Split (1)

train · 7.36k rows

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SantiCalde	null	null	null	false	1	false	SantiCalde/santi	2022-09-30T22:57:28.000Z	null	false	88bec961bc2ca02d0a760243e6e95552ebc4402d	[]	[ "license:unknown" ]	https://huggingface.co/datasets/SantiCalde/santi/resolve/main/README.md	--- license: unknown ---
PCScreen	null	null	null	false	1	false	PCScreen/ThomazJunior1	2022-09-30T22:07:02.000Z	null	false	80768cc6b5ab2f7f6f31de1e0cb92c069fc90f34	[]	[ "license:unknown" ]	https://huggingface.co/datasets/PCScreen/ThomazJunior1/resolve/main/README.md	--- license: unknown ---
lmqg	null	null	null	false	2	false	lmqg/qg_frquad_dummy	2022-11-05T03:05:12.000Z	null	false	c797997d442273e284644de093e2e4ff9419632a	[]	[ "arxiv:2210.03992", "language:fr", "license:cc-by-4.0", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:fquad", "task_categories:text2text-generation", "tags:question-generation" ]	https://huggingface.co/datasets/lmqg/qg_frquad_dummy/resolve/main/README.md	--- language: fr license: cc-by-4.0 multilinguality: monolingual size_categories: 10K<n<100K source_datasets: fquad task_categories: - text2text-generation task_ids: [] pretty_name: FQuAD for question generation tags: - question-generation --- # Dataset Card for "lmqg/qg_frquad" *IMPORTANT: This is a dummy dataset for [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad). The original FRQuAD requires to fill a form (https://fquad.illuin.tech/) to get the data, and our lmqg/qg_frquad follows FQuAD's license. If you need lmqg/qg_frquad, please first request the access to FQuAD on their website https://fquad.illuin.tech/ . Once you obtain the access, we will add you to our lmqg group so that you can access https://huggingface.co/datasets/lmqg/qg_frquad. Leave a comment to the [discussion page](https://huggingface.co/datasets/lmqg/qg_frquad_dummy/discussions/1) to request access to the `lmqg/qg_frquad` after being granted FQuAD access! ## Dataset Description - Repository:* [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - Point of Contact: [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992). This is a modified version of [FQuAD](https://huggingface.co/datasets/fquad) for question generation (QG) task. Since the original dataset only contains training/validation set, we manually sample test set from training set, which has no overlap in terms of the paragraph with the training set. *IMPORTANT NOTE:* The license of this dataset belongs to [FQuAD](https://fquad.illuin.tech/), so please check the guideline there and request the right to access the dataset [here](https://fquad.illuin.tech/) promptly if you use the datset. ### Supported Tasks and Leaderboards * `question-generation`: The dataset is assumed to be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). ### Languages French (fr) ## Dataset Structure An example of 'train' looks as follows. ``` { 'answer': '16 janvier 1377', 'question': 'Quand est-ce que Grégoire XI arrive à Rome ?', 'sentence': "Le pape poursuit son voyage jusqu'à Rome en passant par Corneto où il parvient le 6 décembre 1376, puis il arrive à Rome le 16 janvier 1377 en remontant le Tibre.", 'paragraph': "Quant à Catherine, elle part par voie terrestre en passant par Saint-Tropez, Varazze, puis Gênes. C'est dans cette dernière ville que, selon la Legenda minore, elle aurait de nouveau rencontré Grégoire XI. Le pape poursuit son voyage jusqu'à Rome en passant par Corneto où il parvient le 6 décembre 1376, puis il arrive à Rome le 16 janvier 1377 en remontant le Tibre.", 'sentence_answer': "Le pape poursuit son voyage jusqu'à Rome en passant par Corneto où il parvient le 6 décembre 1376, puis il arrive à Rome le <hl> 16 janvier 1377 <hl> en remontant le Tibre.", 'paragraph_answer': "Quant à Catherine, elle part par voie terrestre en passant par Saint-Tropez, Varazze, puis Gênes. C'est dans cette dernière ville que, selon la Legenda minore, elle aurait de nouveau rencontré Grégoire XI. Le pape poursuit son voyage jusqu'à Rome en passant par Corneto où il parvient le 6 décembre 1376, puis il arrive à Rome le <hl> 16 janvier 1377 <hl> en remontant le Tibre.", 'paragraph_sentence': "Quant à Catherine, elle part par voie terrestre en passant par Saint-Tropez, Varazze, puis Gênes. C'est dans cette dernière ville que, selon la Legenda minore, elle aurait de nouveau rencontré Grégoire XI. <hl> Le pape poursuit son voyage jusqu'à Rome en passant par Corneto où il parvient le 6 décembre 1376, puis il arrive à Rome le 16 janvier 1377 en remontant le Tibre. <hl>" } ``` The data fields are the same among all splits. - `question`: a `string` feature. - `paragraph`: a `string` feature. - `answer`: a `string` feature. - `sentence`: a `string` feature. - `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`. - `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`. - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`. Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model, but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and `paragraph_sentence` feature is for sentence-aware question generation. ## Data Splits \|train\|validation\|test \| \|----:\|---------:\|----:\| \|17543\| 3188 \|3188 \| ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
