --> <!-- scope: telescope --> 11<n<50 #### Rater Qualifications <!-- info: Describe the qualifications required of an annotator. --> <!-- scope: periscope --> Professional annotator who is a fluent speaker of the respective language #### Raters per Training Example <!-- info: How many annotators saw each training example? --> <!-- scope: periscope --> 0 #### Raters per Test Example <!-- info: How many annotators saw each test example? --> <!-- scope: periscope --> 1 #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> yes #### Which Annotation Service <!-- info: Which annotation services were used? --> <!-- scope: periscope --> `other` #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> The additional annotations are for system outputs and references and serve to develop metrics for this task. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> validated by data curators #### Quality Control Details <!-- info: Describe the quality control measures that were taken. --> <!-- scope: microscope --> Ratings were compared to a small (English) expert-curated set of ratings to ensure high agreement. There were additional rounds of training and feedback to annotators to ensure high quality judgments. ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Other Consented Downstream Use <!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? --> <!-- scope: microscope --> In addition to data-to-text generation, the dataset can be used for translation or multimodal research. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> The DHS program only publishes aggregate survey information and thus, no personal information is included. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> yes #### Details on how Dataset Addresses the Needs <!-- info: Describe how this dataset addresses the needs of underserved communities. --> <!-- scope: microscope --> The dataset is focusing on data about African countries and the languages included in the dataset are spoken in Africa. It aims to improve the representation of African languages in the NLP and NLG communities. ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> The language producers for this dataset are those employed by the DHS program which is a US-funded program. While the data is focused on African countries, there may be implicit western biases in how the data is presented. ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> While tables were transcribed in the available languages, the majority of the tables were published in English as the first language. Professional translators were used to translate the data, which makes it plausible that some translationese exists in the data. Moreover, it was unavoidable to collect reference sentences that are only partially entailed by the source tables. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> The domain of health reports includes potentially sensitive topics relating to reproduction, violence, sickness, and death. Perceived negative values could be used to amplify stereotypes about people from the respective regions or countries. The intended academic use of this dataset is to develop and evaluate models that neutrally report the content of these tables but not use the outputs to make value judgments, and these applications are thus discouraged.
alfredodeza	null	null	null	false	2	false	alfredodeza/world-junior-championships-results	2022-11-02T15:41:33.000Z	null	false	dfbc45e3c26ef1a03ef6e9e8c5e3d3da3ffc50f9	[]	[ "license:mit" ]	https://huggingface.co/datasets/alfredodeza/world-junior-championships-results/resolve/main/README.md	--- license: mit ---
ficsort	null	""" _FEATURES = Features( { "id": Value("int32"), "tokens": Sequence(Value("string")), "ner": Sequence( ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ] ) ), "document_id": Value("int32"), "sentence_id": Value("int32") } ) class SzegedNERConfig(BuilderConfig):	The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc.	false	9	false	ficsort/SzegedNER	2022-11-02T15:56:22.000Z	null	false	048280e285175987c092a96b6149c032fcecc0c7	[]	[ "annotations_creators:expert-generated", "language:hu", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "tags:hungarian", "tags:szeged", "tags:ner", "task_categories:token-classification", "task_ids:named-entity-recognition" ]	