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c219307f7fd35f295dcd0cdf4cc94cd949158b30
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-6.7b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-7776e8-1573055858
[ "autotrain", "evaluation", "region:us" ]
2022-09-27T15:14:32+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-6.7b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-09-27T15:30:46+00:00
4596f8cd06aa6f0fc71957d2e6a1f33c8664b643
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-1.3b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955856
[ "autotrain", "evaluation", "region:us" ]
2022-09-27T15:14:33+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-1.3b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-09-27T15:17:41+00:00
fba43e6d568abcfdab87ffe3068571fd21dca450
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-13b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-7776e8-1573055859
[ "autotrain", "evaluation", "region:us" ]
2022-09-27T15:14:34+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-13b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-09-27T15:43:28+00:00
25a3771e345e9226611b04bc2bd695eaebad972e
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-2.7b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955857
[ "autotrain", "evaluation", "region:us" ]
2022-09-27T15:14:36+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-2.7b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-09-27T15:19:55+00:00
36506bf4050ad3043e111c1812be9c557b238954
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-30b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-7776e8-1573055860
[ "autotrain", "evaluation", "region:us" ]
2022-09-27T15:14:36+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-30b", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-09-27T16:25:03+00:00
2afaf26908533ee079a8fe1fb7d36c595b8d7176
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-350m * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955855
[ "autotrain", "evaluation", "region:us" ]
2022-09-27T15:14:38+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "facebook/opt-350m", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}}
2022-09-27T15:15:50+00:00
e17a8195959cef8071410fd7fa8c4130a16a3a72
# Dataset Card for "tner/wikiann" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/) - **Dataset:** WikiAnn - **Domain:** Wikipedia - **Number of Entity:** 3 ### Dataset Summary WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `LOC`, `ORG`, `PER` ## Dataset Structure ### Data Instances An example of `train` of `ja` looks as follows. ``` { 'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'], 'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json). ```python { "B-LOC": 0, "B-ORG": 1, "B-PER": 2, "I-LOC": 3, "I-ORG": 4, "I-PER": 5, "O": 6 } ``` ### Data Splits | language | train | validation | test | |:-------------|--------:|-------------:|-------:| | ace | 100 | 100 | 100 | | bg | 20000 | 10000 | 10000 | | da | 20000 | 10000 | 10000 | | fur | 100 | 100 | 100 | | ilo | 100 | 100 | 100 | | lij | 100 | 100 | 100 | | mzn | 100 | 100 | 100 | | qu | 100 | 100 | 100 | | su | 100 | 100 | 100 | | vi | 20000 | 10000 | 10000 | | af | 5000 | 1000 | 1000 | | bh | 100 | 100 | 100 | | de | 20000 | 10000 | 10000 | | fy | 1000 | 1000 | 1000 | | io | 100 | 100 | 100 | | lmo | 100 | 100 | 100 | | nap | 100 | 100 | 100 | | rm | 100 | 100 | 100 | | sv | 20000 | 10000 | 10000 | | vls | 100 | 100 | 100 | | als | 100 | 100 | 100 | | bn | 10000 | 1000 | 1000 | | diq | 100 | 100 | 100 | | ga | 1000 | 1000 | 1000 | | is | 1000 | 1000 | 1000 | | ln | 100 | 100 | 100 | | nds | 100 | 100 | 100 | | ro | 20000 | 10000 | 10000 | | sw | 1000 | 1000 | 1000 | | vo | 100 | 100 | 100 | | am | 100 | 100 | 100 | | bo | 100 | 100 | 100 | | dv | 100 | 100 | 100 | | gan | 100 | 100 | 100 | | it | 20000 | 10000 | 10000 | | lt | 10000 | 10000 | 10000 | | ne | 100 | 100 | 100 | | ru | 20000 | 10000 | 10000 | | szl | 100 | 100 | 100 | | wa | 100 | 100 | 100 | | an | 1000 | 1000 | 1000 | | br | 1000 | 1000 | 1000 | | el | 20000 | 10000 | 10000 | | gd | 100 | 100 | 100 | | ja | 20000 | 10000 | 10000 | | lv | 10000 | 10000 | 10000 | | nl | 20000 | 10000 | 10000 | | rw | 100 | 100 | 100 | | ta | 15000 | 1000 | 1000 | | war | 100 | 100 | 100 | | ang | 100 | 100 | 100 | | bs | 15000 | 1000 | 1000 | | eml | 100 | 100 | 100 | | gl | 15000 | 10000 | 10000 | | jbo | 100 | 100 | 100 | | map-bms | 100 | 100 | 100 | | nn | 20000 | 1000 | 1000 | | sa | 100 | 100 | 100 | | te | 1000 | 1000 | 1000 | | wuu | 100 | 100 | 100 | | ar | 20000 | 10000 | 10000 | | ca | 20000 | 10000 | 10000 | | en | 20000 | 10000 | 10000 | | gn | 100 | 100 | 100 | | jv | 100 | 100 | 100 | | mg | 100 | 100 | 100 | | no | 20000 | 10000 | 10000 | | sah | 100 | 100 | 100 | | tg | 100 | 100 | 100 | | xmf | 100 | 100 | 100 | | arc | 100 | 100 | 100 | | cbk-zam | 100 | 100 | 100 | | eo | 15000 | 10000 | 10000 | | gu | 100 | 100 | 100 | | ka | 10000 | 10000 | 10000 | | mhr | 100 | 100 | 100 | | nov | 100 | 100 | 100 | | scn | 100 | 100 | 100 | | th | 20000 | 10000 | 10000 | | yi | 100 | 100 | 100 | | arz | 100 | 100 | 100 | | cdo | 100 | 100 | 100 | | es | 20000 | 10000 | 10000 | | hak | 100 | 100 | 100 | | kk | 1000 | 1000 | 1000 | | mi | 100 | 100 | 100 | | oc | 100 | 100 | 100 | | sco | 100 | 100 | 100 | | tk | 100 | 100 | 100 | | yo | 100 | 100 | 100 | | as | 100 | 100 | 100 | | ce | 100 | 100 | 100 | | et | 15000 | 10000 | 10000 | | he | 20000 | 10000 | 10000 | | km | 100 | 100 | 100 | | min | 100 | 100 | 100 | | or | 100 | 100 | 100 | | sd | 100 | 100 | 100 | | tl | 10000 | 1000 | 1000 | | zea | 100 | 100 | 100 | | ast | 1000 | 1000 | 1000 | | ceb | 100 | 100 | 100 | | eu | 10000 | 10000 | 10000 | | hi | 5000 | 1000 | 1000 | | kn | 100 | 100 | 100 | | mk | 10000 | 1000 | 1000 | | os | 100 | 100 | 100 | | sh | 20000 | 10000 | 10000 | | tr | 20000 | 10000 | 10000 | | zh-classical | 100 | 100 | 100 | | ay | 100 | 100 | 100 | | ckb | 1000 | 1000 | 1000 | | ext | 100 | 100 | 100 | | hr | 20000 | 10000 | 10000 | | ko | 20000 | 10000 | 10000 | | ml | 10000 | 1000 | 1000 | | pa | 100 | 100 | 100 | | si | 100 | 100 | 100 | | tt | 1000 | 1000 | 1000 | | zh-min-nan | 100 | 100 | 100 | | az | 10000 | 1000 | 1000 | | co | 100 | 100 | 100 | | fa | 20000 | 10000 | 10000 | | hsb | 100 | 100 | 100 | | ksh | 100 | 100 | 100 | | mn | 100 | 100 | 100 | | pdc | 100 | 100 | 100 | | simple | 20000 | 1000 | 1000 | | ug | 100 | 100 | 100 | | zh-yue | 20000 | 10000 | 10000 | | ba | 100 | 100 | 100 | | crh | 100 | 100 | 100 | | fi | 20000 | 10000 | 10000 | | hu | 20000 | 10000 | 10000 | | ku | 100 | 100 | 100 | | mr | 5000 | 1000 | 1000 | | pl | 20000 | 10000 | 10000 | | sk | 20000 | 10000 | 10000 | | uk | 20000 | 10000 | 10000 | | zh | 20000 | 10000 | 10000 | | bar | 100 | 100 | 100 | | cs | 20000 | 10000 | 10000 | | fiu-vro | 100 | 100 | 100 | | hy | 15000 | 1000 | 1000 | | ky | 100 | 100 | 100 | | ms | 20000 | 1000 | 1000 | | pms | 100 | 100 | 100 | | sl | 15000 | 10000 | 10000 | | ur | 20000 | 1000 | 1000 | | bat-smg | 100 | 100 | 100 | | csb | 100 | 100 | 100 | | fo | 100 | 100 | 100 | | ia | 100 | 100 | 100 | | la | 5000 | 1000 | 1000 | | mt | 100 | 100 | 100 | | pnb | 100 | 100 | 100 | | so | 100 | 100 | 100 | | uz | 1000 | 1000 | 1000 | | be-x-old | 5000 | 1000 | 1000 | | cv | 100 | 100 | 100 | | fr | 20000 | 10000 | 10000 | | id | 20000 | 10000 | 10000 | | lb | 5000 | 1000 | 1000 | | mwl | 100 | 100 | 100 | | ps | 100 | 100 | 100 | | sq | 5000 | 1000 | 1000 | | vec | 100 | 100 | 100 | | be | 15000 | 1000 | 1000 | | cy | 10000 | 1000 | 1000 | | frr | 100 | 100 | 100 | | ig | 100 | 100 | 100 | | li | 100 | 100 | 100 | | my | 100 | 100 | 100 | | pt | 20000 | 10000 | 10000 | | sr | 20000 | 10000 | 10000 | | vep | 100 | 100 | 100 | ### Citation Information ``` @inproceedings{pan-etal-2017-cross, title = "Cross-lingual Name Tagging and Linking for 282 Languages", author = "Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P17-1178", doi = "10.18653/v1/P17-1178", pages = "1946--1958", abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.", } ```
tner/wikiann
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:multilingual", "size_categories:10K<100k", "language:ace", "language:bg", "language:da", "language:fur", "language:ilo", "language:lij", "language:mzn", "language:qu", "language:su", "language:vi", "language:af", "language:bh", "language:de", "language:fy", "language:io", "language:lmo", "language:nap", "language:rm", "language:sv", "language:vls", "language:als", "language:bn", "language:diq", "language:ga", "language:is", "language:ln", "language:nds", "language:ro", "language:sw", "language:vo", "language:am", "language:bo", "language:dv", "language:gan", "language:it", "language:lt", "language:ne", "language:ru", "language:szl", "language:wa", "language:an", "language:br", "language:el", "language:gd", "language:ja", "language:lv", "language:nl", "language:rw", "language:ta", "language:war", "language:ang", "language:bs", "language:eml", "language:gl", "language:jbo", "language:nn", "language:sa", "language:te", "language:wuu", "language:ar", "language:ca", "language:en", "language:gn", "language:jv", "language:mg", "language:no", "language:sah", "language:tg", "language:xmf", "language:arc", "language:eo", "language:gu", "language:ka", "language:mhr", "language:nov", "language:scn", "language:th", "language:yi", "language:arz", "language:cdo", "language:es", "language:hak", "language:kk", "language:mi", "language:oc", "language:sco", "language:tk", "language:yo", "language:as", "language:ce", "language:et", "language:he", "language:km", "language:min", "language:or", "language:sd", "language:tl", "language:zea", "language:ast", "language:ceb", "language:eu", "language:hi", "language:kn", "language:mk", "language:os", "language:sh", "language:tr", "language:ay", "language:ckb", "language:ext", "language:hr", "language:ko", "language:ml", "language:pa", "language:si", "language:tt", "language:az", "language:co", "language:fa", "language:hsb", "language:ksh", "language:mn", "language:pdc", "language:ug", "language:ba", "language:crh", "language:fi", "language:hu", "language:ku", "language:mr", "language:pl", "language:sk", "language:uk", "language:zh", "language:bar", "language:cs", "language:hy", "language:ky", "language:ms", "language:pms", "language:sl", "language:ur", "language:csb", "language:fo", "language:ia", "language:la", "language:mt", "language:pnb", "language:so", "language:uz", "language:cv", "language:fr", "language:id", "language:lb", "language:mwl", "language:ps", "language:sq", "language:vec", "language:be", "language:cy", "language:frr", "language:ig", "language:li", "language:my", "language:pt", "language:sr", "region:us" ]
2022-09-27T15:22:58+00:00