Dannyseeu	null	null	null	false	1	false	Dannyseeu/test	2022-10-01T00:11:10.000Z	null	false	4e2cf183771adae09efa45c170d17d5b04ccdb49	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/Dannyseeu/test/resolve/main/README.md	--- license: afl-3.0 ---
heegyu	null	null	null	false	7	false	heegyu/namuwiki	2022-10-01T02:40:40.000Z	null	false	5631a9bd17a096bab2cd02ea23adbf2327db0d91	[]	[ "license:cc-by-nc-sa-2.0", "language:ko", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "task_categories:other" ]	https://huggingface.co/datasets/heegyu/namuwiki/resolve/main/README.md	--- license: cc-by-nc-sa-2.0 language: - ko language_creators: - other multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - other --- # namu.wiki database dump ## https://namu.wiki/ database dump 2022/03/01<br/> - 867024 rows - download size: 3GB ## Usage ```bash pip install datasets ``` ```python from datasets import load_dataset dataset = load_dataset("heegyu/namuwiki") print(dataset["train"][0]) ``` ``` {'title': '!!아앗!!', 'text': '\n[목차]\n\n\'\'\'{{{+1 ！！ああっと！！}}}\'\'\'\n\n== 개요 ==\n[[파일:3444050440.jpg\|width=60%]]\n▲[[신 세계수의 미궁 2 파프니르기사\|신 세계수의 미궁 2]]에서 뜬 !!아앗!!\n\n[[세계수의 미궁 시리즈]]에 전통으로 등장하는 대사. [[세계수의 미궁 2 제왕의 성배\|2편]]부터 등장했으며 훌륭한 [[사망 플래그]]의 예시이다.\n\n세계수의 모험가들이 탐험하는 던전인 수해의 구석구석에는 채취/벌채/채굴 포인트가 있으며, 이를 위한 채집 스킬에 투자하면 제한된 채집 기회에서 보다 큰 이득을 챙길 수 있다. 그러나 분배할 수 있는 스킬 포인트는 한정되어 있기 때문에 채집 스킬에 투자하는 만큼 전투 스킬 레벨은 낮아지게 된다.[* 다만 채집 시스템은 신 세계수 시리즈의 그리모어 복제, 복합 채집 스킬인 야생의 감, 5편의 종족 특유 스킬, 크로스의 1레벨이 만렙인 채집 스킬 등으로 편의성이 점차 나아져서 채집 스킬 때문에 스킬 트리가 내려가는 일은 점점 줄어들었다.] !!아앗!!이 발생하는 과정을 요약하면 다음과 같다.\n\n 1. 채집용 캐릭터들로 이루어진 약한 파티(ex: [[레인저(세계수의 미궁 2)\|레인저]] 5명)가 수해에 입장한다.\n 1. 필드 전투를 피해 채집 포인트에 도착한 후 열심히 아이템을 캐는 중에...\n 1. \'\'\'!!아앗!!\'\'\' ~~라플레시아가 나타났다!~~\n 이때 등장하는 것은 [[FOE(세계수의 미궁 시리즈)\|FOE]]는 아니지만 \'\'\'훨씬 위층에 등장하는 강력한 필드 몬스터이며 선제 공격을 당하게 된다!\'\'\'\n 1. \'\'\'으앙 죽음\'\'\'(hage)\n\n여담으로 !!아앗!!의 유래는 1인칭 던전 크롤러의 원조 [[위저드리]]에서 함정을 건드렸을 때 나오는 대사 Oops!(おおっと！)라고 한다.\n\n== 각 작품에서의 모습 ==\n=== [[세계수의 미궁 2 제왕의 성배]] ===\n!!아앗!!의 악랄함은 첫 등장한 작품이자 시리즈 중에서도 불친절하기로 정평이 난 2편이 절정이었다. 그야말로 위의 !!아앗!! 시퀀스 그대로, 묻지도 따지지도 않고 채집할 때마다 일정 확률로 \'\'\'강제로\'\'\' 전투에 돌입해야 했다. 게다가 이럴 때 쓰라고 있는 레인저의 스킬 \'위험 감지(중간 확률로 적의 선제 공격을 무효화)\'는 정작 작동하지 않는다!\n\n참고로 2편에서 채집 도중 !!아앗!!이 뜰 확률은 [[http://www.atlusnet.jp/topic/detail/910\|고작 1%다.]] [[던파확률의 법칙\|낮아 보이는 확률이어도 플레이 중 한 번이라도 일어나는 것]]을 경험하는 체감 확률을 고려하여 확률을 설정한다고.\n\n=== [[세계수의 미궁 3 성해의 내방자]] ===\n다행히 채집 중 낮은 확률로 "좋은 아이템을 얻을 수 있을 것 같지만... 주변에서 몬스터들의 기척이 느껴진다."는 메시지가 뜨고 이때 운이 좋으면 레어 아이템을 얻을 수 있지만 반대의 경우 적과 싸우게 되는 것으로 조정되었다.\n\n=== [[세계수의 미궁 4 전승의 거신]] ===\n기본적인 것은 3편과 같지만, 4편에서는 움직이지 않고 채집할 때도 턴이 경과하도록 조정되었기 때문에 주변에 있는 FOE를 잊고 채집에 몰두하다가 FOE와 부딪히면 FOE 버전 !!아앗!!이 뜬다. 그리고 난이도 CASUAL로 플레이시, FOE로 인한 !!아앗!!을 제외하면 절대로 발생하지 않는다.\n\n=== [[신 세계수의 미궁 밀레니엄의 소녀\|신 세계수의]] [[신 세계수의 미궁 2 파프니르기사\|미궁 시리즈]] ===\n채집 방식이 한 턴으로 끝나는 구조[* 채집으로 한 번 아이템을 획득하면 "다시, (채집 스킬)에 의해..."가 뜨면서 한꺼번에 획득되는 구조.]로 바뀐 덕분인지 강제 조우로 다시 회귀해버렸다(...). 그나마 위험 감지 먹통과 같은 버그성 난점들은 수정되었다. 그 이후에 나온 [[세계수의 미궁 5 오랜 신화의 끝]]과 시리즈의 집대성 작품이자 3DS 마지막 작품인 [[세계수의 미궁 X]]도 마찬가지.\n\n=== [[세계수의 미궁 X]] ===\n본작의 채집은 신 세계수 시리즈와 같은 매커니즘이라 굳이 언급할 필요는 없으나, 퀘스트중에 2편의 !!아앗!! 시퀀스를 재현하면서 \'\'\'라플레시아\'\'\'가 등장하는 퀘스트가 존재한다.(...) 깨알같이 시스템 메세지 창이 아니라 대화창을 이용해서 완벽 재현한 것이 포인트.\n\n=== [[페르소나 Q 섀도우 오브 더 래버린스]] ===\n세계수 시스템을 기반으로 한 [[페르소나 시리즈]]와의 콜라보 작품인 페르소나 Q에서도 등장한다. 3, 4편과 같이 파워 스폿에서 채집 도중 메시지가 뜨며, 실패하면 파티에 참가하고 있는 멤버 중 한 명의 [[http://nico.ms/sm25683358\|!!아앗!! 하는 음성]] ~~또는 [[코로마루\|개소리]]~~과 함께 그 던전의 \'강적\'인 거대 [[섀도(페르소나 시리즈)\|섀도우]]가 나타난다.\n\n그러나 내비 전용 스킬인 뱀눈 노려보기(위험 감지와 같은 효과)와 채집 보조 스킬은 파티의 전투력에 전혀 지장을 주지 않으며, \'대안심\'을 달면 거의 볼 일이 없어져서 초중반 이후에는 존재감이 급격히 줄어든다.\n[[분류:세계수의 미궁 시리즈]]', 'contributors': '110.46.34.123,kirby10,max0243,218.54.117.149,ruby3141,121.165.63.239,iviyuki,1.229.200.194,anatra95,kiri47,175.127.134.2,nickchaos71,chkong1998,kiwitree2,namubot,huwieblusnow', 'namespace': ''} ```