https://huggingface.co/datasets/ficsort/SzegedNER/resolve/main/README.md	--- annotations_creators: - expert-generated language: - hu language_creators: - other license: [] multilinguality: - monolingual paperswithcode_id: null pretty_name: SzegedNER size_categories: - 1K<n<10K source_datasets: - original tags: - hungarian - szeged - ner task_categories: - token-classification task_ids: - named-entity-recognition --- # Introduction The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc. ## Corpus of Business Newswire Texts (business) The Named Entity Corpus for Hungarian is a subcorpus of the Szeged Treebank, which contains full syntactic annotations done manually by linguist experts. A significant part of these texts has been annotated with Named Entity class labels in line with the annotation standards used on the CoNLL-2003 shared task. Statistical data on Named Entities occurring in the corpus: ``` \| tokens \| phrases ------ \| ------ \| ------- non NE \| 200067 \| PER \| 1921 \| 982 ORG \| 20433 \| 10533 LOC \| 1501 \| 1294 MISC \| 2041 \| 1662 ``` ### Reference > György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik: Highly accurate Named Entity corpus for Hungarian. International Conference on Language Resources and Evaluation 2006, Genova (Italy) ## Criminal NE corpus (criminal) The Hungarian National Corpus and its Heti Világgazdaság (HVG) subcorpus provided the basis for corpus text selection: articles related to the topic of financially liable offences were selected and annotated for the categories person, organization, location and miscellaneous. There are two annotated versions of the corpus. When preparing the tag-for-meaning annotation, our linguists took into consideration the context in which the Named Entity under investigation occurred, thus, it was not the primary sense of the Named Entity that determined the tag (e.g. Manchester=LOC) but its contextual reference (e.g. Manchester won the Premier League=ORG). As for tag-for-tag annotation, these cases were not differentiated: tags were always given on the basis of the primary sense. Statistical data on Named Entities occurring in the corpus: ``` \| tag-for-meaning \| tag-for-tag ------ \| --------------- \| ----------- non NE \| 200067 \| PER \| 8101 \| 8121 ORG \| 8782 \| 9480 LOC \| 5049 \| 5391 MISC \| 1917 \| 854 ``` ## Metadata dataset_info: - config_name: business features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: 0: O 1: B-PER 2: I-PER 3: B-ORG 4: I-ORG 5: B-LOC 6: I-LOC 7: B-MISC 8: I-MISC - name: document_id dtype: string - name: sentence_id dtype: string splits: - name: original num_bytes: 4452207 num_examples: 9573 - name: test num_bytes: 856798 num_examples: 1915 - name: train num_bytes: 3171931 num_examples: 6701 - name: validation num_bytes: 423478 num_examples: 957 download_size: 0 dataset_size: 8904414 - config_name: criminal features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: 0: O 1: B-PER 2: I-PER 3: B-ORG 4: I-ORG 5: B-LOC 6: I-LOC 7: B-MISC 8: I-MISC - name: document_id dtype: string - name: sentence_id dtype: string splits: - name: original num_bytes: 2807970 num_examples: 5375 - name: test num_bytes: 520959 num_examples: 1089 - name: train num_bytes: 1989662 num_examples: 3760 - name: validation num_bytes: 297349 num_examples: 526 download_size: 0 dataset_size: 5615940
LiveEvil	null	null	null	false	1	false	LiveEvil/autotrain-data-testtextexists	2022-11-03T15:55:01.000Z	null	false	64335ac3f9bfae6f6e2b467c6c904820ede01999	[]	[ "language:en" ]	https://huggingface.co/datasets/LiveEvil/autotrain-data-testtextexists/resolve/main/README.md	--- language: - en task_categories: - text-scoring --- # AutoTrain Dataset for project: testtextexists ## Dataset Description This dataset has been automatically processed by AutoTrain for project testtextexists. ### 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": "According to the National Soft Drink Association, the annual consumption of soda by the U.S. citizens is 600 cans", "target": 66.0 }, { "text": "Experts say new vaccines are fake!", "target": 50.0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='float32', 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 \| 19 \| \| valid \| 18 \|
Meiruofeng	null	null	null	false	38	false	Meiruofeng/test	2022-11-05T03:28:10.000Z	null	false	c466f287741cdebbe8a01c14f11b0b3a10ba3b36	[]	[]	https://huggingface.co/datasets/Meiruofeng/test/resolve/main/README.md