{"language": ["ace", "bg", "da", "fur", "ilo", "lij", "mzn", "qu", "su", "vi", "af", "bh", "de", "fy", "io", "lmo", "nap", "rm", "sv", "vls", "als", "bn", "diq", "ga", "is", "ln", "nds", "ro", "sw", "vo", "am", "bo", "dv", "gan", "it", "lt", "ne", "ru", "szl", "wa", "an", "br", "el", "gd", "ja", "lv", "nl", "rw", "ta", "war", "ang", "bs", "eml", "gl", "jbo", "nn", "sa", "te", "wuu", "ar", "ca", "en", "gn", "jv", "mg", false, "sah", "tg", "xmf", "arc", "eo", "gu", "ka", "mhr", "nov", "scn", "th", "yi", "arz", "cdo", "es", "hak", "kk", "mi", "oc", "sco", "tk", "yo", "as", "ce", "et", "he", "km", "min", "or", "sd", "tl", "zea", "ast", "ceb", "eu", "hi", "kn", "mk", "os", "sh", "tr", "ay", "ckb", "ext", "hr", "ko", "ml", "pa", "si", "tt", "az", "co", "fa", "hsb", "ksh", "mn", "pdc", "ug", "ba", "crh", "fi", "hu", "ku", "mr", "pl", "sk", "uk", "zh", "bar", "cs", "hy", "ky", "ms", "pms", "sl", "ur", "csb", "fo", "ia", "la", "mt", "pnb", "so", "uz", "cv", "fr", "id", "lb", "mwl", "ps", "sq", "vec", "be", "cy", "frr", "ig", "li", "my", "pt", "sr"], "multilinguality": ["multilingual"], "size_categories": ["10K<100k"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WikiAnn"}
2022-09-27T17:39:42+00:00
b04b5ce4ae52ba21af979685fe68bbf29782951a
ErikSihab/erik-sihab
[ "region:us" ]
2022-09-27T15:54:55+00:00
{}
2022-09-27T22:14:07+00:00
a7b32d401cdb057eb9a204e471db642abe3058ab
freddyaboulton/gradio-reviews
[ "license:mit", "region:us" ]
2022-09-27T16:49:12+00:00
{"license": "mit"}
2023-11-03T19:13:19+00:00
ce7483a909a7b68ddc02920087462355f7680057
# Dataset Card for "tner/wikineural" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/) - **Dataset:** WikiNeural - **Domain:** Wikipedia - **Number of Entity:** 16 ### Dataset Summary WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC` ## Dataset Structure ### Data Instances An example of `train` of `de` looks as follows. ``` { 'tokens': [ "Dieses", "wiederum", "basierte", "auf", "dem", "gleichnamigen", "Roman", "von", "Noël", "Calef", "." ], 'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0 ] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/raw/main/dataset/label.json). ```python { "O": 0, "B-PER": 1, "I-PER": 2, "B-LOC": 3, "I-LOC": 4, "B-ORG": 5, "I-ORG": 6, "B-ANIM": 7, "I-ANIM": 8, "B-BIO": 9, "I-BIO": 10, "B-CEL": 11, "I-CEL": 12, "B-DIS": 13, "I-DIS": 14, "B-EVE": 15, "I-EVE": 16, "B-FOOD": 17, "I-FOOD": 18, "B-INST": 19, "I-INST": 20, "B-MEDIA": 21, "I-MEDIA": 22, "B-PLANT": 23, "I-PLANT": 24, "B-MYTH": 25, "I-MYTH": 26, "B-TIME": 27, "I-TIME": 28, "B-VEHI": 29, "I-VEHI": 30, "B-MISC": 31, "I-MISC": 32 } ``` ### Data Splits | language | train | validation | test | |:-----------|--------:|-------------:|-------:| | de | 98640 | 12330 | 12372 | | en | 92720 | 11590 | 11597 | | es | 76320 | 9540 | 9618 | | fr | 100800 | 12600 | 12678 | | it | 88400 | 11050 | 11069 | | nl | 83680 | 10460 | 10547 | | pl | 108160 | 13520 | 13585 | | pt | 80560 | 10070 | 10160 | | ru | 92320 | 11540 | 11580 | ### Citation Information ``` @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", doi = "10.18653/v1/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } ```
tner/wikineural
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:multilingual", "size_categories:10K<100k", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "region:us" ]
2022-09-27T16:56:40+00:00
{"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "multilinguality": ["multilingual"], "size_categories": ["10K<100k"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WikiNeural"}
2022-09-27T18:46:37+00:00
3c9285ea8a531da6066ac04bb17394bc8e8ca3b6
# Dataset Card for "pip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-source-metrics/pip
[ "region:us" ]
2022-09-27T17:19:45+00:00
{"dataset_info": {"features": [{"name": "day", "dtype": "string"}, {"name": "num_downloads", "dtype": "int64"}], "splits": [{"name": "gradio", "num_bytes": 27742, "num_examples": 1261}, {"name": "safetensors", "num_bytes": 9812, "num_examples": 446}, {"name": "optimum", "num_bytes": 19360, "num_examples": 880}, {"name": "evaluate", "num_bytes": 16346, "num_examples": 743}, {"name": "huggingface_hub", "num_bytes": 25256, "num_examples": 1148}, {"name": "pytorch_image_models", "num_bytes": 27742, "num_examples": 1261}, {"name": "accelerate", "num_bytes": 24376, "num_examples": 1108}, {"name": "tokenizers", "num_bytes": 27742, "num_examples": 1261}, {"name": "transformers", "num_bytes": 28424, "num_examples": 1292}, {"name": "peft", "num_bytes": 8602, "num_examples": 391}, {"name": "diffusers", "num_bytes": 13750, "num_examples": 625}, {"name": "datasets", "num_bytes": 24376, "num_examples": 1108}], "download_size": 148060, "dataset_size": 253528}, "configs": [{"config_name": "default", "data_files": [{"split": "accelerate", "path": "data/accelerate-*"}, {"split": "datasets", "path": "data/datasets-*"}, {"split": "diffusers", "path": "data/diffusers-*"}, {"split": "evaluate", "path": "data/evaluate-*"}, {"split": "gradio", "path": "data/gradio-*"}, {"split": "huggingface_hub", "path": "data/huggingface_hub-*"}, {"split": "optimum", "path": "data/optimum-*"}, {"split": "peft", "path": "data/peft-*"}, {"split": "pytorch_image_models", "path": "data/pytorch_image_models-*"}, {"split": "safetensors", "path": "data/safetensors-*"}, {"split": "tokenizers", "path": "data/tokenizers-*"}, {"split": "transformers", "path": "data/transformers-*"}]}]}
2024-02-15T11:18:27+00:00
533a80b990626e7984be36fbfeb2371c425b2a27
chunkeduptube/chunkis
[ "license:artistic-2.0", "region:us" ]
2022-09-27T17:21:14+00:00
{"license": "artistic-2.0"}
2022-09-27T17:26:34+00:00
facdfd1c6f139820e44b5dd7b341d056fbe2044e
# Dataset Card for "tner/multinerd" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://aclanthology.org/2022.findings-naacl.60/](https://aclanthology.org/2022.findings-naacl.60/) - **Dataset:** MultiNERD - **Domain:** Wikipedia, WikiNews - **Number of Entity:** 18 ### Dataset Summary MultiNERD NER benchmark dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`, `SUPER`, `PHY` ## Dataset Structure ### Data Instances An example of `train` of `de` looks as follows. ``` { 'tokens': [ "Die", "Blätter", "des", "Huflattichs", "sind", "leicht", "mit", "den", "sehr", "ähnlichen", "Blättern", "der", "Weißen", "Pestwurz", "(", "\"", "Petasites", "albus", "\"", ")", "zu", "verwechseln", "." ], 'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0 ] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/multinerd/raw/main/dataset/label.json). ```python { "O": 0, "B-PER": 1, "I-PER": 2, "B-LOC": 3, "I-LOC": 4, "B-ORG": 5, "I-ORG": 6, "B-ANIM": 7, "I-ANIM": 8, "B-BIO": 9, "I-BIO": 10, "B-CEL": 11, "I-CEL": 12, "B-DIS": 13, "I-DIS": 14, "B-EVE": 15, "I-EVE": 16, "B-FOOD": 17, "I-FOOD": 18, "B-INST": 19, "I-INST": 20, "B-MEDIA": 21, "I-MEDIA": 22, "B-PLANT": 23, "I-PLANT": 24, "B-MYTH": 25, "I-MYTH": 26, "B-TIME": 27, "I-TIME": 28, "B-VEHI": 29, "I-VEHI": 30, "B-SUPER": 31, "I-SUPER": 32, "B-PHY": 33, "I-PHY": 34 } ``` ### Data Splits | language | test | |:-----------|-------:| | de | 156792 | | en | 164144 | | es | 173189 | | fr | 176185 | | it | 181927 | | nl | 171711 | | pl | 194965 | | pt | 177565 | | ru | 82858 | ### Citation Information ``` @inproceedings{tedeschi-navigli-2022-multinerd, title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)", author = "Tedeschi, Simone and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-naacl.60", doi = "10.18653/v1/2022.findings-naacl.60", pages = "801--812", abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.", } ```
tner/multinerd
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:multilingual", "size_categories:<10K", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "region:us" ]
2022-09-27T18:13:36+00:00
{"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "multilinguality": ["multilingual"], "size_categories": ["<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "MultiNERD"}
2022-09-27T18:48:40+00:00
dfd59f85a7256d183b215f86b8ad1c8a8bdc6ec3
LucaBlight/Kheiron
[ "license:afl-3.0", "region:us" ]
2022-09-27T19:36:17+00:00
{"license": "afl-3.0"}
2022-09-27T19:36:17+00:00
ebbb3a2ae953c0a73ab3db40e849c6c23a82542a
--- Sample --- - 6900 transcripts - 44 churches - timeframe: 2010-2022 - Denomination: Unitarian Universalist, USA --- Dataset structure --- - church (church name or website) - source (mp3 file) - text - sentences (count) - errors (number of sentences skipped because could not understand audio, or just long pauses skipped) - duration (in seconds) --- Dataset creation --- - see notebook in files
marcmaxmeister/unitarian-universalist-sermons
[ "license:mit", "region:us" ]
2022-09-27T21:11:20+00:00
{"license": "mit"}
2022-09-28T20:04:16+00:00
6947305648990c358f904def2a18cc3cc62fd4c0
The code is provided under a Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Under the license, the code is provided royalty free for non-commercial purposes only. The code may be covered by patents and if you want to use the code for commercial purposes, please contact us for a different license. This dataset is a pre-processed small sample of the Waymo Open Motion Dataset intended for illustration purposes only.
jmercat/risk_biased_dataset
[ "license:cc-by-nc-4.0", "region:us" ]
2022-09-27T21:35:21+00:00
{"license": "cc-by-nc-4.0"}
2023-08-01T18:08:31+00:00
01982dd3e03603a1e07e2c2d9ad30d0a5a722e95
Zavek/Contradictory-xnli
[ "license:other", "region:us" ]
2022-09-27T23:49:35+00:00
{"license": "other"}
2022-09-28T00:37:20+00:00
e01e8edff5797a78f34c568ecab33a64794842f2
zyznull/msmarco-passage-ranking
[ "license:apache-2.0", "region:us" ]
2022-09-28T01:29:39+00:00
{"license": "apache-2.0"}
2022-09-28T02:30:10+00:00
074942294995d8a21a045f2dcafcb9dd19966991
zyznull/msmarco-passage-corpus
[ "license:mit", "region:us" ]
2022-09-28T05:15:51+00:00
{"license": "mit"}
2023-01-09T08:16:28+00:00
b7b9168a7ce51714c0914a4ac7c8511abc3d82c3
dhruvs00/datahogyaset
[ "license:openrail", "region:us" ]
2022-09-28T05:46:48+00:00
{"license": "openrail"}
2022-09-28T05:46:48+00:00
5e92c47f62e3a16dc4b38ed70aa8841eacb22514
--- annotations_creators: - expert-generated language: - en language_creators: - found license: - mit multilinguality: - monolingual paperswithcode_id: acronym-identification pretty_name: datahogyas size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - token-classification task_ids: - part-of-speech train-eval-index: - col_mapping: labels: tags tokens: tokens config: default splits: eval_split: test task: token-classification task_id: entity_extraction ---
dhruvs00/datahogyas
[ "region:us" ]
2022-09-28T05:47:21+00:00
{}
2022-09-28T07:08:02+00:00
5e2a11d9729621f0375b6ccd1114d335c6ee1b94
# Dummy Dataset for AutoTrain Benchmark This dataset contains dummy data that's needed to create AutoTrain projects for benchmarks like [RAFT](https://huggingface.co/spaces/ought/raft-leaderboard). See [here](https://github.com/huggingface/hf_benchmarks) for more details.