MoreMemes	null	null	null	false	1	false	MoreMemes/Image	2022-10-01T01:14:46.000Z	null	false	d65e67ec2ab80128e746d83e2aaf3888f096e29f	[]	[ "license:openrail" ]	https://huggingface.co/datasets/MoreMemes/Image/resolve/main/README.md	--- license: openrail ---
heegyu	null	null	null	false	598	false	heegyu/namuwiki-extracted	2022-10-02T09:02:13.000Z	null	false	2184f274a727f48d78951beca4ce318efa685f94	[]	[ "license:cc-by-nc-sa-2.0", "language:ko", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "task_categories:other" ]	https://huggingface.co/datasets/heegyu/namuwiki-extracted/resolve/main/README.md	--- license: cc-by-nc-sa-2.0 language: - ko language_creators: - other multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - other --- # namu.wiki database dump ## https://namu.wiki/ database dump 2022/03/01<br/> - 571308rows - download size: 2.19GB ## 주의사항 namu-wiki-extractor를 이용하여 전처리, 추가로 아래 전처리를 수행했습니다 1. 헤더 제거 `== 개요 ==` 1. 테이블 제거 1. `[age(1997-01-01)]` 는 전처리 시점 기준으로 적용(2022년 10월 2일) 1. `[math(a / b + c)]` 는 제거하지 않음. 1. math 마크다운이 각주 내에 있을 경우, 각주가 전처리되지 않은 문제 있음. ## Usage ```bash pip install datasets ``` ```python from datasets import load_dataset dataset = load_dataset("heegyu/namuwiki-extracted") print(dataset["train"][0]) ``` ``` { 'title': '!!아앗!!', 'text': '！！ああっと！！ ▲신 세계수의 미궁 2에서 뜬 !!아앗!! 세계수의 미궁 시리즈에 전통으로 등장하는 대사. 2편부터 등장했으며 훌륭한 사망 플래그의 예시이다. 세계수의 모험가들이 탐험하는 던전인 수해의 구석구석에는 채취/벌채/채굴 포인트가 있으며, 이를 위한 채집 스킬에 ...', 'contributors': '110.46.34.123,kirby10,max0243,218.54.117.149,ruby3141,121.165.63.239,iviyuki,1.229.200.194,anatra95,kiri47,175.127.134.2,nickchaos71,chkong1998,kiwitree2,namubot,huwieblusnow', 'namespace': '' } ```
ZidaneAdnie	null	null	null	false	1	false	ZidaneAdnie/test	2022-10-01T03:38:29.000Z	null	false	45874d64c3667373215855cbdf86611caec69386	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/ZidaneAdnie/test/resolve/main/README.md	--- license: afl-3.0 ---
ali4546	null	null	null	false	1	false	ali4546/all	2022-10-01T04:17:50.000Z	null	false	215e153b7ffc3017223f2a2bc580cf3d27c67bd2	[]	[ "license:openrail" ]	https://huggingface.co/datasets/ali4546/all/resolve/main/README.md	--- license: openrail ---
heegyu	null	null	null	false	42	false	heegyu/namuwiki-sentences	2022-10-14T07:55:44.000Z	null	false	13a03baacde282bc1573bee2963ea0ca677286d3	[]	[ "license:cc-by-nc-sa-2.0", "language:ko", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "task_categories:other" ]	https://huggingface.co/datasets/heegyu/namuwiki-sentences/resolve/main/README.md	--- license: cc-by-nc-sa-2.0 language: - ko language_creators: - other multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - other --- - 38,015,081 rows
yihan422	null	null	null	false	2	false	yihan422/johnny	2022-10-01T06:17:11.000Z	null	false	62f2c7c1ccbf16e4a335042a5fad8be287abd525	[]	[ "license:openrail" ]	https://huggingface.co/datasets/yihan422/johnny/resolve/main/README.md	--- license: openrail ---
ashwinperti	null	null	null	false	2	false	ashwinperti/autotrain-data-ashwin_sentiment140dataset	2022-10-01T08:40:44.000Z	null	false	0e661470ee297dc7b3d13fa9e70ff4c9e96cd1a2	[]	[ "language:en", "task_categories:text-classification" ]	https://huggingface.co/datasets/ashwinperti/autotrain-data-ashwin_sentiment140dataset/resolve/main/README.md	--- language: - en task_categories: - text-classification --- # AutoTrain Dataset for project: ashwin_sentiment140dataset ## Dataset Description This dataset has been automatically processed by AutoTrain for project ashwin_sentiment140dataset. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "@JordainFTW i didnt watch them BUT CALEB PLAYS NAZI ZOMBIES TOOOOOO!!!!!!!!!! OMG OMG OMG! HE IS MY BESTFREIND! what do u needa tell me?", "target": 1 }, { "text": "@Jennymac22 too much info! good for you hun. I'm pleased for you. ", "target": 1 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(num_classes=2, names=['0', '4'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 2399 \| \| valid \| 601 \|
ZoZ2354	null	null	null	false	1	false	ZoZ2354/Photos_face	2022-10-01T12:09:27.000Z	null	false	f5faee12003025a4abb1c54cc0ec40a4554a55bd	[]	[ "license:openrail" ]	https://huggingface.co/datasets/ZoZ2354/Photos_face/resolve/main/README.md	--- license: openrail ---
Brathna	null	null	null	false	2	false	Brathna/nanao	2022-10-01T13:53:22.000Z	null	false	f221043ea28fcd9a6fddcf46ebde8ebdc74ab667	[]	[ "license:openrail" ]	https://huggingface.co/datasets/Brathna/nanao/resolve/main/README.md	--- license: openrail ---
emilanker	null	null	null	false	1	false	emilanker/cryptopunks_5_gpt2	2022-10-01T13:53:59.000Z	null	false	b505c573ccc1c2491f70e3a36672ce75596427ae	[]	[]	https://huggingface.co/datasets/emilanker/cryptopunks_5_gpt2/resolve/main/README.md