allenai	null	@inproceedings{Cohan2019EMNLP, title={Pretrained Language Models for Sequential Sentence Classification}, author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld}, year={2019}, booktitle={EMNLP}, }	As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.	false	16	false	allenai/csabstruct	2022-11-02T17:54:38.000Z	null	false	82e266d8effde67520d50532587b5f000237b50a	[]	[ "arxiv:1909.04054", "license:apache-2.0" ]	https://huggingface.co/datasets/allenai/csabstruct/resolve/main/README.md	--- license: apache-2.0 --- # CSAbstruct CSAbstruct was created as part of "Pretrained Language Models for Sequential Sentence Classification" ([ACL Anthology][2], [arXiv][1], [GitHub][6]). It contains 2,189 manually annotated computer science abstracts with sentences annotated according to their rhetorical roles in the abstract, similar to the [PUBMED-RCT][3] categories. ## Dataset Construction Details CSAbstruct is a new dataset of annotated computer science abstracts with sentence labels according to their rhetorical roles. The key difference between this dataset and [PUBMED-RCT][3] is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form. Therefore, there is more variety in writing styles in CSAbstruct. CSAbstruct is collected from the Semantic Scholar corpus [(Ammar et a3., 2018)][4]. E4ch sentence is annotated by 5 workers on the [Figure-eight platform][5], with one of 5 categories `{BACKGROUND, OBJECTIVE, METHOD, RESULT, OTHER}`. We use 8 abstracts (with 51 sentences) as test questions to train crowdworkers. Annotators whose accuracy is less than 75% are disqualified from doing the actual annotation job. The annotations are aggregated using the agreement on a single sentence weighted by the accuracy of the annotator on the initial test questions. A confidence score is associated with each instance based on the annotator initial accuracy and agreement of all annotators on that instance. We then split the dataset 75%/15%/10% into train/dev/test partitions, such that the test set has the highest confidence scores. Agreement rate on a random subset of 200 sentences is 75%, which is quite high given the difficulty of the task. Compared with [PUBMED-RCT][3], our dataset exhibits a wider variety of writ- ing styles, since its abstracts are not written with an explicit structural template. ## Dataset Statistics \| Statistic \| Avg ± std \| \|--------------------------\|-------------\| \| Doc length in sentences \| 6.7 ± 1.99 \| \| Sentence length in words \| 21.8 ± 10.0 \| \| Label \| % in Dataset \| \|---------------\|--------------\| \| `BACKGROUND` \| 33% \| \| `METHOD` \| 32% \| \| `RESULT` \| 21% \| \| `OBJECTIVE` \| 12% \| \| `OTHER` \| 03% \| ## Citation If you use this dataset, please cite the following paper: ``` @inproceedings{Cohan2019EMNLP, title={Pretrained Language Models for Sequential Sentence Classification}, author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld}, year={2019}, booktitle={EMNLP}, } ``` [1]: https://arxiv.org/abs/1909.04054 [2]: https://aclanthology.org/D19-1383 [3]: https://github.com/Franck-Dernoncourt/pubmed-rct [4]: https://aclanthology.org/N18-3011/ [5]: https://www.figure-eight.com/ [6]: https://github.com/allenai/sequential_sentence_classification
shunk031	null	@INPROCEEDINGS{caesar2018cvpr, title={COCO-Stuff: Thing and stuff classes in context}, author={Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio}, booktitle={Computer vision and pattern recognition (CVPR), 2018 IEEE conference on}, organization={IEEE}, year={2018} }	COCO-Stuff augments all 164K images of the popular COCO dataset with pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning.	false	1	false	shunk031/cocostuff	2022-11-04T01:53:17.000Z	null	false	325d994d8b815216cdbafab67e5de47e62fc3931	[]	[ "arxiv:1612.03716", "language:en", "license:cc-by-4.0", "tags:computer-vision", "tags:object-detection", "tags:ms-coco", "datasets:stuff-thing", "datasets:stuff-only", "metrics:accuracy", "metrics:iou" ]	https://huggingface.co/datasets/shunk031/cocostuff/resolve/main/README.md	--- language: - en license: cc-by-4.0 tags: - computer-vision - object-detection - ms-coco datasets: - stuff-thing - stuff-only metrics: - accuracy - iou --- # Dataset Card for COCO-Stuff [![CI](https://github.com/shunk031/huggingface-datasets_cocostuff/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_cocostuff/actions/workflows/ci.yaml) ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/nightrome/cocostuff - Repository: https://github.com/nightrome/cocostuff - Paper (preprint): https://arxiv.org/abs/1612.03716 - Paper (CVPR2018): https://openaccess.thecvf.com/content_cvpr_2018/html/Caesar_COCO-Stuff_Thing_and_CVPR_2018_paper.html ### Dataset Summary COCO-Stuff is the largest existing dataset with dense stuff and thing annotations. From the paper: > Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things. ### Dataset Preprocessing ### Supported Tasks and Leaderboards ### Languages All of annotations use English as primary language. ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("shunk031/cocostuff", "stuff-thing") ``` #### stuff-things An example of looks as follows. ```json { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FCA033C9C40>, 'image_filename': '000000000009.jpg', 'image_id': '9', 'width': 640 'height': 480, 'objects': [ { 'object_id': '121', 'x': 0, 'y': 11, 'w': 640, 'h': 469, 'name': 'food-other' }, { 'object_id': '143', 'x': 0, 'y': 0 'w': 640, 'h': 480, 'name': 'plastic' }, { 'object_id': '165', 'x': 0, 'y': 0, 'w': 319, 'h': 118, 'name': 'table' }, { 'object_id': '183', 'x': 0, 'y': 2, 'w': 631, 'h': 472, 'name': 'unknown-183' } ], 'stuff_map': <PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FCA0222D880>, } ``` #### stuff-only An example of looks as follows. ```json { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FCA033C9C40>, 'image_filename': '000000000009.jpg', 'image_id': '9', 'width': 640 'height': 480, 'objects': [ { 'object_id': '121', 'x': 0, 'y': 11, 'w': 640, 'h': 469, 'name': 'food-other' }, { 'object_id': '143', 'x': 0, 'y': 0 'w': 640, 'h': 480, 'name': 'plastic' }, { 'object_id': '165', 'x': 0, 'y': 0, 'w': 319, 'h': 118, 'name': 'table' }, { 'object_id': '183', 'x': 0, 'y': 2, 'w': 631, 'h': 472, 'name': 'unknown-183' } ] } ``` ### Data Fields #### stuff-things - `image`: A `PIL.Image.Image` object containing the image. - `image_id`: Unique numeric ID of the image. - `image_filename`: File name of the image. - `width`: Image width. - `height`: Image height. - `stuff_map`: A `PIL.Image.Image` object containing the Stuff + thing PNG-style annotations - `objects`: Holds a list of `Object` data classes: - `object_id`: Unique numeric ID of the object. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `name`: object name #### stuff-only - `image`: A `PIL.Image.Image` object containing the image. - `image_id`: Unique numeric ID of the image. - `image_filename`: File name of the image. - `width`: Image width. - `height`: Image height. - `objects`: Holds a list of `Object` data classes: - `object_id`: Unique numeric ID of the object. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `name`: object name ### Data Splits \| name \| train \| validation \| \|-------------\|--------:\|-----------:\| \| stuff-thing \| 118,280 \| 5,000 \| \| stuff-only \| 118,280 \| 5,000 \| ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? From the paper: > COCO-Stuff contains 172 classes: 80 thing, 91 stuff, and 1 class unlabeled. The 80 thing classes are the same as in COCO [35]. The 91 stuff classes are curated by an expert annotator. The class unlabeled is used in two situations: if a label does not belong to any of the 171 predefined classes, or if the annotator cannot infer the label of a pixel. ### 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 COCO-Stuff is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply: - COCO images: [Flickr Terms of use](http://cocodataset.org/#termsofuse) - COCO annotations: [Creative Commons Attribution 4.0 License](http://cocodataset.org/#termsofuse) - COCO-Stuff annotations & code: [Creative Commons Attribution 4.0 License](http://cocodataset.org/#termsofuse) ### Citation Information ```bibtex @INPROCEEDINGS{caesar2018cvpr, title={COCO-Stuff: Thing and stuff classes in context}, author={Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio}, booktitle={Computer vision and pattern recognition (CVPR), 2018 IEEE conference on}, organization={IEEE}, year={2018} } ``` ### Contributions Thanks to [@nightrome](https://github.com/nightrome) for publishing the COCO-Stuff dataset.