autoevaluator/benchmark-dummy-data
[ "region:us" ]
2022-09-28T06:57:08+00:00
{}
2022-11-18T13:19:56+00:00
519a29f2934a650967d7c6c99f4c53ed99e083d0
# dureader 数据来自DuReader-Retreval数据集,这里是[原始地址](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval)。 > 本数据集只用作学术研究使用。如果本仓库涉及侵权行为,会立即删除。
zyznull/dureader-retrieval-corpus
[ "license:apache-2.0", "region:us" ]
2022-09-28T07:03:03+00:00
{"license": "apache-2.0"}
2023-01-03T08:05:06+00:00
f33c72ade15f98638f3598a9ca4ac989d21f699e
All eight of datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library: ```python from datasets import load_dataset librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", split="train") ``` - `"esc-benchmark"`: the repository namespace. This is fixed for all ESC datasets. - `"librispeech"`: the dataset name. This can be changed to any of any one of the eight datasets in ESC to download that dataset. - `split="train"`: the split. Set this to one of train/validation/test to generate a specific split. Omit the `split` argument to generate all splits for a dataset. The datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts. ## Dataset Information A data point can be accessed by indexing the dataset object loaded through `load_dataset`: ```python print(librispeech[0]) ``` A typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name: ```python { 'dataset': 'librispeech', 'audio': {'path': '/home/esc-bencher/.cache/huggingface/datasets/downloads/extracted/d2da1969fe9e7d06661b5dc370cf2e3c119a14c35950045bcb76243b264e4f01/374-180298-0000.flac', 'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ..., -2.74658203e-04, -1.83105469e-04, -3.05175781e-05]), 'sampling_rate': 16000}, 'text': 'chapter sixteen i might have told you of the beginning of this liaison in a few lines but i wanted you to see every step by which we came i to agree to whatever marguerite wished', 'id': '374-180298-0000' } ``` ### Data Fields - `dataset`: name of the ESC dataset from which the sample is taken. - `audio`: a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `text`: the transcription of the audio file. - `id`: unique id of the data sample. ### Data Preparation #### Audio The audio for all ESC datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts. 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]`. #### Transcriptions The transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_&lt;unk>_) or converting symbolic punctuation to spelled out form (_&lt;comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts. Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esc-benchmark/esc for scoring. ### Access All eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages: * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0 * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech ## LibriSpeech The LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the [LibriVox](https://librivox.org) project. It is licensed under CC-BY-4.0. Example Usage: ```python librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech") ``` Train/validation splits: - `train` (combination of `train.clean.100`, `train.clean.360` and `train.other.500`) - `validation.clean` - `validation.other` Test splits: - `test.clean` - `test.other` Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument: ```python librispeech = load_dataset("esc-benchmark/esc-datasets", "librispeech", subconfig="clean.100") ``` - `clean.100`: 100 hours of training data from the 'clean' subset - `clean.360`: 360 hours of training data from the 'clean' subset - `other.500`: 500 hours of training data from the 'other' subset ## Common Voice Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0. Example usage: ```python common_voice = load_dataset("esc-benchmark/esc-datasets", "common_voice", use_auth_token=True) ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## VoxPopuli VoxPopuli s a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0. Example usage: ```python voxpopuli = load_dataset("esc-benchmark/esc-datasets", "voxpopuli") ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## TED-LIUM TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0. Example usage: ```python tedlium = load_dataset("esc-benchmark/esc-datasets", "tedlium") ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## GigaSpeech GigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0. Example usage: ```python gigaspeech = load_dataset("esc-benchmark/esc-datasets", "gigaspeech", use_auth_token=True) ``` Training/validation splits: - `train` (`l` subset of training data (2,500 h)) - `validation` Test splits: - `test` Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument: ```python gigaspeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="xs", use_auth_token=True) ``` - `xs`: extra-small subset of training data (10 h) - `s`: small subset of training data (250 h) - `m`: medium subset of training data (1,000 h) - `xl`: extra-large subset of training data (10,000 h) ## SPGISpeech SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement. Loading the dataset requires authorization. Example usage: ```python spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", use_auth_token=True) ``` Training/validation splits: - `train` (`l` subset of training data (~5,000 h)) - `validation` Test splits: - `test` Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument: ```python spgispeech = load_dataset("esc-benchmark/esc-datasets", "spgispeech", subconfig="s", use_auth_token=True) ``` - `s`: small subset of training data (~200 h) - `m`: medium subset of training data (~1,000 h) ## Earnings-22 Earnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0. Example usage: ```python earnings22 = load_dataset("esc-benchmark/esc-datasets", "earnings22") ``` Training/validation splits: - `train` - `validation` Test splits: - `test` ## AMI The AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0. Example usage: ```python ami = load_dataset("esc-benchmark/esc-datasets", "ami") ``` Training/validation splits: - `train` - `validation` Test splits: - `test`
esc-benchmark/esc-datasets
[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:original", "source_datasets:extended|librispeech_asr", "source_datasets:extended|common_voice", "language:en", "license:cc-by-4.0", "license:apache-2.0", "license:cc0-1.0", "license:cc-by-nc-3.0", "license:other", "asr", "benchmark", "speech", "esc", "region:us" ]
2022-09-28T07:40:04+00:00
{"annotations_creators": ["expert-generated", "crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["cc-by-4.0", "apache-2.0", "cc0-1.0", "cc-by-nc-3.0", "other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "1M<n<10M"], "source_datasets": ["original", "extended|librispeech_asr", "extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "esc-datasets", "tags": ["asr", "benchmark", "speech", "esc"], "extra_gated_prompt": "Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. \nTo do so, fill in the access forms on the specific datasets' pages:\n * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0\n * GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech\n * SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech", "extra_gated_fields": {"I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset": "checkbox", "I hereby confirm that I have accepted the terms of usages on GigaSpeech page": "checkbox", "I hereby confirm that I have accepted the terms of usages on SPGISpeech page": "checkbox"}}
2022-10-14T13:30:30+00:00
70ae446852c18cf146a29082a2acf66e74609cd8
# dureader 数据来自DuReader-Retreval数据集,这里是[原始地址](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval)。 > 本数据集只用作学术研究使用。如果本仓库涉及侵权行为,会立即删除。
zyznull/dureader-retrieval-ranking
[ "license:apache-2.0", "region:us" ]
2022-09-28T08:00:20+00:00
{"license": "apache-2.0"}
2023-01-03T08:05:57+00:00
e7da52d27ed5301d1f0f4c7359c04f95befbada5
mayjestro/LittleHodler
[ "license:c-uda", "region:us" ]
2022-09-28T08:30:12+00:00
{"license": "c-uda"}
2022-09-28T13:30:31+00:00
0d792180b9349c544a2ea220de6b72f78611fb17
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: facebook/bart-large-cnn * Dataset: big_patent * Config: g * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@jonesdaniel](https://huggingface.co/jonesdaniel) for evaluating this model.
autoevaluate/autoeval-eval-big_patent-g-9d42aa-1581555947
[ "autotrain", "evaluation", "region:us" ]
2022-09-28T08:54:38+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["big_patent"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-cnn", "metrics": ["perplexity"], "dataset_name": "big_patent", "dataset_config": "g", "dataset_split": "validation", "col_mapping": {"text": "description", "target": "abstract"}}}
2022-09-28T10:15:24+00:00
c801dc186b40a532c5820b4662570390da90431b
# Dataset Card for "tacred" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://nlp.stanford.edu/projects/tacred](https://nlp.stanford.edu/projects/tacred) - **Paper:** [Position-aware Attention and Supervised Data Improve Slot Filling](https://aclanthology.org/D17-1004/) - **Point of Contact:** See [https://nlp.stanford.edu/projects/tacred/](https://nlp.stanford.edu/projects/tacred/) - **Size of downloaded dataset files:** 62.3 MB - **Size of the generated dataset:** 139.2 MB - **Total amount of disk used:** 201.5 MB ### Dataset Summary The TAC Relation Extraction Dataset (TACRED) is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC KBP challenges and crowdsourcing. Please see [Stanford's EMNLP paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf), or their [EMNLP slides](https://nlp.stanford.edu/projects/tacred/files/position-emnlp2017.pdf) for full details. Note: - There is currently a [label-corrected version](https://github.com/DFKI-NLP/tacrev) of the TACRED dataset, which you should consider using instead of the original version released in 2017. For more details on this new version, see the [TACRED Revisited paper](https://aclanthology.org/2020.acl-main.142/) published at ACL 2020. - There is also a [relabeled and pruned version](https://github.com/gstoica27/Re-TACRED) of the TACRED dataset. For more details on this new version, see the [Re-TACRED paper](https://arxiv.org/abs/2104.08398) published at ACL 2020. This repository provides all three versions of the dataset as BuilderConfigs - `'original'`, `'revisited'` and `'re-tacred'`. Simply set the `name` parameter in the `load_dataset` method in order to choose a specific version. The original TACRED is loaded per default. ### Supported Tasks and Leaderboards - **Tasks:** Relation Classification - **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-tacred](https://paperswithcode.com/sota/relation-extraction-on-tacred) ### Languages The language in the dataset is English. ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 62.3 MB - **Size of the generated dataset:** 139.2 MB - **Total amount of disk used:** 201.5 MB An example of 'train' looks as follows: ```json { "id": "61b3a5c8c9a882dcfcd2", "docid": "AFP_ENG_20070218.0019.LDC2009T13", "relation": "org:founded_by", "token": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."], "subj_start": 10, "subj_end": 13, "obj_start": 0, "obj_end": 2, "subj_type": "ORGANIZATION", "obj_type": "PERSON", "stanford_pos": ["NNP", "NNP", "VBD", "IN", "NNP", "JJ", "NN", "TO", "VB", "DT", "DT", "NNP", "NNP", "-LRB-", "NNP", "-RRB-", ",", "VBG", "DT", "NN", "IN", "CD", "NNS", "IN", "NN", ",", "VBG", "JJ", "NN", "NNP", "NNP", "NNP", "TO", "VB", "NN", "CC", "VB", "DT", "NN", "NN", "."], "stanford_ner": ["PERSON", "PERSON", "O", "O", "DATE", "DATE", "DATE", "O", "O", "O", "O", "O", "O", "O", "ORGANIZATION", "O", "O", "O", "O", "O", "O", "NUMBER", "O", "O", "O", "O", "O", "O", "O", "O", "PERSON", "PERSON", "O", "O", "O", "O", "O", "O", "O", "O", "O"], "stanford_head": [2, 3, 0, 5, 3, 7, 3, 9, 3, 13, 13, 13, 9, 15, 13, 15, 3, 3, 20, 18, 23, 23, 18, 25, 23, 3, 3, 32, 32, 32, 32, 27, 34, 27, 34, 34, 34, 40, 40, 37, 3], "stanford_deprel": ["compound", "nsubj", "ROOT", "case", "nmod", "amod", "nmod:tmod", "mark", "xcomp", "det", "compound", "compound", "dobj", "punct", "appos", "punct", "punct", "xcomp", "det", "dobj", "case", "nummod", "nmod", "case", "nmod", "punct", "xcomp", "amod", "compound", "compound", "compound", "dobj", "mark", "xcomp", "dobj", "cc", "conj", "det", "compound", "dobj", "punct"] } ``` ### Data Fields The data fields are the same among all splits. - `id`: the instance id of this sentence, a `string` feature. - `docid`: the TAC KBP document id of this sentence, a `string` feature. - `token`: the list of tokens of this sentence, obtained with the StanfordNLP toolkit, a `list` of `string` features. - `relation`: the relation label of this instance, a `string` classification label. - `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature. - `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature. - `subj_type`: the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature. - `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature. - `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature. - `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature. - `stanford_pos`: the part-of-speech tag per token. the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features. - `stanford_ner`: the NER tags of tokens (IO-Scheme), among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features. - `stanford_deprel`: the Stanford dependency relation tag per token, a `list` of `string` features. - `stanford_head`: the head (source) token index (0-based) for the dependency relation per token. The root token has a head index of -1, a `list` of `int` features. ### Data Splits To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run: | | Train | Dev | Test | | ----- | ------ | ----- | ---- | | TACRED | 68,124 (TAC KBP 2009-2012) | 22,631 (TAC KBP 2013) | 15,509 (TAC KBP 2014) | | Re-TACRED | 58,465 (TAC KBP 2009-2012) | 19,584 (TAC KBP 2013) | 13,418 (TAC KBP 2014) | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process See the Stanford paper and the Tacred Revisited paper, plus their appendices. To ensure that models trained on TACRED are not biased towards predicting false positives on real-world text, all sampled sentences where no relation was found between the mention pairs were fully annotated to be negative examples. As a result, 79.5% of the examples are labeled as no_relation. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information To respect the copyright of the underlying TAC KBP corpus, TACRED is released via the Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)). You can download TACRED from the [LDC TACRED webpage](https://catalog.ldc.upenn.edu/LDC2018T24). If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed. ### Citation Information The original dataset: ``` @inproceedings{zhang2017tacred, author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.}, booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, title = {Position-aware Attention and Supervised Data Improve Slot Filling}, url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf}, pages = {35--45}, year = {2017} } ``` For the revised version (`"revisited"`), please also cite: ``` @inproceedings{alt-etal-2020-tacred, title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task", author = "Alt, Christoph and Gabryszak, Aleksandra and Hennig, Leonhard", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.142", doi = "10.18653/v1/2020.acl-main.142", pages = "1558--1569", } ``` For the relabeled version (`"re-tacred"`), please also cite: ``` @inproceedings{DBLP:conf/aaai/StoicaPP21, author = {George Stoica and Emmanouil Antonios Platanios and Barnab{\'{a}}s P{\'{o}}czos}, title = {Re-TACRED: Addressing Shortcomings of the {TACRED} Dataset}, booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9, 2021}, pages = {13843--13850}, publisher = {{AAAI} Press}, year = {2021}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/17631}, } ``` ### Contributions Thanks to [@dfki-nlp](https://github.com/dfki-nlp) and [@phucdev](https://github.com/phucdev) for adding this dataset.