MIlgacia	null	null	null	false	null	false	MIlgacia/eret	2022-10-13T08:58:19.000Z	null	false	279c76889787d6c2e7f3e259af0f52ffa8fa1626	[]	[]	https://huggingface.co/datasets/MIlgacia/eret/resolve/main/README.md
earroyo	null	null	null	false	1	false	earroyo/earroyoluna	2022-10-01T14:22:24.000Z	null	false	c9d466e0d1ebf538e1e9c666ca6596405e292cad	[]	[ "license:openrail" ]	https://huggingface.co/datasets/earroyo/earroyoluna/resolve/main/README.md	--- license: openrail ---
zoiz	null	null	null	false	1	false	zoiz/test	2022-10-01T15:09:09.000Z	null	false	41b15060fa8d35740ef0e2dcf908a9b987d51690	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/zoiz/test/resolve/main/README.md	--- license: afl-3.0 ---
ihassan1	null	null	null	false	14	false	ihassan1/auditor-sentiment	2022-10-02T08:44:54.000Z	null	false	338ff07c51b098d242e535cd8d7d536e873dea68	[]	[ "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "tags:auditor", "tags:financial", "tags:sentiment", "tags:markets", "task_categories:text-classification", "task_ids:sentiment-scoring" ]	https://huggingface.co/datasets/ihassan1/auditor-sentiment/resolve/main/README.md	--- annotations_creators: - expert-generated language: [] language_creators: - expert-generated license: [] multilinguality: - monolingual pretty_name: auditor-sentiment size_categories: [] source_datasets: [] tags: - auditor - financial - sentiment - markets task_categories: - text-classification task_ids: - sentiment-scoring --- # Dataset Card for Auditor Sentiment
earroyo	null	null	null	false	1	false	earroyo/earroyo	2022-10-01T16:13:44.000Z	null	false	6d7bd4418d47bf4a598c577e7e16c59a854d1b90	[]	[ "license:openrail" ]	https://huggingface.co/datasets/earroyo/earroyo/resolve/main/README.md	--- license: openrail ---
Mainred	null	null	null	false	1	false	Mainred/model	2022-10-01T19:46:07.000Z	null	false	8f32ed1a4ce202774cc29ed193671366c5b1d85e	[]	[ "license:unknown" ]	https://huggingface.co/datasets/Mainred/model/resolve/main/README.md	--- license: unknown ---
freefire31	null	null	null	false	1	false	freefire31/autotrain-data-oveja31	2022-10-01T17:26:57.000Z	null	false	53e379cb1f25191b32d37c43646edade37434e59	[]	[ "task_categories:image-classification" ]	https://huggingface.co/datasets/freefire31/autotrain-data-oveja31/resolve/main/README.md	--- task_categories: - image-classification --- # AutoTrain Dataset for project: oveja31 ## Dataset Description This dataset has been automatically processed by AutoTrain for project oveja31. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<1424x1424 RGB PIL image>", "target": 0 }, { "image": "<1627x1627 RGB PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(num_classes=1, names=['oveja'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 4 \| \| valid \| 1 \|
Hellisotherpeople	null	null	null	false	1	false	Hellisotherpeople/one_syllable	2022-10-01T17:46:42.000Z	null	false	c72b2a584cc89b468e1d54759df144dd2d08751f	[]	[ "annotations_creators:no-annotation", "language:en", "language_creators:expert-generated", "license:mit", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "tags:syllable", "tags:one_syllable", "task_categories:text-generation", "task_categories:fill-mask",...	https://huggingface.co/datasets/Hellisotherpeople/one_syllable/resolve/main/README.md	--- annotations_creators: - no-annotation language: - en language_creators: - expert-generated license: - mit multilinguality: - monolingual pretty_name: 'one_syllable from Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio' size_categories: - 10K<n<100K source_datasets: - original tags: - syllable - one_syllable task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for Lipogram-e ## 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://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio - Repository: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio - Paper Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio - Leaderboard: https://github.com/Hellisotherpeople/Constrained-Text-Generation-Studio - Point of Contact: https://www.linkedin.com/in/allen-roush-27721011b/ ### Dataset Summary ![Gadsby](https://www.gutenberg.org/cache/epub/6936/pg6936.cover.medium.jpg) This is a dataset of English books which only write using one syllable at a time. At this time, the dataset only contains Robinson Crusoe — in Words of One Syllable by Lucy Aikin and Daniel Defoe This dataset is contributed as part of a paper titled "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" to appear at COLING 2022. This dataset does not appear in the paper itself, but