Vanimal0221	null	null	null	false	null	false	Vanimal0221/VaanceFace	2022-11-02T19:17:09.000Z	null	false	6b103e4b7fd9abf2d1aa6af0a2aa5ce8536af705	[]	[ "license:artistic-2.0" ]	https://huggingface.co/datasets/Vanimal0221/VaanceFace/resolve/main/README.md	--- license: artistic-2.0 ---
Nerfgun3	null	null	null	false	null	false	Nerfgun3/sciamano	2022-11-02T21:15:27.000Z	null	false	88835bf225b88600767b73618ad4f6aa7ea4d77d	[]	[ "language:en", "tags:stable-diffusion", "tags:text-to-image", "license:creativeml-openrail-m" ]	https://huggingface.co/datasets/Nerfgun3/sciamano/resolve/main/README.md	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: false --- # Sciamano Artist Embedding / Textual Inversion ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt: ```"drawn by sciamano"``` If it is to strong just add [] around it. Trained until 14000 steps Have fun :) ## Example Pictures <table> <tr> <td><img src=https://i.imgur.com/xlHVUJ4.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/Nsqdc5Q.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/Av4NTd8.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/ctVCTiY.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/kO6IE4S.png width=100% height=100%/></td> </tr> </table> ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding 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 embedding 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/spaces/CompVis/stable-diffusion-license)
Nerfgun3	null	null	null	false	null	false	Nerfgun3/john_kafka	2022-11-02T21:25:38.000Z	null	false	768e7ebca5725cd852f4579d170a8726b061619d	[]	[ "language:en", "tags:stable-diffusion", "tags:text-to-image", "license:creativeml-openrail-m" ]	https://huggingface.co/datasets/Nerfgun3/john_kafka/resolve/main/README.md	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: false --- # John Kafka Artist Embedding / Textual Inversion ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt: ```"drawn by john_kafka"``` If it is to strong just add [] around it. Trained until 6000 steps Have fun :) ## Example Pictures <table> <tr> <td><img src=https://i.imgur.com/aCnC1zv.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/FdBuWbG.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/1rkuXkZ.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/5N9Wp7q.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/v2AkXjU.png width=100% height=100%/></td> </tr> </table> ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding 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 embedding 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/spaces/CompVis/stable-diffusion-license)
Nerfgun3	null	null	null	false	null	false	Nerfgun3/shatter_style	2022-11-02T21:30:48.000Z	null	false	f480d9dfb53d9f3a663001496e929c9184cbeeea	[]	[ "language:en", "tags:stable-diffusion", "tags:text-to-image", "license:creativeml-openrail-m" ]	https://huggingface.co/datasets/Nerfgun3/shatter_style/resolve/main/README.md	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: false --- # Shatter Style Embedding / Textual Inversion ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt: ```"drawn by shatter_style"``` If it is to strong just add [] around it. Trained until 6000 steps Have fun :) ## Example Pictures <table> <tr> <td><img src=https://i.imgur.com/ebXN3C2.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/7zUtEDQ.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/uEuKyBP.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/qRJ5o3E.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/FybZxbO.png width=100% height=100%/></td> </tr> </table> ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding 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 embedding 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/spaces/CompVis/stable-diffusion-license)