DFKI-SLT/tacred
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|other", "language:en", "license:other", "relation extraction", "arxiv:2104.08398", "region:us" ]
2022-09-28T09:02:34+00:00
{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|other"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "The TAC Relation Extraction Dataset, TACRED Revisited and Re-TACRED", "tags": ["relation extraction"]}
2023-05-17T11:55:00+00:00
812fc620b27eb25e0a3b85699e631e01e407c7dd
awkwardneutrino/daniellismore-01
[ "license:openrail", "region:us" ]
2022-09-28T09:14:03+00:00
{"license": "openrail"}
2022-09-28T09:25:12+00:00
c385a6a9a7c200cde48d6b7ed171e9187db8c99a
--- annotations_creators: - found language_creators: - found language: - ar license: - other multilinguality: - monolingual pretty_name: disTD task_categories: - token-classification task_ids: - disfluency-detection dataset_info: features: - name: tokens sequence: string - name: isDisf sequence: class_label: names: 0: O 1: B_RM 2: I_RM 3: B_RP 4: I_RP 5: IP config_name: disTD # Dataset Card for myds ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary dataset for Tunisian dialect ### Supported Tasks and Leaderboards [Needs More Information] ### Languages tuanisian arabic dialect ## Dataset Structure ### Data Instances Size of downloaded dataset files: 4.63 MB Size of the generated dataset: 9.78 MB Total amount of disk used: 14.41 MB ### Data Fields dsfsergrth ### Data Splits rtsert ## Dataset Creation ### Curation Rationale link ### Source Data #### Initial Data Collection and Normalization kink #### Who are the source language producers? link ### Annotations #### Annotation process tool #### Who are the annotators? me ### Personal and Sensitive Information ok ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]
EmnaBou/tokenDS
[ "region:us" ]
2022-09-28T10:34:05+00:00
{}
2022-11-30T11:32:39+00:00
91e996a3d990bddbd4c554f54ebe821afc978fb9
# UD_Catalan-AnCora ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Website:** https://github.com/UniversalDependencies/UD_Catalan-AnCora - **Point of Contact:** [Daniel Zeman]([email protected]) ### Dataset Summary This dataset is composed of the annotations from the [AnCora corpus](http://clic.ub.edu/corpus/), projected on the [Universal Dependencies treebank](https://universaldependencies.org/). We use the POS annotations of this corpus as part of the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/). This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC Attribution 4.0 International License</a>. ### Supported Tasks and Leaderboards POS tagging ### Languages The dataset is in Catalan (`ca-ES`) ## Dataset Structure ### Data Instances Three conllu files. Annotations are encoded in plain text files (UTF-8, normalized to NFC, using only the LF character as line break, including an LF character at the end of file) with three types of lines: 1) Word lines containing the annotation of a word/token in 10 fields separated by single tab characters (see below). 2) Blank lines marking sentence boundaries. 3) Comment lines starting with hash (#). ### Data Fields Word lines contain the following fields: 1) ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes (decimal numbers can be lower than 1 but must be greater than 0). 2) FORM: Word form or punctuation symbol. 3) LEMMA: Lemma or stem of word form. 4) UPOS: Universal part-of-speech tag. 5) XPOS: Language-specific part-of-speech tag; underscore if not available. 6) FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available. 7) HEAD: Head of the current word, which is either a value of ID or zero (0). 8) DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one. 9) DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs. 10) MISC: Any other annotation. From: [https://universaldependencies.org](https://universaldependencies.org/guidelines.html) ### Data Splits - ca_ancora-ud-train.conllu - ca_ancora-ud-dev.conllu - ca_ancora-ud-test.conllu ## Dataset Creation ### Curation Rationale [N/A] ### Source Data - [UD_Catalan-AnCora](https://github.com/UniversalDependencies/UD_Catalan-AnCora) #### Initial Data Collection and Normalization The original annotation was done in a constituency framework as a part of the [AnCora project](http://clic.ub.edu/corpus/) at the University of Barcelona. It was converted to dependencies by the [Universal Dependencies team](https://universaldependencies.org/) and used in the CoNLL 2009 shared task. The CoNLL 2009 version was later converted to HamleDT and to Universal Dependencies. For more information on the AnCora project, visit the [AnCora site](http://clic.ub.edu/corpus/). To learn about the Universal Dependences, visit the webpage [https://universaldependencies.org](https://universaldependencies.org) #### Who are the source language producers? For more information on the AnCora corpus and its sources, visit the [AnCora site](http://clic.ub.edu/corpus/). ### Annotations #### Annotation process For more information on the first AnCora annotation, visit the [AnCora site](http://clic.ub.edu/corpus/). #### Who are the annotators? For more information on the AnCora annotation team, visit the [AnCora site](http://clic.ub.edu/corpus/). ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset This dataset contributes to the development of language models in Catalan, a low-resource language. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators ### Licensing Information This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC Attribution 4.0 International License</a>. ### Citation Information The following paper must be cited when using this corpus: Taulé, M., M.A. Martí, M. Recasens (2008) 'Ancora: Multilevel Annotated Corpora for Catalan and Spanish', Proceedings of 6th International Conference on Language Resources and Evaluation. Marrakesh (Morocco). To cite the Universal Dependencies project: Rueter, J. (Creator), Erina, O. (Contributor), Klementeva, J. (Contributor), Ryabov, I. (Contributor), Tyers, F. M. (Contributor), Zeman, D. (Contributor), Nivre, J. (Creator) (15 Nov 2020). Universal Dependencies version 2.7 Erzya JR. Universal Dependencies Consortium.
projecte-aina/UD_Catalan-AnCora
[ "task_categories:token-classification", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "language:ca", "license:cc-by-4.0", "region:us" ]
2022-09-28T10:51:06+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": ["token-classification"], "task_ids": ["part-of-speech"], "pretty_name": "UD_Catalan-AnCora", "tags": []}
2023-11-25T06:31:40+00:00
8a5e23f6ffbd1b55efaf0ffe6322f985fe859bf2
# Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?", "targets": "Sí" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3). |Language|Kilobytes|%|Samples|%|Non-English prompts| |--------|------:|-:|---:|-:|-:| |tw|106288|0.11|265071|0.33| | |bm|107056|0.11|265180|0.33| | |ak|108096|0.11|265071|0.33| | |ca|110608|0.11|271191|0.34| | |eu|113008|0.12|281199|0.35| | |fon|113072|0.12|265063|0.33| | |st|114080|0.12|265063|0.33| | |ki|115040|0.12|265180|0.33| | |tum|116032|0.12|265063|0.33| | |wo|122560|0.13|365063|0.46| | |ln|126304|0.13|365060|0.46| | |as|156256|0.16|265063|0.33| | |or|161472|0.17|265063|0.33| | |kn|165456|0.17|265063|0.33| | |ml|175040|0.18|265864|0.33| | |rn|192992|0.2|318189|0.4| | |nso|229712|0.24|915051|1.14| | |tn|235536|0.24|915054|1.14| | |lg|235936|0.24|915021|1.14| | |rw|249360|0.26|915043|1.14| | |ts|250256|0.26|915044|1.14| | |sn|252496|0.26|865056|1.08| | |xh|254672|0.26|915058|1.14| | |zu|263712|0.27|915061|1.14| | |ny|272128|0.28|915063|1.14| | |ig|325440|0.33|950097|1.19|✅| |yo|339664|0.35|913021|1.14|✅| |ne|398144|0.41|315754|0.39|✅| |pa|529632|0.55|339210|0.42|✅| |sw|561392|0.58|1114439|1.39|✅| |gu|566576|0.58|347499|0.43|✅| |mr|674000|0.69|417269|0.52|✅| |bn|854864|0.88|428725|0.54|✅| |ta|943440|0.97|410633|0.51|✅| |te|1384016|1.42|573354|0.72|✅| |ur|1944416|2.0|855756|1.07|✅| |vi|3113184|3.2|1667306|2.08|✅| |code|4330752|4.46|2707724|3.38| | |hi|4469712|4.6|1543441|1.93|✅| |id|4538768|4.67|2582272|3.22|✅| |zh|4604112|4.74|3571636|4.46|✅| |ar|4703968|4.84|2148970|2.68|✅| |fr|5558912|5.72|5055942|6.31|✅| |pt|6130016|6.31|3562772|4.45|✅| |es|7579424|7.8|5151349|6.43|✅| |en|39252528|40.4|32740750|40.87| | |total|97150128|100.0|80100816|100.0|✅| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.
bigscience/xP3mt
[ "task_categories:other", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100M<n<1B", "language:ak", "language:ar", "language:as", "language:bm", "language:bn", "language:ca", "language:code", "language:en", "language:es", "language:eu", "language:fon", "language:fr", "language:gu", "language:hi", "language:id", "language:ig", "language:ki", "language:kn", "language:lg", "language:ln", "language:ml", "language:mr", "language:ne", "language:nso", "language:ny", "language:or", "language:pa", "language:pt", "language:rn", "language:rw", "language:sn", "language:st", "language:sw", "language:ta", "language:te", "language:tn", "language:ts", "language:tum", "language:tw", "language:ur", "language:vi", "language:wo", "language:xh", "language:yo", "language:zh", "language:zu", "license:apache-2.0", "arxiv:2211.01786", "region:us" ]
2022-09-28T11:36:00+00:00
{"annotations_creators": ["expert-generated", "crowdsourced"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zu"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": ["100M<n<1B"], "task_categories": ["other"], "pretty_name": "xP3", "programming_language": ["C", "C++", "C#", "Go", "Java", "JavaScript", "Lua", "PHP", "Python", "Ruby", "Rust", "Scala", "TypeScript"]}
2023-05-30T14:50:57+00:00
cf5adcceef86da1cf28e72987026fcabb357a54b
Nielser/minithresh
[ "license:afl-3.0", "region:us" ]
2022-09-28T12:25:52+00:00
{"license": "afl-3.0"}
2022-09-28T12:37:05+00:00
8b08f37958afaaf8b6afec45f6aa348167ea777f
sasha/stablediffusionbias
[ "license:cc-by-nc-4.0", "region:us" ]
2022-09-28T12:33:54+00:00
{"license": "cc-by-nc-4.0"}
2022-09-28T12:33:54+00:00
c5c81300c6eed75b0c2fba9e702ec21039d9a961
ankitkupadhyay/XNLI
[ "license:apache-2.0", "region:us" ]
2022-09-28T12:47:21+00:00
{"license": "apache-2.0"}
2022-09-28T18:27:00+00:00
dda37a4cbf1f2cee6d752d6bc501f03c53d90317
OMGSAMUELRBR/Test47236
[ "license:gpl-3.0", "region:us" ]
2022-09-28T14:08:59+00:00
{"license": "gpl-3.0"}
2022-09-28T14:08:59+00:00
097422ac9004c632e11f3a0dcd52fca53226f85d
NobuLuis/zeein
[ "license:other", "region:us" ]
2022-09-28T14:18:04+00:00
{"license": "other"}
2022-09-28T14:21:04+00:00
d03aafe26e3255f043e10bdf4d1d098c9f0707d1
eround/MyFace
[ "region:us" ]
2022-09-28T14:21:35+00:00
{}
2022-09-28T22:24:40+00:00
ee9293bbaae6d3604d2774b49e2cc93aaa10f585
macfarrut/macfarrut
[ "license:openrail", "region:us" ]
2022-09-28T14:23:47+00:00
{"license": "openrail"}
2022-09-28T14:29:14+00:00
67141dfcd78fdce1b716624fe853988f3997b3de
MrContext/DREAMCONTEXT
[ "region:us" ]
2022-09-28T14:35:34+00:00
{}
2022-09-28T14:54:13+00:00
38849a0521e548dd30f944f0e09f1799edf90415
semiller206/semiller206
[ "license:openrail", "region:us" ]
2022-09-28T14:47:30+00:00
{"license": "openrail"}
2022-09-30T19:01:06+00:00
cc06d31cd266a978219b212ba00e72eb0ad14d4c
a
CANUTO/images
[ "region:us" ]
2022-09-28T14:54:45+00:00
{}
2022-09-28T15:00:43+00:00
da95ff05f257074ed9be9c5706d9570e2f9ae7c2
fersebas/Fer
[ "region:us" ]
2022-09-28T15:00:06+00:00
{}
2022-10-05T18:10:55+00:00
4e531582d091467f2f3c4de4e530d0f9733314b5
MrProcastinador/CHOLO
[ "region:us" ]
2022-09-28T15:07:21+00:00
{}
2022-09-28T15:07:58+00:00
2729379a3f4648fdee939b5e501e3dc2789d27e5
khalidx199/k199
[ "license:apache-2.0", "region:us" ]
2022-09-28T15:47:43+00:00
{"license": "apache-2.0"}
2022-09-28T15:49:21+00:00
e85d8a286079ca576ea7d8820dfd0f20f57dbef5
almost/test
[ "license:afl-3.0", "region:us" ]
2022-09-28T15:51:34+00:00
{"license": "afl-3.0"}
2022-09-28T15:51:34+00:00
74c2e9f15ecd969d74ae3f82749c26d10268190a
PCScreen/Thomaz_Junior
[ "license:unknown", "region:us" ]
2022-09-28T15:54:08+00:00
{"license": "unknown"}
2022-09-28T15:57:51+00:00
e38cf8f0d16cdefbe65415f8173812f68b24108f
kashif/tourism-monthly-batch
[ "license:cc", "region:us" ]
2022-09-28T16:08:10+00:00
{"license": "cc"}
2022-09-28T16:29:04+00:00
ed89518500ea14c7cf8208d1e82f16bf5abdd07c
alx-ai/noggles_inversion
[ "license:cc0-1.0", "region:us" ]
2022-09-28T16:28:06+00:00
{"license": "cc0-1.0"}
2022-09-28T16:30:23+00:00
d0a11f31e2c40f1da8060c3377289514669606d6
marcosfevre/images
[ "license:cc-by-4.0", "region:us" ]
2022-09-28T16:59:45+00:00
{"license": "cc-by-4.0"}
2022-09-28T18:42:07+00:00
d965544df7c29b63d21cd188684998673e726467
CarlosMachucaFotografia/Imagenesmias
[ "region:us" ]
2022-09-28T17:26:34+00:00
{}
2022-09-28T17:38:45+00:00
9a76277bcbb403d82f84201035723d3d7bd600c7
JosephEudave/Stabledifussion-dreambooth
[ "license:other", "region:us" ]
2022-09-28T17:34:33+00:00
{"license": "other"}
2022-09-28T18:21:08+00:00
42b703eeb2f8b004158d0cb88752aaeca90eb439
jurer/farias
[ "license:cc-by-4.0", "region:us" ]
2022-09-28T17:41:45+00:00
{"license": "cc-by-4.0"}
2022-09-28T17:51:07+00:00
e91596d78fb16f41a5b993e2db7d4345bca01d77
#Training IA Model Here are the images that i used to train an a SD model with "tiomonkey" concept
EltioMonkey/MonkeyTrain
[ "region:us" ]
2022-09-28T17:52:43+00:00
{}
2022-09-29T16:44:43+00:00
d23b094346c5dbda1080a74bb2a24c18adbf7409