was gathered as a candidate constrained text generation dataset. ### Supported Tasks and Leaderboards The main task for this dataset is Constrained Text Generation - but all types of language modeling are suitable. ### Languages English ## Dataset Structure ### Data Instances Each is extracted directly from the available pdf or epub documents converted to txt using pandoc. ### Data Fields Text. The name of each work appears before the work starts and again at the end, so the books can be trivially split again if necessary. ### Data Splits None given. The way I do so in the paper is to extract the final 20% of each book, and concatenate these together. This may not be the most ideal way to do a train/test split, but I couldn't think of a better way. I did not believe randomly sampling was appropriate, but I could be wrong. ## Dataset Creation ### Curation Rationale There are several books which claim to only be written using one syllable words. A list of them can be found here: https://diyhomeschooler.com/2017/01/25/classics-in-words-of-one-syllable-free-ebooks/ Unfortunately, after careful human inspection, it appears that only one of these works actually does reliably maintain the one syllable constraint through the whole text. Outside of proper names, I cannot spot or computationally find a single example of a more-than-one-syllable word in this whole work. ### Source Data Robinson Crusoe — in Words of One Syllable by Lucy Aikin and Daniel Defoe #### Initial Data Collection and Normalization Project Gutenberg #### Who are the source language producers? Lucy Aikin and Daniel Defoe ### Annotations #### Annotation process None #### Who are the annotators? n/a ### Personal and Sensitive Information None ## Considerations for Using the Data There may be OCR conversion artifacts. ### Social Impact of Dataset These books have existed for a awhile now, so it's unlikely that this will have dramatic Social Impact. ### Discussion of Biases The only biases possible are related to the contents of Robinson Crusoe or the possibility of the authors changing Robinson Crusoe in some problematic way by using one-syllable words. This is unlikely, as this work was aimed at children. ### Other Known Limitations It's possible that more works exist but were not well known enough for the authors to find them and include them. Finding such inclusions would be grounds for iteration of this dataset (e.g. a version 1.1 would be released). The goal of this project is to eventually encompass all book length english language works that do not use more than one syllable in each of their words (except for names) ## Additional Information n/a ### Dataset Curators Allen Roush ### Licensing Information MIT ### Citation Information TBA ### Contributions Thanks to [@Hellisotherpeople](https://github.com/Hellisotherpeople) for adding this dataset.
urbanalaura	null	null	null	false	1	false	urbanalaura/toni	2022-10-01T18:41:43.000Z	null	false	d8867e3859435abc058208e0fe5fda18a621a31e	[]	[ "license:creativeml-openrail-m" ]	https://huggingface.co/datasets/urbanalaura/toni/resolve/main/README.md	--- license: creativeml-openrail-m ---
RAPTORIDK	null	null	null	false	1	false	RAPTORIDK/Face	2022-10-01T19:46:10.000Z	null	false	4214e4cc24f29f54c25a794aedbf14f4fb8e130b	[]	[ "license:unknown" ]	https://huggingface.co/datasets/RAPTORIDK/Face/resolve/main/README.md	--- license: unknown ---
jwfeniello	null	null	null	false	1	false	jwfeniello/skrill	2022-10-01T19:39:27.000Z	null	false	e5b5a21dc0e4977078885497e4343e3c27a92278	[]	[ "license:openrail" ]	https://huggingface.co/datasets/jwfeniello/skrill/resolve/main/README.md	--- license: openrail ---
ELUNIVERSODEJDC	null	null	null	false	1	false	ELUNIVERSODEJDC/liuouio	2022-10-01T20:53:27.000Z	null	false	a5c4b9c07d04a685a0880cc31df2df24747b935d	[]	[ "license:openrail" ]	https://huggingface.co/datasets/ELUNIVERSODEJDC/liuouio/resolve/main/README.md	--- license: openrail ---
RAPTORIDK	null	null	null	false	1	false	RAPTORIDK/Mi-Cara	2022-10-01T21:17:01.000Z	null	false	505c751fbe3932c04c1e85ae1f21b2a460e8c446	[]	[ "license:unknown" ]	https://huggingface.co/datasets/RAPTORIDK/Mi-Cara/resolve/main/README.md	--- license: unknown ---
ZeFluffyNuphkin	null	null	null	false	1	false	ZeFluffyNuphkin/testimage	2022-10-01T23:37:59.000Z	null	false	d42e77e41b6370eebff7ae693554e2683927165e	[]	[]	https://huggingface.co/datasets/ZeFluffyNuphkin/testimage/resolve/main/README.md