hushell	null	null	null	false	null	false	hushell/meta_dataset_h5	2022-11-02T23:46:22.000Z	null	false	fb2872529db40dea4c95368a88460eef589a5763	[]	[ "license:cc-by-nc-sa-4.0" ]	https://huggingface.co/datasets/hushell/meta_dataset_h5/resolve/main/README.md	--- license: cc-by-nc-sa-4.0 ---
connorhoehn	null	Connor Hoehn	null	false	51	false	connorhoehn/card_display_v1	2022-11-03T02:21:11.000Z	null	false	b62839591f22b070148a84e852aea9183a01778c	[]	[ "language:en" ]	https://huggingface.co/datasets/connorhoehn/card_display_v1/resolve/main/README.md	--- language: - en dataset_info: - config_name: card-detection features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects list: - name: category_id dtype: class_label: names: 0: boxed 1: grid 2: spread 3: stack - name: image_id dtype: string - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: iscrowd dtype: bool splits: - name: train download_size: 96890427 dataset_size: 0 - config_name: display-detection features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects list: - name: category_id dtype: class_label: names: 0: boxed 1: grid 2: spread 3: stack - name: image_id dtype: string - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: iscrowd dtype: bool splits: - name: train num_bytes: 42942 num_examples: 154 download_size: 96967919 dataset_size: 42942 ---
liyongsea	null	null	PTB-XL, a large publicly available electrocardiography dataset	false	21	false	liyongsea/PTB-XL	2022-11-03T15:57:19.000Z	null	false	a4d0d1862c7cb8176bcdf098ee2b11705dcb6800	[]	[ "license:other" ]	https://huggingface.co/datasets/liyongsea/PTB-XL/resolve/main/README.md	--- license: other ---
sabita9	null	null	null	false	null	false	sabita9/mauricio-macri-2	2022-11-03T03:33:23.000Z	null	false	48df4de700a2757b6122b4b3633aeb5c36120473	[]	[ "license:mit" ]	https://huggingface.co/datasets/sabita9/mauricio-macri-2/resolve/main/README.md	--- license: mit ---
juliensimon	null	null	null	false	5	false	juliensimon/food102-stockholm	2022-11-03T05:21:44.000Z	null	false	44a663ee108faca3a7b09990500bd566b3847e5d	[]	[]	https://huggingface.co/datasets/juliensimon/food102-stockholm/resolve/main/README.md	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: 0: apple_pie 1: baby_back_ribs 2: baklava 3: beef_carpaccio 4: beef_tartare 5: beet_salad 6: beignets 7: bibimbap 8: bread_pudding 9: breakfast_burrito 10: bruschetta 11: caesar_salad 12: cannoli 13: caprese_salad 14: carrot_cake 15: ceviche 16: cheese_plate 17: cheesecake 18: chicken_curry 19: chicken_quesadilla 20: chicken_wings 21: chocolate_cake 22: chocolate_mousse 23: churros 24: clam_chowder 25: club_sandwich 26: crab_cakes 27: creme_brulee 28: croque_madame 29: cup_cakes 30: deviled_eggs 31: donuts 32: dumplings 33: edamame 34: eggs_benedict 35: escargots 36: falafel 37: filet_mignon 38: fish_and_chips 39: foie_gras 40: french_fries 41: french_onion_soup 42: french_toast 43: fried_calamari 44: fried_rice 45: frozen_yogurt 46: garlic_bread 47: gnocchi 48: greek_salad 49: grilled_cheese_sandwich 50: grilled_salmon 51: guacamole 52: gyoza 53: hamburger 54: hot_and_sour_soup 55: hot_dog 56: huevos_rancheros 57: hummus 58: ice_cream 59: lasagna 60: lobster_bisque 61: lobster_roll_sandwich 62: macaroni_and_cheese 63: macarons 64: miso_soup 65: mussels 66: nachos 67: omelette 68: onion_rings 69: oysters 70: pad_thai 71: paella 72: pancakes 73: panna_cotta 74: peking_duck 75: pho 76: pizza 77: pork_chop 78: poutine 79: prime_rib 80: pulled_pork_sandwich 81: ramen 82: ravioli 83: red_velvet_cake 84: risotto 85: samosa 86: sashimi 87: scallops 88: seaweed_salad 89: shrimp_and_grits 90: spaghetti_bolognese 91: spaghetti_carbonara 92: spring_rolls 93: steak 94: strawberry_shortcake 95: sushi 96: swedish_meatballs 97: tacos 98: takoyaki 99: tiramisu 100: tuna_tartare 101: waffles splits: - name: test num_bytes: 1313331456.8001626 num_examples: 25301 - name: train num_bytes: 3855197470.2528377 num_examples: 75900 download_size: 5154346740 dataset_size: 5168528927.053 --- # Dataset Card for "food102-stockholm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
0xJustin	null	null	null	false	66	false	0xJustin/Dungeons-and-Diffusion	2022-11-12T04:42:41.000Z	null	false	af96f4b3b543cab5d4501c2c49bc03ef8a85f2da	[]	[]	https://huggingface.co/datasets/0xJustin/Dungeons-and-Diffusion/resolve/main/README.md	This is the dataset! Not the .ckpt trained model - the model is located here: https://huggingface.co/0xJustin/Dungeons-and-Diffusion/tree/main This dataset includes ~2500 images of fantasy RPG character art. This dataset has a distribution of races and classes, though only races are annotated right now. Additionally, BLIP captions were generated for all examples. Thus, there are two datasets- one with the human generated race annotation formatted as 'D&D Character, {race}' BLIP captions are formatted as 'D&D Character, {race} {caption}' for example: 'D&D Character, drow a woman with horns and horns' Distribution of races: ({'kenku': 31, 'drow': 162, 'tiefling': 285, 'dwarf': 116, 'dragonborn': 110, 'gnome': 72, 'orc': 184, 'aasimar': 74, 'kobold': 61, 'aarakocra': 24, 'tabaxi': 123, 'genasi': 126, 'human': 652, 'elf': 190, 'goblin': 80, 'halfling': 52, 'centaur': 22, 'firbolg': 76, 'goliath': 35}) There is a high chance some images are mislabelled! Please feel free to enrich this dataset with whatever attributes you think might be useful!
J3H0X77K	null	null	null	false	null	false	J3H0X77K/CHAMOX	2022-11-03T06:50:53.000Z	null	false	b66f8130f392e1d994cd96d646ac3a27ae93bdec	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/J3H0X77K/CHAMOX/resolve/main/README.md	--- license: afl-3.0 ---
amphora	null	null	null	false	3	false	amphora/KorFin-ABSA	2022-11-04T03:36:00.000Z	null	false	22a9cbd93ddcece0c69ed46d54da12f78cd7088e	[]	[ "annotations_creators:expert-generated", "language:ko", "language_creators:expert-generated", "license:apache-2.0", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:klue", "tags:sentiment analysis", "tags:aspect based sentiment analysis", "tags:finance", "task_categories:text-classification", "task_ids:topic-classification", "task_ids:sentiment-classification" ]	https://huggingface.co/datasets/amphora/KorFin-ABSA/resolve/main/README.md	--- annotations_creators: - expert-generated language: - ko language_creators: - expert-generated license: - apache-2.0 multilinguality: - monolingual pretty_name: KorFin-ABSA size_categories: - 1K<n<10K source_datasets: - klue tags: - sentiment analysis - aspect based sentiment analysis - finance task_categories: - text-classification task_ids: - topic-classification - sentiment-classification --- # Dataset Card for KorFin-ABSA ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary The KorFin-ABSA includes 3,002 sentences with (aspect, polarity) pairs annotated. The sentences were collected from [KLUE-TC](https://klue-benchmark.com/tasks/66/overview/description). Annotation