# Dataset Card for MultiPL-E ## Dataset Description - **Homepage:** https://nuprl.github.io/MultiPL-E/ - **Repository:** https://github.com/nuprl/MultiPL-E - **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177 - **Point of Contact:** [email protected], [email protected], [email protected] ## Dataset Summary MultiPL-E is a dataset for evaluating large language models for code generation that supports 18 programming languages. It takes the OpenAI "HumanEval" and the MBPP Python benchmarks and uses little compilers to translate them to other languages. It is easy to add support for new languages and benchmarks. ## Subsets For most purposes, you should use the variations called *SRCDATA-LANG*, where *SRCDATA* is either "humaneval" or "mbpp" and *LANG* is one of the supported languages. We use the canonical file extension for each language to identify the language, e.g., "py" for Python, "cpp" for C++, "lua" for Lua, and so on. We also provide a few other variations: - *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt is totally unchanged. If the original prompt had Python doctests, they remain as Python instead of being translated to *LANG*. If the original prompt had Python-specific terminology, e.g., "list", it remains "list", instead of being translated, e.g., to "vector" for C++. - *SRCDATA-LANG-transform* transforms the doctests to *LANG* but leaves the natural language text of the prompt unchanged. - *SRCDATA-LANG-removed* removes the doctests from the prompt. Note that MBPP does not have any doctests, so the "removed" and "transform" variations are not available for MBPP. ## Example The following script uses the Salesforce/codegen model to generate Lua and MultiPL-E to produce a script with unit tests for luaunit. ```python import datasets from transformers import AutoTokenizer, AutoModelForCausalLM LANG = "lua" MODEL_NAME = "Salesforce/codegen-350M-multi" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda() problems = datasets.load_dataset("nuprl/MultiPL-E", f"humaneval-{LANG}") def stop_at_stop_token(decoded_string, problem): """ Truncates the output at stop tokens, taking care to skip the prompt which may have stop tokens. """ min_stop_index = len(decoded_string) for stop_token in problem["stop_tokens"]: stop_index = decoded_string.find(stop_token) if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index: min_stop_index = stop_index return decoded_string[:min_stop_index] for problem in problems["test"]: input_ids = tokenizer( problem["prompt"], return_tensors="pt", ).input_ids.cuda() generated_ids = model.generate( input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2 ) truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem) filename = problem["name"] + "." + LANG with open(filename, "w") as f: print(f"Created {filename}") f.write(truncated_string) f.write("\n") f.write(problem["tests"]) ```
nuprl/MultiPL-E
[ "annotations_creators:machine-generated", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "source_datasets:extended|openai_humaneval", "source_datasets:extended|mbpp", "language:en", "license:mit", "region:us" ]
2022-09-28T18:20:07+00:00
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2023-06-15T23:08:57+00:00
34326d1ee26cafea5e2ac83b0f3b5308de2077c0
bastiankase/dianakreuz
[ "license:openrail", "region:us" ]
2022-09-28T18:38:10+00:00
{"license": "openrail"}
2022-09-29T17:07:05+00:00
53f065e69993fb412774efb69e933fec782970e4
LuisPerezT/Fotos
[ "license:openrail", "region:us" ]
2022-09-28T18:42:55+00:00
{"license": "openrail"}
2022-09-28T20:27:29+00:00
cda2e3de3397cb59cb0eed606c2179e780e66663
Grim421/testing
[ "license:afl-3.0", "region:us" ]
2022-09-28T18:51:20+00:00
{"license": "afl-3.0"}
2022-09-28T18:51:56+00:00
5c9e80ea311d9ab56264265b77ed06a1d32bcef0
# Cannabis Licenses <!-- FIXME: <div align="center" style="text-align:center; margin-top:1rem; margin-bottom: 1rem;"> <img style="max-height:365px;width:100%;max-width:720px;" alt="" src="analysis/figures/cannabis-licenses-map.png"> </div> --> ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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) - [Data Collection and Normalization](#data-collection-and-normalization) - [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) - [License](#license) - [Citation](#citation) - [Contributions](#contributions) ## Dataset Description - **Homepage:** <https://github.com/cannlytics/cannlytics> - **Repository:** <https://huggingface.co/datasets/cannlytics/cannabis_licenses> - **Point of Contact:** <[email protected]> ### Dataset Summary **Cannabis Licenses** is a collection of cannabis license data for each state with permitted adult-use cannabis. The dataset also includes a sub-dataset, `all`, that includes all licenses. ## Dataset Structure The dataset is partitioned into 18 subsets for each state and the aggregate. | State | Code | Status | |-------|------|--------| | [All](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/all) | `all` | ✅ | | [Alaska](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ak) | `ak` | ✅ | | [Arizona](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/az) | `az` | ✅ | | [California](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ca) | `ca` | ✅ | | [Colorado](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/co) | `co` | ✅ | | [Connecticut](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ct) | `ct` | ✅ | | [Delaware](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/de) | `md` | ✅ | | [Illinois](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/il) | `il` | ✅ | | [Maine](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/me) | `me` | ✅ | | [Maryland](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/md) | `md` | ✅ | | [Massachusetts](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ma) | `ma` | ✅ | | [Michigan](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mi) | `mi` | ✅ | | [Missouri](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mo) | `mo` | ✅ | | [Montana](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mt) | `mt` | ✅ | | [Nevada](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nv) | `nv` | ✅ | | [New Jersey](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nj) | `nj` | ✅ | | [New Mexico](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nm) | `nm` | ✅ | | [New York](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ny) | `ny` | ✅ | | [Oregon](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/or) | `or` | ✅ | | [Rhode Island](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ri) | `ri` | ✅ | | [Vermont](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/vt) | `vt` | ✅ | | Virginia | `va` | ⏳ Expected 2024 | | [Washington](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/wa) | `wa` | ✅ | The following states have issued medical cannabis licenses, but are not (yet) included in the dataset: - Alabama - Arkansas - District of Columbia (D.C.) - Florida - Kentucky (2024) - Louisiana - Minnesota - Mississippi - New Hampshire - North Dakota - Ohio - Oklahoma - Pennsylvania - South Dakota - Utah - West Virginia ### Data Instances You can load the licenses for each state. For example: ```py from datasets import load_dataset # Get the licenses for a specific state. dataset = load_dataset('cannlytics/cannabis_licenses', 'all') data = dataset['data'] ``` ### Data Fields Below is a non-exhaustive list of fields, used to standardize the various data that are encountered, that you may expect to find for each observation. | Field | Example | Description | |-------|-----|-------------| | `id` | `"1046"` | A state-unique ID for the license. | | `license_number` | `"C10-0000423-LIC"` | A unique license number. | | `license_status` | `"Active"` | The status of the license. Only licenses that are active are included. | | `license_status_date` | `"2022-04-20T00:00"` | The date the status was assigned, an ISO-formatted date if present. | | `license_term` | `"Provisional"` | The term for the license. | | `license_type` | `"Commercial - Retailer"` | The type of business license. | | `license_designation` | `"Adult-Use and Medicinal"` | A state-specific classification for the license. | | `issue_date` | `"2019-07-15T00:00:00"` | An issue date for the license, an ISO-formatted date if present. | | `expiration_date` | `"2023-07-14T00:00:00"` | An expiration date for the license, an ISO-formatted date if present. | | `licensing_authority_id` | `"BCC"` | A unique ID for the state licensing authority. | | `licensing_authority` | `"Bureau of Cannabis Control (BCC)"` | The state licensing authority. | | `business_legal_name` | `"Movocan"` | The legal name of the business that owns the license. | | `business_dba_name` | `"Movocan"` | The name the license is doing business as. | | `business_owner_name` | `"redacted"` | The name of the owner of the license. | | `business_structure` | `"Corporation"` | The structure of the business that owns the license. | | `activity` | `"Pending Inspection"` | Any relevant license activity. | | `premise_street_address` | `"1632 Gateway Rd"` | The street address of the business. | | `premise_city` | `"Calexico"` | The city of the business. | | `premise_state` | `"CA"` | The state abbreviation of the business. | | `premise_county` | `"Imperial"` | The county of the business. | | `premise_zip_code` | `"92231"` | The zip code of the business. | | `business_email` | `"[email protected]"` | The business email of the license. | | `business_phone` | `"(555) 555-5555"` | The business phone of the license. | | `business_website` | `"cannlytics.com"` | The business website of the license. | | `parcel_number` | `"A42"` | An ID for the business location. | | `premise_latitude` | `32.69035693` | The latitude of the business. | | `premise_longitude` | `-115.38987552` | The longitude of the business. | | `data_refreshed_date` | `"2022-09-21T12:16:33.3866667"` | An ISO-formatted time when the license data was updated. | ### Data Splits The data is split into subsets by state. You can retrieve all licenses by requesting the `all` subset. ```py from datasets import load_dataset # Get all cannabis licenses. dataset = load_dataset('cannlytics/cannabis_licenses', 'all') data = dataset['data'] ``` ## Dataset Creation ### Curation Rationale Data about organizations operating in the cannabis industry for each state is valuable for research. ### Source Data | State | Data Source URL | |-------|-----------------| | Alaska | <https://www.commerce.alaska.gov/abc/marijuana/Home/licensesearch> | | Arizona | <https://azcarecheck.azdhs.gov/s/?licenseType=null> | | California | <https://search.cannabis.ca.gov/> | | Colorado | <https://sbg.colorado.gov/med/licensed-facilities> | | Connecticut | <https://portal.ct.gov/DCP/Medical-Marijuana-Program/Connecticut-Medical-Marijuana-Dispensary-Facilities> | | Delaware | <https://dhss.delaware.gov/dhss/dph/hsp/medmarcc.html> | | Illinois | <https://www.idfpr.com/LicenseLookup/AdultUseDispensaries.pdf> | | Maine | <https://www.maine.gov/dafs/ocp/open-data/adult-use> | | Maryland | <https://mmcc.maryland.gov/Pages/Dispensaries.aspx> | | Massachusetts | <https://masscannabiscontrol.com/open-data/data-catalog/> | | Michigan | <https://michigan.maps.arcgis.com/apps/webappviewer/index.html?id=cd5a1a76daaf470b823a382691c0ff60> | | Missouri | <https://health.mo.gov/safety/cannabis/licensed-facilities.php> | | Montana | <https://mtrevenue.gov/cannabis/#CannabisLicenses> | | Nevada | <https://ccb.nv.gov/list-of-licensees/> | | New Jersey | <https://data.nj.gov/stories/s/ggm4-mprw> | | New Mexico | <https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US> | | New York | <https://cannabis.ny.gov/licensing> | | Oregon | <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx> | | Rhode Island | <https://dbr.ri.gov/office-cannabis-regulation/compassion-centers/licensed-compassion-centers> | | Vermont | <https://ccb.vermont.gov/licenses> | | Washington | <https://lcb.wa.gov/records/frequently-requested-lists> | ### Data Collection and Normalization In the `algorithms` directory, you can find the algorithms used for data collection. You can use these algorithms to recreate the dataset. First, you will need to clone the repository: ``` git clone https://huggingface.co/datasets/cannlytics/cannabis_licenses ``` You can then install the algorithm Python (3.9+) requirements: ``` cd cannabis_licenses pip install -r requirements.txt ``` Then you can run all of the data-collection algorithms: ``` python algorithms/main.py ``` Or you can run each algorithm individually. For example: ``` python algorithms/get_licenses_ny.py ``` ### Personal and Sensitive Information This dataset includes names of individuals, public addresses, and contact information for cannabis licensees. It is important to take care to use these data points in a legal manner. ## Considerations for Using the Data ### Social Impact of Dataset Arguably, there is substantial social impact that could result from the study of permitted adult-use cannabis, therefore, researchers and data consumers alike should take the utmost care in the use of this dataset. ### Discussion of Biases Cannlytics is a for-profit data and analytics company that primarily serves cannabis businesses. The data are not randomly collected and thus sampling bias should be taken into consideration. ### Other Known Limitations The data is for adult-use cannabis licenses. It would be valuable to include medical cannabis licenses too. ## Additional Information ### Dataset Curators Curated by [🔥Cannlytics](https://cannlytics.com)<br> <[email protected]> ### License ``` Copyright (c) 2022-2023 Cannlytics and the Cannabis Data Science Team The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International license. You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party. ``` ### Citation Please cite the following if you use the code examples in your research: ```bibtex @misc{cannlytics2023, title={Cannabis Data Science}, author={Skeate, Keegan and O'Sullivan-Sutherland, Candace}, journal={https://github.com/cannlytics/cannabis-data-science}, year={2023} } ``` ### Contributions Thanks to [🔥Cannlytics](https://cannlytics.com), [@candy-o](https://github.com/candy-o), [@hcadeaux](https://huggingface.co/hcadeaux), [@keeganskeate](https://github.com/keeganskeate), and the entire [Cannabis Data Science Team](https://meetup.com/cannabis-data-science/members) for their contributions.