heegyu	null	@InProceedings{huggingface:dataset, title = {kowikitext}, author={Wikipedia}, year={2022} }	한국어 위키피디아 article	false	72	false	heegyu/kowikitext	2022-10-02T05:07:59.000Z	null	false	dd14ef9eaf8a803cc68cec01f9fc7d353d162264	[]	[ "license:cc-by-sa-3.0" ]	https://huggingface.co/datasets/heegyu/kowikitext/resolve/main/README.md	--- license: cc-by-sa-3.0 --- 한국어 위키피디아 article 덤프(20221001) - 1334694 rows - download size: 474MB ```python from datasets import load_dataset ds = load_dataset("heegyu/kowikitext", "20221001") ds["train"][0] ``` ``` {'id': '5', 'revid': '595831', 'url': 'https://ko.wikipedia.org/wiki?curid=5', 'title': '지미 카터', 'text': '제임스 얼 카터 주니어(, 1924년 10월 1일 ~ )는 민주당 출신 미국 39대 대통령 (1977년 ~ 1981년)이다.\n생애.\n어린 시절.\n지미 카터는 조지아주 섬터 카운티 플레인스 마을에서 태어났다.\n조지아 공과대학교를 졸업하였다. 그 후 해군에 들어가 전함·원자력·잠수함의 승무원으로 일하였다. 1953년 미국 해군 대위로 예편하였고 이후 땅콩·면화 등을 가꿔 많은 돈을 벌었다. 그의 별명이 "땅콩 농부" (Peanut Farmer)로 알려졌다.\n정계 입문.\n1962년 조지아주 상원 의원 선거에서 낙선하나 그 선거가 부정선거 였음을 ... " } ```
pipexta	null	null	null	false	1	false	pipexta/yo	2022-10-02T05:09:07.000Z	null	false	9b1ce6e04596ad6e820bf11d5617a58788b6be8f	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/pipexta/yo/resolve/main/README.md	--- license: afl-3.0 ---
halo1998	null	null	null	false	1	false	halo1998/yo	2022-10-02T05:50:35.000Z	null	false	c7775ee196a6b7fd3ef1b2d74ee0be731ff1edf5	[]	[]	https://huggingface.co/datasets/halo1998/yo/resolve/main/README.md	![cv-2.jpg](https://s3.amazonaws.com/moonup/production/uploads/1664689489882-63391346a806650bd038c7ca.jpeg) ![finsin.jpg](https://s3.amazonaws.com/moonup/production/uploads/1664689654370-63391346a806650bd038c7ca.jpeg) ![IMG_5594.jpg](https://s3.amazonaws.com/moonup/production/uploads/1664689743312-63391346a806650bd038c7ca.jpeg) ![casivieja-Recuperado-3.jpg](https://s3.amazonaws.com/moonup/production/uploads/1664689802595-63391346a806650bd038c7ca.jpeg)
Smuzzer	null	null	null	false	1	false	Smuzzer/Rach	2022-10-02T08:07:11.000Z	null	false	061145ef43c2bad28c8c81d9c1d9fb4448c8840b	[]	[ "license:openrail" ]	https://huggingface.co/datasets/Smuzzer/Rach/resolve/main/README.md	--- license: openrail ---
ricewind	null	null	null	false	11	false	ricewind/logo-union	2022-10-02T11:13:10.000Z	null	false	b281fecc25a04fc100389a93fce9d835bf9ec347	[]	[]	https://huggingface.co/datasets/ricewind/logo-union/resolve/main/README.md	imagenes logo del real union tenerife license: other ---
nrtf	null	null	null	false	7	false	nrtf/exp-gan	2022-10-02T11:03:57.000Z	null	false	fbcb4551b60e7eb90ef5a3d55afed6ad212d2d8d	[]	[ "license:cc-by-nc-sa-4.0" ]	https://huggingface.co/datasets/nrtf/exp-gan/resolve/main/README.md	--- license: cc-by-nc-sa-4.0 ---
xueqing12	null	null	null	false	1	false	xueqing12/Arcane-style	2022-10-02T12:49:54.000Z	null	false	1f0aa574fa48c8a418312bce7db5b63e4ad8f5d6	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/xueqing12/Arcane-style/resolve/main/README.md	--- license: afl-3.0 ---
JannaB	null	null	null	false	10	false	JannaB/dreambooth_corgie	2022-10-02T13:45:49.000Z	null	false	8a097d8f1fe23fb1493689f89dd63637d070d640	[]	[]	https://huggingface.co/datasets/JannaB/dreambooth_corgie/resolve/main/README.md
st4lk1981	null	null	null	false	11	false	st4lk1981/titou	2022-10-02T13:31:56.000Z	null	false	cca7aee96d624adec293e9f91e3c1fded3463b55	[]	[ "license:cc" ]	https://huggingface.co/datasets/st4lk1981/titou/resolve/main/README.md	--- license: cc ---
Llamacha	null	null	null	false	10	false	Llamacha/ner_quechua_iic	2022-10-02T14:19:29.000Z	null	false	419747e72470311563b3b35b9c178dc69e3ab116	[]	[ "annotations_creators:crowdsourced", "language:qu", "license:apache-2.0", "size_categories:n<1K", "source_datasets:original", "task_categories:token-classification", "task_ids:named-entity-recognition" ]	https://huggingface.co/datasets/Llamacha/ner_quechua_iic/resolve/main/README.md	--- annotations_creators: - crowdsourced language: - qu license: - apache-2.0 size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition --- # Dataset Card for WikiANN ## 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 - Paper: The original datasets come from Introducing QuBERT: A Large Monolingual Corpus and BERT Model for Southern Quechua [paper](https://aclanthology.org/2022.deeplo-1.1.pdf) by Rodolfo Zevallos et al. (2022). - Point of Contact: [Rodolfo Zevallos](mailto:rodolfojoel.zevallos@upf.edu) ### Dataset Summary NER_Quechua_IIC is a named entity recognition dataset consisting of dictionary texts provided by the Peruvian Ministry of Education, annotated with LOC (location), PER (person) and ORG (organization) tags in the IOB2 format. ### Supported Tasks and Leaderboards - `named-entity-recognition`: The dataset can be used to train a model for named entity recognition in Quechua languages.