of the dataset is described in the paper (about to be published). ### Supported Tasks and Leaderboards This dataset supports the following tasks: * Aspect-Based Sentiment Classification ### Languages Korean ## Dataset Structure ### Data Instances Each instance consists of a single sentence, aspect, and corresponding polarity (POSITIVE/NEGATIVE/NEUTRAL). ``` { "title": "LGU＋ 1분기 영업익 1천706억원…마케팅 비용 감소", "aspect": "LG U+", 'sentiment': 'NEUTRAL', 'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008363739', 'annotator_id': 'A_01', 'Type': 'single' } ``` ### Data Fields * title: * aspect: * sentiment: * url: * annotator_id: * url: ### Data Splits The dataset currently does not contain standard data splits. ## Additional Information You can download the data via: ``` from datasets import load_dataset dataset = load_dataset("amphora/KorFin-ABSA") ``` Please find more information about the code and how the data was collected in the paper (About to be added.). ### Licensing Information [apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Citation Information Please cite this data using: ``` About to be added ``` ### Contributions Thanks to [@Albertmade](https://github.com/h-albert-lee), [@amphora](https://github.com/guijinSON) for making this dataset.
nev	null	null	null	false	null	false	nev/worm-activity-data	2022-11-03T09:02:28.000Z	null	false	065fc8ac2f9921f39cd03a5003377589a48293ee	[]	[ "license:cc-by-4.0" ]	https://huggingface.co/datasets/nev/worm-activity-data/resolve/main/README.md	--- license: cc-by-4.0 ---
lewtun	null	null	null	false	1	false	lewtun/music_genres_small	2022-11-03T13:36:49.000Z	null	false	ab4b90142da320df49a31aaa9fa8df1df67d123f	[]	[]	https://huggingface.co/datasets/lewtun/music_genres_small/resolve/main/README.md	--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: int64 - name: genre_id dtype: int64 - name: genre dtype: string splits: - name: train num_bytes: 392427659.9527852 num_examples: 1000 download_size: 390675126 dataset_size: 392427659.9527852 --- # Dataset Card for "music_genres_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Markmus	null	null	null	false	null	false	Markmus/amazon-shoe-reviews	2022-11-03T13:41:50.000Z	null	false	17e87976452beb6cd28dd83ee3b98604fca98632	[]	[]	https://huggingface.co/datasets/Markmus/amazon-shoe-reviews/resolve/main/README.md	--- dataset_info: features: - name: labels dtype: int64 - name: text dtype: string splits: - name: test num_bytes: 1871962.8 num_examples: 10000 - name: train num_bytes: 16847665.2 num_examples: 90000 download_size: 10939033 dataset_size: 18719628.0 --- # Dataset Card for "amazon-shoe-reviews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Matthaios	null	null	null	false	2	false	Matthaios/amazon-shoe-reviews	2022-11-03T13:43:56.000Z	null	false	c0e1f6c4ab0b7ec8268e9eed39185c002df10344	[]	[]	https://huggingface.co/datasets/Matthaios/amazon-shoe-reviews/resolve/main/README.md	--- dataset_info: features: - name: labels dtype: int64 - name: text dtype: string splits: - name: test num_bytes: 1871962.8 num_examples: 10000 - name: train num_bytes: 16847665.2 num_examples: 90000 download_size: 10939031 dataset_size: 18719628.0 --- # Dataset Card for "amazon-shoe-reviews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Wannita	null	null	null	false	null	false	Wannita/PyCoder	2022-11-03T14:31:36.000Z	null	false	62ad9144b47a3ea76959499677b60f5c45d189aa	[]	[ "license:mit" ]	https://huggingface.co/datasets/Wannita/PyCoder/resolve/main/README.md	--- license: mit ---

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