cannlytics/cannabis_licenses
[ "annotations_creators:expert-generated", "language_creators:expert-generated", "license:cc-by-4.0", "cannabis", "licenses", "region:us" ]
2022-09-28T18:52:23+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "license": ["cc-by-4.0"], "pretty_name": "cannabis_licenses", "tags": ["cannabis", "licenses"]}
2023-09-30T13:23:05+00:00
3562204543b81d961ccef05e11e3d69011fe5104
# ****Dataset Card for tathagata**** # **I-Dataset Summary** tathagata.txt is a dataset based on summaries of major Buddhist, Hindu and Advaita texts such as: - Diamond Sutra - Lankavatara Sutra - Sri Nisargadatta Maharaj quotes - Quotes from the Bhagavad Gita This dataset was used to train this model https://huggingface.co/radm/rugpt3medium-tathagata # **II-Languages** The texts in the dataset are in Russian (ru).
radm/tathagata
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:ru", "license:apache-2.0", "text_generation", "quotes", "region:us" ]
2022-09-28T18:55:18+00:00
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ru"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "tathagata", "tags": ["text_generation", "quotes"]}
2022-09-28T19:20:13+00:00
bc637e0366cdba0bf5cd9542b4cb6ed819d925b7
valluvera/gemma
[ "license:other", "region:us" ]
2022-09-28T19:01:58+00:00
{"license": "other"}
2022-09-28T19:12:34+00:00
9d61249c9d960863eeefff485280129c7c0b1e44
bjornsing/PCG-signals
[ "license:cc-by-4.0", "region:us" ]
2022-09-28T19:41:44+00:00
{"license": "cc-by-4.0"}
2022-09-28T19:44:06+00:00
c10a50d07a444af455999711419682ae9d6dba15
thewalkerdenton/Canny
[ "license:apache-2.0", "region:us" ]
2022-09-28T19:57:32+00:00
{"license": "apache-2.0"}
2022-09-28T20:02:20+00:00
57e5044606ea180cd495a3c301c25a19fde3d7ff
rousses/imagine
[ "license:other", "region:us" ]
2022-09-28T20:46:38+00:00
{"license": "other"}
2022-09-28T21:16:15+00:00
cc27350c690c3bf84e52554a42e7e6af62d917c3
Franmg/Fotos
[ "region:us" ]
2022-09-28T21:32:24+00:00
{}
2022-09-28T21:37:01+00:00
1786207ffebfbe62211179fccbd4d0566ace37a9
This textual inversion has been trained on WaifuDiffusion v1.2 (`[45dee52b]`). This will probably not work well with the standard Stable Diffusion model. # How to use (with webui) - create `embeddings` folder in the root directory of the webui - paste the .bin in there **keyword: `<marine>`**
cattoroboto/waifudiffusion-marine-textual-inversion
[ "region:us" ]
2022-09-28T22:30:57+00:00
{}
2022-09-28T23:06:45+00:00
048a873dc8ee97644ef250ff3e5fdec23e635a68
AmliArt/face
[ "license:unknown", "region:us" ]
2022-09-28T22:42:04+00:00
{"license": "unknown"}
2022-09-28T22:55:28+00:00
774d821c1bb64c62c0eef7204ff19776946d9892
Jonnyck/myself
[ "license:other", "region:us" ]
2022-09-28T22:46:41+00:00
{"license": "other"}
2022-09-28T23:14:28+00:00
ff362105035ab3d6251d4fd0dbb65bb826d3e357
Limbicnation/pixelart
[ "license:artistic-2.0", "region:us" ]
2022-09-28T23:03:03+00:00
{"license": "artistic-2.0"}
2022-09-28T23:03:03+00:00
6fd8ede7dbde80c793cf5a335a3f5ccf431f9890
JorgeAcevedx/portrait
[ "license:afl-3.0", "region:us" ]
2022-09-28T23:17:43+00:00
{"license": "afl-3.0"}
2022-09-28T23:17:43+00:00
0c52d74f1f27559051c13c40bcbdc0ea22e5dac9
Pitagorak/Yo
[ "license:other", "region:us" ]
2022-09-29T00:03:00+00:00
{"license": "other"}
2022-10-01T03:21:10+00:00
337ec38c58a30812c0944d807f5acdc1f86f4bc3
# Info > This is a repository for anime regularization. If you wish to contribute to the dataset, contact me at naotsue#9786. I will add them to the dataset and update it. # Criteria > 512x512 > No excessive deformations > Vaguely resembles an anime artstyle # Contribution Leaderboard > 1. bWm_nubby: 5838 images > 2. naotsue: 888 images ![Sak](https://i0.wp.com/100wordanime.blog/wp-content/uploads/2019/04/anime-thank-you.jpg?resize=387%2C400&ssl=1)
waifu-research-department/regularization
[ "license:mit", "region:us" ]
2022-09-29T01:09:44+00:00
{"license": "mit"}
2022-09-29T21:00:10+00:00
1b3d125486d7fa6f77402af5339516a157984177
vfx/dh
[ "region:us" ]
2022-09-29T01:25:08+00:00
{}
2022-09-29T02:03:14+00:00
501e676071e2bde888b80b52227f0aedc4f82d81
Brayant115/yo
[ "license:apache-2.0", "region:us" ]
2022-09-29T01:47:35+00:00
{"license": "apache-2.0"}
2022-09-29T01:47:35+00:00
f2c96e0553b980a0f6d6660dac79b7c8b2e8b0a7
Xitari/soyyo
[ "license:artistic-2.0", "region:us" ]
2022-09-29T02:20:05+00:00
{"license": "artistic-2.0"}
2022-09-29T02:49:31+00:00
0277cb91dfecc95f779b25bbd9223bc770b276e1
leizu/face1
[ "license:openrail", "region:us" ]
2022-09-29T03:33:47+00:00
{"license": "openrail"}
2022-09-29T03:33:47+00:00
b4adca9c6281d8076dcd2f1d30d83f991cdca1ec
sudapop/test
[ "license:afl-3.0", "region:us" ]
2022-09-29T03:49:58+00:00
{"license": "afl-3.0"}
2022-09-29T03:51:38+00:00
fb9b4efc3c14b039c5012ad7d7de29bca88e4a0b
ruffusplay/ajolote
[ "license:openrail", "region:us" ]
2022-09-29T04:27:47+00:00
{"license": "openrail"}
2022-09-29T04:27:47+00:00
3fbc3b7f095455dcbfa990c7cd9840bca953aceb
ruffusplay/ajolote2
[ "license:openrail", "region:us" ]
2022-09-29T04:30:34+00:00
{"license": "openrail"}
2022-09-29T04:30:34+00:00
d821a66d1ea7e1a1c3d0f41d2b214d53af651cde
ruffusplay/ajo
[ "license:c-uda", "region:us" ]
2022-09-29T04:31:46+00:00
{"license": "c-uda"}
2022-09-29T04:31:46+00:00
2066097f3c1e270598bdeb8376f45e4d55bfdeb3
Metalistenia/daniel
[ "license:openrail", "region:us" ]
2022-09-29T04:36:11+00:00
{"license": "openrail"}
2022-09-29T04:54:18+00:00
4d7946ef7f0c5ff5e261e384db8015dfe8e417cb
# Dataset Card for EurlexResources: A Corpus Covering the Largest EURLEX Resources ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/eurlex) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Joel Niklaus](mailto:[email protected]) ### Dataset Summary This dataset contains large text resources (~179GB in total) from EURLEX that can be used for pretraining language models. Use the dataset like this: ```python from datasets import load_dataset config = "de_caselaw" # {lang}_{resource} dataset = load_dataset("joelito/eurlex_resources", config, split='train', streaming=True) ``` ### Supported Tasks and Leaderboards The dataset supports the task of masked language modeling. ### Languages The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv ## Dataset Structure ### Data Instances The file format is jsonl.xz and there is one split available ("train"). The following resource types are supported: caselaw, decision, directive, intagr, proposal, recommendation, regulation More information about the resource types can be found here: - Caselaw: [EU](https://eur-lex.europa.eu/collection/eu-law/eu-case-law.html) - Decision: [EU](https://eur-lex.europa.eu/EN/legal-content/summary/european-union-decisions.html), [Wikipedia](https://en.wikipedia.org/wiki/Decision_(European_Union)) - Directive: [EU](https://european-union.europa.eu/institutions-law-budget/law/types-legislation_en), [Wikipedia](https://en.wikipedia.org/wiki/Directive_(European_Union)) - Recommendation: [EU](https://eur-lex.europa.eu/EN/legal-content/glossary/recommendation.html), [Wikipedia](https://en.wikipedia.org/wiki/Recommendation_(European_Union)) - Regulation: [EU](https://european-union.europa.eu/institutions-law-budget/law/types-legislation_en), [Wikipedia](https://en.wikipedia.org/wiki/Regulation_(European_Union)) - Intagr: [EU](https://eur-lex.europa.eu/collection/eu-law/inter-agree.html), [Wikipedia](https://en.wikipedia.org/wiki/Treaties_of_the_European_Union) - Proposal: No resource found | Source | Size (MB) | Words | Documents | Words/Document | |:-------------------|------------:|------------:|------------:|-----------------:| | all_all | 180668 | 12106556233 | 8306749 | 1457 | | all_caselaw | 34939 | 3413551598 | 2487794 | 1372 | | all_decision | 28519 | 1698585620 | 1267402 | 1340 | | all_directive | 4786 | 368577940 | 104187 | 3537 | | all_intagr | 11421 | 743271516 | 274485 | 2707 | | all_proposal | 26526 | 2087989530 | 702392 | 2972 | | all_recommendation | 1886 | 164979037 | 80277 | 2055 | | all_regulation | 72590 | 3629600992 | 3390212 | 1070 | | bg_all | 7819 | 398067053 | 348691 | 1141 | | bg_caselaw | 1588 | 109749174 | 104434 | 1050 | | bg_decision | 1248 | 58817972 | 54075 | 1087 | | bg_directive | 263 | 15731608 | 4388 | 3585 | | bg_intagr | 603 | 31292848 | 11581 | 2702 | | bg_proposal | 1083 | 60674956 | 29251 | 2074 | | bg_recommendation | 89 | 5588991 | 3321 | 1682 | | bg_regulation | 2943 | 116211504 | 141641 | 820 | | cs_all | 8360 | 471961631 | 449793 | 1049 | | cs_caselaw | 1163 | 110005022 | 104519 | 1052 | | cs_decision | 1102 | 58921128 | 54075 | 1089 | | cs_directive | 186 | 13951134 | 4388 | 3179 | | cs_intagr | 449 | 28106332 | 11581 | 2426 | | cs_proposal | 840 | 61838692 | 29252 | 2113 | | cs_recommendation | 64 | 5416549 | 3323 | 1630 | | cs_regulation | 4557 | 193722774 | 242655 | 798 | | da_all | 8932 | 671484862 | 332500 | 2019 | | da_caselaw | 1746 | 185589641 | 88234 | 2103 | | da_decision | 1356 | 89498535 | 54085 | 1654 | | da_directive | 207 | 17525792 | 4388 | 3994 | | da_intagr | 506 | 35596169 | 11582 | 3073 | | da_proposal | 1399 | 119759476 | 29257 | 4093 | | da_recommendation | 100 | 9463897 | 3352 | 2823 | | da_regulation | 3618 | 214051352 | 141602 | 1511 | | de_all | 9607 | 695512401 | 348290 | 1996 | | de_caselaw | 1930 | 193232441 | 104228 | 1853 | | de_decision | 1449 | 93688222 | 53980 | 1735 | | de_directive | 218 | 17337760 | 4385 | 3953 | | de_intagr | 531 | 36791153 | 11580 | 3177 | | de_proposal | 1556 | 126987454 | 29219 | 4346 | | de_recommendation | 109 | 9608034 | 3318 | 2895 | | de_regulation | 3813 | 217867337 | 141580 | 1538 | | el_all | 12469 | 696216541 | 349667 | 1991 | | el_caselaw | 2951 | 202027703 | 105138 | 1921 | | el_decision | 1823 | 94919886 | 54150 | 1752 | | el_directive | 321 | 19411959 | 4390 | 4421 | | el_intagr | 701 | 38965777 | 11584 | 3363 | | el_proposal | 2085 | 128005737 | 29290 | 4370 | | el_recommendation | 145 | 9344866 | 3357 | 2783 | | el_regulation | 4443 | 203540613 | 141758 | 1435 | | en_all | 9217 | 769465561 | 348641 | 2207 | | en_caselaw | 1846 | 222891827 | 104422 | 2134 | | en_decision | 1504 | 114626013 | 54054 | 2120 | | en_directive | 204 | 18860876 | 4388 | 4298 | | en_intagr | 499 | 39029843 | 11581 | 3370 | | en_proposal | 1538 | 140781768 | 29242 | 4814 | | en_recommendation | 97 | 10091809 | 3320 | 3039 | | en_regulation | 3530 | 223183425 | 141634 | 1575 | | es_all | 8588 | 725125274 | 348443 | 2081 | | es_caselaw | 1870 | 220621730 | 104312 | 2115 | | es_decision | 1334 | 98163499 | 54001 | 1817 | | es_directive | 221 | 21484479 | 4385 | 4899 | | es_intagr | 516 | 41841805 | 11581 | 3612 | | es_proposal | 1366 | 133674486 | 29224 | 4574 | | es_recommendation | 82 | 8864018 | 3319 | 2670 | | es_regulation | 3199 | 200475257 | 141621 | 1415 | | et_all | 6090 | 328068754 | 349615 | 938 | | et_caselaw | 1074 | 93096396 | 105111 | 885 | | et_decision | 1069 | 50752324 | 54159 | 937 | | et_directive | 177 | 11555930 | 4390 | 2632 | | et_intagr | 436 | 24018147 | 11584 | 2073 | | et_proposal | 810 | 51600852 | 29283 | 1762 | | et_recommendation | 61 | 4451369 | 3355 | 1326 | | et_regulation | 2464 | 92593736 | 141733 | 653 | | fi_all | 7346 | 404265224 | 349633 | 1156 | | fi_caselaw | 1596 | 126525296 | 105119 | 1203 | | fi_decision | 1227 | 59659475 | 54163 | 1101 | | fi_directive | 204 | 12766491 | 4389 | 2908 | | fi_intagr | 463 | 25392311 | 11584 | 2192 | | fi_proposal | 1075 | 69198401 | 29288 | 2362 | | fi_recommendation | 73 | 5070392 | 3356 | 1510 | | fi_regulation | 2707 | 105652858 | 141734 | 745 | | fr_all | 9937 | 828959218 | 348295 | 2380 | | fr_caselaw | 2158 | 246262666 | 104228 | 2362 | | fr_decision | 1473 | 108648744 | 53981 | 2012 | | fr_directive | 222 | 20308801 | 4385 | 4631 | | fr_intagr | 536 | 41986012 | 11580 | 3625 | | fr_proposal | 1592 | 149134298 | 29218 | 5104 | | fr_recommendation | 112 | 11510415 | 3318 | 3469 | | fr_regulation | 3845 | 251108282 | 141585 | 1773 | | ga_all | 1028 | 65030095 | 349778 | 185 | | ga_caselaw | 11 | 696305 | 105205 | 6 | | ga_decision | 87 | 4415457 | 54189 | 81 | | ga_directive | 18 | 1512027 | 4390 | 344 | | ga_intagr | 19 | 1820723 | 11586 | 157 | | ga_proposal | 289 | 26106889 | 29298 | 891 | | ga_recommendation | 10 | 902390 | 3361 | 268 | | ga_regulation | 594 | 29576304 | 141749 | 208 | | hr_all | 4594 | 258816068 | 348691 | 742 | | hr_caselaw | 617 | 62432734 | 104434 | 597 | | hr_decision | 596 | 31911903 | 54075 | 590 | | hr_directive | 156 | 10855913 | 4388 | 2474 | | hr_intagr | 450 | 24962086 | 11581 | 2155 | | hr_proposal | 552 | 33437815 | 29251 | 1143 | | hr_recommendation | 40 | 3612247 | 3321 | 1087 | | hr_regulation | 2183 | 91603370 | 141641 | 646 | | hu_all | 6653 | 375253894 | 349605 | 1073 | | hu_caselaw | 1278 | 110179375 | 105144 | 1047 | | hu_decision | 1147 | 57108172 | 54156 | 1054 | | hu_directive | 200 | 13568304 | 4389 | 3091 | | hu_intagr | 470 | 27258501 | 11586 | 2352 | | hu_proposal | 912 | 60882750 | 29291 | 2078 | | hu_recommendation | 70 | 5312868 | 3357 | 1582 | | hu_regulation | 2576 | 100943924 | 141682 | 712 | | it_all | 9586 | 768605772 | 333631 | 2303 | | it_caselaw | 1889 | 206117726 | 89560 | 2301 | | it_decision | 1445 | 102848859 | 53983 | 1905 | | it_directive | 217 | 19687773 | 4385 | 4489 | | it_intagr | 528 | 40134330 | 11580 | 3465 | | it_proposal | 1533 | 140713925 | 29218 | 4816 | | it_recommendation | 109 | 10923431 | 3318 | 3292 | | it_regulation | 3865 | 248179728 | 141587 | 1752 | | lt_all | 6400 | 364361783 | 200565 | 1816 | | lt_caselaw | 1137 | 101808706 | 105477 | 965 | | lt_decision | 1096 | 55850308 | 21990 | 2539 | | lt_directive | 185 | 13078983 | 3239 | 4037 | | lt_intagr | 452 | 27009631 | 7481 | 3610 | | lt_proposal | 850 | 58553579 | 29272 | 2000 | | lt_recommendation | 64 | 5121089 | 3363 | 1522 | | lt_regulation | 2617 | 102939487 | 29743 | 3460 | | lv_all | 6349 | 363239195 | 349919 | 1038 | | lv_caselaw | 1153 | 103456811 | 105242 | 983 | | lv_decision | 1103 | 55512944 | 54224 | 1023 | | lv_directive | 186 | 13023024 | 4392 | 2965 | | lv_intagr | 452 | 26693107 | 11630 | 2295 | | lv_proposal | 96 | 58176216 | 29298 | 1985 | | lv_recommendation | 64 | 5074494 | 3361 | 1509 | | lv_regulation | 2545 | 101302599 | 141772 | 714 | | mt_all | 6540 | 367834815 | 350292 | 1050 | | mt_caselaw | 1164 | 100423543 | 105479 | 952 | | mt_decision | 1109 | 55239141 | 54280 | 1017 | | mt_directive | 203 | 14355266 | 4392 | 3268 | | mt_intagr | 470 | 27701991 | 11675 | 2372 | | mt_proposal | 878 | 59749277 | 29274 | 2041 | | mt_recommendation | 65 | 5039600 | 3363 | 1498 | | mt_regulation | 2650 | 105325997 | 141829 | 742 | | nl_all | 9586 | 770312808 | 349407 | 2204 | | nl_caselaw | 1847 | 206271837 | 105005 | 1964 | | nl_decision | 1456 | 104060901 | 54152 | 1921 | | nl_directive | 217 | 19529361 | 4388 | 4450 | | nl_intagr | 529 | 40247634 | 11584 | 3474 | | nl_proposal | 1540 | 141258274 | 29279 | 4824 | | nl_recommendation | 111 | 11002405 | 3355 | 3279 | | nl_regulation | 3886 | 247942396 | 141644 | 1750 | | pl_all | 6677 | 406648795 | 350349 | 1160 | | pl_caselaw | 1231 | 115824759 | 105479 | 1098 | | pl_decision | 1125 | 60407576 | 54287 | 1112 | | pl_directive | 197 | 14672157 | 4392 | 3340 | | pl_intagr | 466 | 28543668 | 11680 | 2443 | | pl_proposal | 886 | 64728230 | 29317 | 2207 | | pl_recommendation | 68 | 5769893 | 3363 | 1715 | | pl_regulation | 2703 | 116702512 | 141831 | 822 | | pt_all | 8450 | 675152149 | 348449 | 1937 | | pt_caselaw | 1763 | 198084937 | 104312 | 1898 | | pt_decision | 1327 | 93278293 | 54007 | 1727 | | pt_directive | 217 | 19831549 | 4385 | 4522 | | pt_intagr | 504 | 37999753 | 11581 | 3281 | | pt_proposal | 1361 | 127461782 | 29224 | 4361 | | pt_recommendation | 81 | 8396661 | 3319 | 2529 | | pt_regulation | 3197 | 190099174 | 141621 | 1342 | | ro_all | 6315 | 415038571 | 350300 | 1184 | | ro_caselaw | 1110 | 114780999 | 105516 | 1087 | | ro_decision | 1047 | 59479553 | 54281 | 1095 | | ro_directive | 206 | 16101628 | 4392 | 3666 | | ro_intagr | 481 | 31497000 | 11675 | 2697 | | ro_proposal | 805 | 62130419 | 29274 | 2122 | | ro_recommendation | 63 | 5977913 | 3363 | 1777 | | ro_regulation | 2603 | 125071059 | 141799 | 882 | | sk_all | 6484 | 392235510 | 350570 | 1118 | | sk_caselaw | 1160 | 110125141 | 105608 | 1042 | | sk_decision | 1111 | 59576875 | 54349 | 1096 | | sk_directive | 188 | 14132755 | 4393 | 3217 | | sk_intagr | 458 | 28298155 | 11676 | 2423 | | sk_proposal | 859 | 63726047 | 29290 | 2175 | | sk_recommendation | 66 | 5654790 | 3364 | 1680 | | sk_regulation | 2642 | 110721747 | 141890 | 780 | | sl_all | 6222 | 394814289 | 350574 | 1126 | | sl_caselaw | 1071 | 111238184 | 105608 | 1053 | | sl_decision | 1075 | 59454906 | 54349 | 1093 | | sl_directive | 176 | 13908097 | 4393 | 3165 | | sl_intagr | 441 | 28239078 | 11676 | 2418 | | sl_proposal | 812 | 63391970 | 29290 | 2164 | | sl_recommendation | 62 | 5628775 | 3364 | 1673 | | sl_regulation | 2585 | 112953279 | 141894 | 796 | | sv_all | 7419 | 500085970 | 351051 | 1424 | | sv_caselaw | 1585 | 162108645 | 105980 | 1529 | | sv_decision | 1213 | 71744934 | 54357 | 1319 | | sv_directive | 195 | 15386273 | 4393 | 3502 | | sv_intagr | 463 | 29845462 | 11676 | 2556 | | sv_proposal | 1059 | 86016237 | 29292 | 2936 | | sv_recommendation | 79 | 7152141 | 3366 | 2124 | | sv_regulation | 2825 | 127832278 | 141987 | 900 | ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data has been downloaded using the R package [eurlex](https://cran.r-project.org/web/packages/eurlex/vignettes/eurlexpkg.html) between June and August 2022. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) [see also the legal notice](https://eur-lex.europa.eu/content/legal-notice/legal-notice.html) ### Citation Information [More Information Needed] ### Contributions Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
joelniklaus/eurlex_resources
[ "task_categories:fill-mask", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-4.0", "region:us" ]
2022-09-29T06:35:34+00:00
{"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "EurlexResources: A Corpus Covering the Largest EURLEX Resources"}
2023-05-10T07:04:28+00:00
a9873510cff4ae717264cf96e403b4ac71548080
pablohorch/miFaceHorch
[ "region:us" ]
2022-09-29T06:42:27+00:00
{}
2022-09-29T06:42:48+00:00
b5776b60b9d42f79b41260579d0e7d3420b045ee
Algp123/seansimon
[ "license:cc", "region:us" ]
2022-09-29T07:04:40+00:00
{"license": "cc"}
2022-09-29T07:06:44+00:00
e5f041fc5d507821b395ff746d57f97818bd8db1
# Dataset Card for Weakly supervised AG News Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The Weakly supervised AG News Dataset was created by Team 44 of FSDL 2022 course with the only purpose of experimenting with weak supervision techniques. It was assumed that only the labels of the original test set and 20% of the training set were available. The labels in the training set were obtained by creating weak labels with LFs and denoising them with Snorkel's label model. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields text: a string feature label: a classification label, with possible values including World (0), Sports (1), Business (2), Sci/Tech (3). ### Data Splits - Training set with probabilistic labels from weak supervision: 37340 - Unlabeled data: 58660 - Validation set: 24000 - Test set: 7600 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to Xiang Zhang ([email protected]) for adding this dataset to the HF Dataset Hub.
bergr7/weakly_supervised_ag_news
[ "task_categories:text-classification", "task_ids:multi-class-classification", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|ag_news", "language:en", "region:us" ]
2022-09-29T07:43:34+00:00
{"annotations_creators": [], "language_creators": ["other"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|ag_news"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "Weakly supervised AG News Dataset", "tags": []}
2022-10-06T11:51:52+00:00
f6323032886e971c842c7b0b5b9f3592e6e2bd0a
Ces images de nuages sont divisées en 2 classes, les cirrus et les cumulus. These cloud images are divided into 2 classes, cirrus and cumulus.