poopat	null	null	null	false	7	false	poopat/gad	2022-10-02T15:30:17.000Z	null	false	77bcbae3e9a4460c19a2aeb5203e7c9286063c5a	[]	[ "license:unknown" ]	https://huggingface.co/datasets/poopat/gad/resolve/main/README.md	--- license: unknown ---
laion	null	null	null	false	1	false	laion/laion1b-nolang-inappropriate	2022-10-19T11:01:06.000Z	null	false	a58171a9da76c081f50737ff77584e2d69a1c723	[]	[]	https://huggingface.co/datasets/laion/laion1b-nolang-inappropriate/resolve/main/README.md	--- license: cc-by-4.0 ---
alexx855	null	null	null	false	12	false	alexx855/alex	2022-10-02T17:00:00.000Z	null	false	e6317501d075d06ec9d66ca80de205db744b1d10	[]	[]	https://huggingface.co/datasets/alexx855/alex/resolve/main/README.md
laion	null	null	null	false	10	false	laion/laion2b-multi-inappropriate	2022-10-19T09:55:50.000Z	null	false	da53154a0607aed6e1717f15e94b8708bb0adb6b	[]	[]	https://huggingface.co/datasets/laion/laion2b-multi-inappropriate/resolve/main/README.md	--- license: cc-by-4.0 ---
Lin0106	null	null	null	false	7	false	Lin0106/0	2022-10-02T17:46:14.000Z	null	false	ce9bbc3b105b6344f3ce3f8e626893190d7211a0	[]	[]	https://huggingface.co/datasets/Lin0106/0/resolve/main/README.md
laion	null	null	null	false	1	false	laion/laion2b-en-inappropriate	2022-10-19T10:34:51.000Z	null	false	1c202a9f31be80534c33182b6cc6266843f77b79	[]	[]	https://huggingface.co/datasets/laion/laion2b-en-inappropriate/resolve/main/README.md	--- license: cc-by-4.0 ---
JECR	null	null	null	false	11	false	JECR/MomosBot-4000	2022-10-02T18:43:01.000Z	null	false	51e8d061ef7a74b3975378314140502056f8cbcc	[]	[ "license:openrail" ]	https://huggingface.co/datasets/JECR/MomosBot-4000/resolve/main/README.md	--- license: openrail ---
Sanatbek	null	@InProceedings{maqolani citationi} }	This is a collection of translated sentences from Uzbek to Kazakh 2 languages, #3,403 bitexts total number of files: #750 total number of tokens: #65.54M total number of sentence fragments: #8.96M	false	11	false	Sanatbek/uzbek-kazakh-parallel-corpora	2022-11-16T19:36:37.000Z	null	false	c088e5277ef73c0878ad19dca262e71888b088f0	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/Sanatbek/uzbek-kazakh-parallel-corpora/resolve/main/README.md	--- license: apache-2.0 ---
Aeromesi	null	null	null	false	7	false	Aeromesi/Aeromesi	2022-10-02T19:02:39.000Z	null	false	1d5ff60f05d41aecea1ef85b472802dbfcc912e0	[]	[ "license:gpl-2.0" ]	https://huggingface.co/datasets/Aeromesi/Aeromesi/resolve/main/README.md	--- license: gpl-2.0 ---
mvazquez	null	null	null	false	11	false	mvazquez/LSE_eSaude_UVIGO_OSLWL	2022-10-02T19:35:04.000Z	null	false	116f94359b7479e58f21e746b3ab6a301c756275	[]	[]	https://huggingface.co/datasets/mvazquez/LSE_eSaude_UVIGO_OSLWL/resolve/main/README.md	--- annotations_creators: - expert-generated language: - lse language_creators: - expert-generated license: - mit multilinguality: - monolingual pretty_name: LSE_eSaude_UVIGO_OSLWL size_categories: - n<1K source_datasets: - original tags: - sign spotting - sign language recognition - lse task_categories: - other task_ids: [] # Dataset Card for LSE_eSaude_UVIGO_OSLWL ## 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.
mvazquez	null	null	null	false	10	false	mvazquez/LSE_eSaude_UVIGO_MSSL	2022-10-02T22:17:37.000Z	null	false	d58dec86dc1e680d142ec8e108ed48d06da35188	[]	[]	https://huggingface.co/datasets/mvazquez/LSE_eSaude_UVIGO_MSSL/resolve/main/README.md	--- annotations_creators: - expert-generated language: - lse language_creators: - expert-generated license: - mit multilinguality: - monolingual pretty_name: - LSE_eSaude_UVIGO_MSSL size_categories: - n<1K source_datasets: - original tags: - sign spotting - sign language recognition - lse task_categories: - other task_ids: [] # Dataset Card for LSE_eSaude_UVIGO_MSSL ## 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.
nlp-guild	null	null	null	false	7	false	nlp-guild/Nonlinear-System-Identification-with-Deep-Learning	2022-11-08T18:25:47.000Z	null	false	0adb3ca0d766c5fe50f036896a99e725eb9a5b57	[]	[ "license:mit" ]	https://huggingface.co/datasets/nlp-guild/Nonlinear-System-Identification-with-Deep-Learning/resolve/main/README.md	--- license: mit --- please use the following code to load data: ```python # start data loading !git lfs install !git clone https://huggingface.co/datasets/nlp-guild/Nonlinear-System-Identification-with-Deep-Learning def load_dataset(path='dataset.npy'): """ :return: f_and_xs: numpy array of size [sample_number, channels, sample_length] label_0, label_1, label_2: one-hot encodes of size [sample_number, number_bins] """ r = np.load(path, allow_pickle=True).item() f_and_xs = r['f_and_xs'] label_0 = r['l_0'] label_1 = r['l_1'] label_2 = r['l_2'] return f_and_xs, label_0, label_1, label_2 f_and_xs, label_0, label_1, label_2 = load_dataset('/content/Nonlinear-System-Identification-with-Deep-Learning/dataset.npy') # end data loading ```
Kkoustubh	null	null	null	false	11	false	Kkoustubh/iPhone14Tweets	2022-10-02T20:33:12.000Z	null	false	e93ef8a7d61d58ce27df4f12bfa62f4f804b3029	[]	[ "license:cc" ]	https://huggingface.co/datasets/Kkoustubh/iPhone14Tweets/resolve/main/README.md	--- license: cc --- Approx 144K tweets about iPhone 14
NeelNanda	null	null	null	false	119	false	NeelNanda/pile-10k	2022-10-14T21:27:22.000Z	null	false	127bfedcd5047750df5ccf3a12979a47bfa0bafa	[]	[ "license:bigscience-bloom-rail-1.0" ]	https://huggingface.co/datasets/NeelNanda/pile-10k/resolve/main/README.md	--- license: bigscience-bloom-rail-1.0 --- The first 10K elements of [The Pile](https://pile.eleuther.ai/), useful for debugging models trained on it. See the [HuggingFace page for the full Pile](https://huggingface.co/datasets/the_pile) for more info. Inspired by [stas' great resource](https://huggingface.co/datasets/stas/openwebtext-10k) doing the same for OpenWebText
illorg	null	null	null	false	11	false	illorg/illodata	2022-10-02T21:34:52.000Z	null	false	4f3d39bcb6e59ebe0d744d4d4a42f947c84a6d04	[]	[ "license:gpl" ]	https://huggingface.co/datasets/illorg/illodata/resolve/main/README.md	--- license: gpl ---
doorfromenchumto	null	null	null	false	7	false	doorfromenchumto/Zuzulinda	2022-10-08T23:12:36.000Z	null	false	b0f26da4cf74e72ac9e6e1d8532a6b9abbe13b81	[]	[]	https://huggingface.co/datasets/doorfromenchumto/Zuzulinda/resolve/main/README.md	dxs
Fedeya	null	null	null	false	11	false	Fedeya/me	2022-10-02T23:16:51.000Z	null	false	d03328df89b03b1f314feaaea42d8879621cfc3a	[]	[ "license:unknown" ]	https://huggingface.co/datasets/Fedeya/me/resolve/main/README.md	--- license: unknown ---