Doudou69/Cloud_Recognition
[ "region:us" ]
2022-09-29T08:48:44+00:00
{}
2022-09-29T09:19:04+00:00
e6fb52c53dc1e653addb69adfa0113d171f221ab
Fhantomchaos/testing
[ "license:afl-3.0", "region:us" ]
2022-09-29T08:52:08+00:00
{"license": "afl-3.0"}
2022-09-29T08:53:27+00:00
aa6c355c4ac69c8e28fe1db0a5b5c194839328aa
liuweihug/da
[ "license:openrail", "region:us" ]
2022-09-29T08:56:08+00:00
{"license": "openrail"}
2022-09-29T08:56:08+00:00
402ac2dfe0a7d2e2353f93ef0fde8e40f59a21fa
HansHansHansHans/me
[ "license:unlicense", "region:us" ]
2022-09-29T09:13:43+00:00
{"license": "unlicense"}
2022-09-29T09:54:38+00:00
81b731b90a2a11229c78e6791d0d8c1ccf6833d4
# Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
merkalo-ziri/vsosh2022
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:ru", "license:other", "region:us" ]
2022-09-29T09:35:38+00:00
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ru"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "vsosh_dataset", "tags": []}
2022-09-29T10:02:34+00:00
e214dad7ae9dd678a2f01c9220d45d42c94c8f91
# Dataset Card for MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/mc4_legal) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Joel Niklaus](mailto:[email protected]) ### Dataset Summary This dataset contains large text resources (~133GB in total) from mc4 filtered for legal data that can be used for pretraining language models. Use the dataset like this: ```python from datasets import load_dataset dataset = load_dataset("joelito/mc4_legal", "de", split='train', streaming=True) ``` ### Supported Tasks and Leaderboards The dataset supports the task of masked language modeling. ### Languages The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv ## Dataset Structure ### Data Instances The file format is jsonl.xz and there is one split available ("train"). | Source | Size (MB) | Words | Documents | Words/Document | |:---------|------------:|------------:|------------:|-----------------:| | all | 448980 | 28599300521 | 9873288 | 2896 | | bg | 57 | 2390349 | 379 | 6306 | | cs | 31005 | 1840827375 | 677796 | 2715 | | da | 162 | 10466716 | 3231 | 3239 | | de | 105739 | 6184578784 | 3164461 | 1954 | | el | 30 | 1155977 | 307 | 3765 | | en | 13734 | 966539309 | 359283 | 2690 | | es | 132053 | 9058939804 | 2281888 | 3969 | | et | 2059 | 110198368 | 49987 | 2204 | | fi | 1270 | 62799074 | 44875 | 1399 | | fr | 30878 | 2117306229 | 598983 | 3534 | | ga | 1 | 32772 | 8 | 4096 | | hu | 4677 | 244911748 | 58857 | 4161 | | it | 46957 | 3053920779 | 990823 | 3082 | | lt | 156 | 9142223 | 1529 | 5979 | | lv | 1 | 58702 | 16 | 3668 | | mt | 65 | 3479869 | 731 | 4760 | | nl | 326 | 21962633 | 6875 | 3194 | | pl | 37950 | 2235839721 | 827641 | 2701 | | pt | 20120 | 1338147828 | 382173 | 3501 | | ro | 8816 | 551372510 | 136513 | 4038 | | sk | 5850 | 349265172 | 130701 | 2672 | | sl | 1742 | 107493024 | 32574 | 3299 | | sv | 5332 | 328471555 | 123657 | 2656 | ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation The dataset was created by filtering mc4 for legal data. We used terms indicating legal citations to get the texts. Note that this dataset can be quite noisy, and the quality is not known. ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
joelniklaus/mc4_legal
[ "task_categories:fill-mask", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-4.0", "region:us" ]
2022-09-29T09:53:01+00:00
{"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages"}
2023-03-20T23:24:13+00:00
d5ed1a1b69fc5d8f027273a4686fc3bff6c6c05f
INAI/svet
[ "region:us" ]
2022-09-29T11:18:27+00:00
{}
2022-09-29T11:36:43+00:00
d8c978c8b79d61393b9036a9bf09e76a83b39345
DannyHane/test
[ "region:us" ]
2022-09-29T12:28:38+00:00
{}
2022-09-29T12:43:52+00:00
f400ef054edf219b2529b673de34ff6c49f9ac9c
# Dataset Card for AISegment.cn - Matting Human datasets ## Table of Contents - [Dataset Card for AISegment.cn - Matting Human datasets](#dataset-card-for-aisegmentcn---matting-human-datasets) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Structure](#dataset-structure) - [Licensing Information](#licensing-information) ## Dataset Description Quoting the [dataset's github](https://github.com/aisegmentcn/matting_human_datasets) (translated by Apple Translator): > This dataset is currently the largest portrait matting dataset, containing 34,427 images and corresponding matting results. > The data set was marked by the high quality of Beijing Play Star Convergence Technology Co. Ltd., and the portrait soft segmentation model trained using this data set has been commercialized. > The original images in the dataset are from `Flickr`, `Baidu`, and `Taobao`. After face detection and area cropping, a half-length portrait of 600\*800 was generated. > The clip_img directory is a half-length portrait image in the format jpg; the matting directory is the corresponding matting file (convenient to confirm the matting quality), the format is png, you should first extract the alpha map from the png image before training. - **Repository:** [aisegmentcn/matting_human_datasets](https://github.com/aisegmentcn/matting_human_datasets) ## Dataset Structure ```text └── data/ ├── clip_img/ │ └── {group-id}/ │ └── clip_{subgroup-id}/ │ └── {group-id}-{img-id}.jpg └── matting/ └── {group-id}/ └── matting_{subgroup-id}/ └── {group-id}-{img-id}.png ``` The input `data/clip_img/1803151818/clip_00000000/1803151818-00000003.jpg` matches the label `data/matting/1803151818/matting_00000000/1803151818-00000003.png` ### Licensing Information See authors [Github](https://github.com/aisegmentcn/matting_human_datasets)
fredguth/aisegmentcn-matting-human
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "annotations_creators:Beijing Wanxing Convergence Technology Co", "size_categories:10K<n<100K", "license:mit", "binary", "aisegment.cn", "region:us" ]
2022-09-29T12:32:40+00:00
{"annotations_creators": ["Beijing Wanxing Convergence Technology Co"], "license": ["mit"], "size_categories": ["10K<n<100K"], "task_categories": ["image-segmentation"], "task_ids": ["semantic-segmentation"], "pretty_name": "aisegmentcn-matting-human", "tags": ["binary", "aisegment.cn"]}
2022-09-29T14:18:42+00:00
3293876da7c613c9e5c603411139d2c8933319e5
airnicco8/umls_sent_trans
[ "license:gpl-3.0", "region:us" ]
2022-09-29T13:04:52+00:00
{"license": "gpl-3.0"}
2022-09-29T13:04:52+00:00
a80bf0644d4149cbe69d2e57b0517c86975dd1fa
Gossher/GossherImages
[ "license:other", "region:us" ]
2022-09-29T13:35:18+00:00
{"license": "other"}
2022-09-29T13:51:25+00:00
d921ec7e349ce0d28daf30b2da9da5ee698bef0d
# Dataset Card for MIRACL Corpus ## Dataset Description * **Homepage:** http://miracl.ai * **Repository:** https://github.com/project-miracl/miracl * **Paper:** https://arxiv.org/abs/2210.09984 MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world. This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later. The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. ## Dataset Structure Each retrieval unit contains three fields: `docid`, `title`, and `text`. Consider an example from the English corpus: ``` { "docid": "39#0", "title": "Albedo", "text": "Albedo (meaning 'whiteness') is the measure of the diffuse reflection of solar radiation out of the total solar radiation received by an astronomical body (e.g. a planet like Earth). It is dimensionless and measured on a scale from 0 (corresponding to a black body that absorbs all incident radiation) to 1 (corresponding to a body that reflects all incident radiation)." } ``` The `docid` has the schema `X#Y`, where all passages with the same `X` come from the same Wikipedia article, whereas `Y` denotes the passage within that article, numbered sequentially. The text field contains the text of the passage. The title field contains the name of the article the passage comes from. The collection can be loaded using: ``` lang='ar' # or any of the 16 languages miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train'] for doc in miracl_corpus: docid = doc['docid'] title = doc['title'] text = doc['text'] ``` ## Dataset Statistics and Links The following table contains the number of passage and Wikipedia articles in the collection of each language, along with the links to the datasets and raw Wikipedia dumps. | Language | # of Passages | # of Articles | Links | Raw Wiki Dump | |:----------------|--------------:|--------------:|:------|:------| | Arabic (ar) | 2,061,414 | 656,982 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ar) | [🌏](https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2) | Bengali (bn) | 297,265 | 63,762 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-bn) | [🌏](https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2) | English (en) | 32,893,221 | 5,758,285 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-en) | [🌏](https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2) | Spanish (es) | 10,373,953 | 1,669,181 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-es) | [🌏](https://archive.org/download/eswiki-20220301/eswiki-20220301-pages-articles-multistream.xml.bz2) | Persian (fa) | 2,207,172 | 857,827 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fa) | [🌏](https://archive.org/download/fawiki-20220301/fawiki-20220301-pages-articles-multistream.xml.bz2) | Finnish (fi) | 1,883,509 | 447,815 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fi) | [🌏](https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2) | French (fr) | 14,636,953 | 2,325,608 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fr) | [🌏](https://archive.org/download/frwiki-20220301/frwiki-20220301-pages-articles-multistream.xml.bz2) | Hindi (hi) | 506,264 | 148,107 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-hi) | [🌏](https://archive.org/download/hiwiki-20220301/hiwiki-20220301-pages-articles-multistream.xml.bz2) | Indonesian (id) | 1,446,315 | 446,330 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-id) | [🌏](https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2) | Japanese (ja) | 6,953,614 | 1,133,444 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ja) | [🌏](https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2) | Korean (ko) | 1,486,752 | 437,373 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ko) | [🌏](https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2) | Russian (ru) | 9,543,918 | 1,476,045 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ru) | [🌏](https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2) | Swahili (sw) | 131,924 | 47,793 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-sw) | [🌏](https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2) | Telugu (te) | 518,079 | 66,353 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-te) | [🌏](https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2) | Thai (th) | 542,166 | 128,179 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-th) | [🌏](https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2) | Chinese (zh) | 4,934,368 | 1,246,389 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-zh) | [🌏](https://archive.org/download/zhwiki-20220301/zhwiki-20220301-pages-articles-multistream.xml.bz2)
miracl/miracl-corpus
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:expert-generated", "multilinguality:multilingual", "language:ar", "language:bn", "language:en", "language:es", "language:fa", "language:fi", "language:fr", "language:hi", "language:id", "language:ja", "language:ko", "language:ru", "language:sw", "language:te", "language:th", "language:zh", "license:apache-2.0", "arxiv:2210.09984", "region:us" ]
2022-09-29T13:49:58+00:00
{"annotations_creators": ["expert-generated"], "language": ["ar", "bn", "en", "es", "fa", "fi", "fr", "hi", "id", "ja", "ko", "ru", "sw", "te", "th", "zh"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "MIRACL-corpus", "tags": []}
2023-01-05T17:28:26+00:00
59ced5f474e574d107b1b669e745b047f33d2947
riogerz/florz
[ "license:openrail", "region:us" ]
2022-09-29T13:54:13+00:00
{"license": "openrail"}
2022-09-29T13:54:13+00:00
aa4f6645451098df234769f89af1fcccd16d567f
--- license: othera
Shinadayu/test
[ "region:us" ]
2022-09-29T14:19:40+00:00
{}
2022-09-29T14:21:16+00:00
6eb9f5c5ce5375d1620a1809cd1d0490d5318342
KamiNoGi/pochi
[ "license:openrail", "region:us" ]
2022-09-29T14:29:52+00:00
{"license": "openrail"}
2022-09-29T14:39:50+00:00