pkavumba	null	null	null	false	476	false	pkavumba/balanced-copa	2022-10-03T00:39:01.000Z	null	false	813bd03cd6e07d9bd8d7333896ad5d40abb95ea9	[]	[ "annotations_creators:expert-generated", "language_creators:expert-generated", "language:en", "license:cc-by-4.0", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended\|copa", "task_categories:question-answering", "task_ids:multiple-choice-qa" ]	https://huggingface.co/datasets/pkavumba/balanced-copa/resolve/main/README.md	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: BCOPA size_categories: - unknown source_datasets: - extended\|copa task_categories: - question-answering task_ids: - multiple-choice-qa --- # Dataset Card for "Balanced COPA" ## 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://balanced-copa.github.io/](https://balanced-copa.github.io/) - Repository: [Balanced COPA](https://github.com/Balanced-COPA/Balanced-COPA) - Paper: [When Choosing Plausible Alternatives, Clever Hans can be Clever](https://aclanthology.org/D19-6004/) - Point of Contact: [@pkavumba](https://github.com/pkavumba) ### Dataset Summary Bala-COPA: An English language Dataset for Training Robust Commonsense Causal Reasoning Models The Balanced Choice of Plausible Alternatives dataset is a benchmark for training machine learning models that are robust to superficial cues/spurious correlations. The dataset extends the COPA dataset(Roemmele et al. 2011) with mirrored instances that mitigate against token-level superficial cues in the original COPA answers. The superficial cues in the original COPA datasets result from an unbalanced token distribution between the correct and the incorrect answer choices, i.e., some tokens appear more in the correct choices than the incorrect ones. Balanced COPA equalizes the token distribution by adding mirrored instances with identical answer choices but different labels. The details about the creation of Balanced COPA and the implementation of the baselines are available in the paper. Balanced COPA language en ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages - English ## Dataset Structure ### Data Instances An example of 'validation' looks as follows. ``` { "id": 1, "premise": "My body cast a shadow over the grass.", "choice1": "The sun was rising.", "choice2": "The grass was cut.", "question": "cause", "label": 1, "mirrored": false, } { "id": 1001, "premise": "The garden looked well-groomed.", "choice1": "The sun was rising.", "choice2": "The grass was cut.", "question": "cause", "label": 1, "mirrored": true, } ``` ### Data Fields The data fields are the same among all splits. #### en - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `id`: a `int32` feature. - `mirrored`: a `bool` feature. ### Data Splits \| validation \| test \| \| ---------: \| ---: \| \| 1,000 \| 500 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @inproceedings{kavumba-etal-2019-choosing, title = "When Choosing Plausible Alternatives, Clever Hans can be Clever", author = "Kavumba, Pride and Inoue, Naoya and Heinzerling, Benjamin and Singh, Keshav and Reisert, Paul and Inui, Kentaro", booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-6004", doi = "10.18653/v1/D19-6004", pages = "33--42", abstract = "Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one. Are BERT{'}s and RoBERTa{'}s good performance on COPA also caused by this? We find superficial cues in COPA, as well as evidence that BERT exploits these cues.To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-to-exploit single token cues. We analyze BERT{'}s and RoBERTa{'}s performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to. In contrast, RoBERTa does not appear to rely on superficial cues.", } @inproceedings{roemmele2011choice, title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning}, author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S}, booktitle={2011 AAAI Spring Symposium Series}, year={2011}, url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF}, } ``` ### Contributions Thanks to [@pkavumba](https://github.com/pkavumba) for adding this dataset.
Nithiwat	null	null	null	false	10	false	Nithiwat/claimbuster	2022-10-03T02:19:55.000Z	null	false	22070db560e13c40e8035108e3f965dc86243273	[]	[ "license:cc-by-sa-4.0" ]	https://huggingface.co/datasets/Nithiwat/claimbuster/resolve/main/README.md	--- license: cc-by-sa-4.0 ---
bigcode	null	null	null	false	931	true	bigcode/the-stack	2022-11-09T09:09:40.000Z	null	false	c37a8cd1e382064d8aced5e05543c5f7753834da	[]	[ "arxiv:2107.03374", "arxiv:2207.14157", "language_creators:crowdsourced", "language_creators:expert-generated", "language:code", "license:other", "multilinguality:multilingual", "size_categories:unknown", "task_categories:text-generation", "extra_gated_prompt:## Terms of Use for The Stack\n\nThe S...	https://huggingface.co/datasets/bigcode/the-stack/resolve/main/README.md
lzkhit	null	null	null	false	9	false	lzkhit/images	2022-10-03T04:26:50.000Z	null	false	8773546d3ab6da40447285488e8383c70b3e4a08	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/lzkhit/images/resolve/main/README.md	--- license: apache-2.0 ---
bigscience-biomedical	null	@article{gu2021domain, title = { Domain-specific language model pretraining for biomedical natural language processing }, author = { Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung }, year = 2021, journal = {ACM Transactions on Computing for Healthcare (HEALTH)}, publisher = {ACM New York, NY}, volume = 3, number = 1, pages = {1--23} }	The BioCreative II Gene Mention task. The training corpus for the current task consists mainly of the training and testing corpora (text collections) from the BCI task, and the testing corpus for the current task consists of an additional 5,000 sentences that were held 'in reserve' from the previous task. In the current corpus, tokenization is not provided; instead participants are asked to identify a gene mention in a sentence by giving its start and end characters. As before, the training set consists of a set of sentences, and for each sentence a set of gene mentions (GENE annotations). - Homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/ - Repository: https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/ - Paper: Overview of BioCreative II gene mention recognition https://link.springer.com/article/10.1186/gb-2008-9-s2-s2	false	31	false	bigscience-biomedical/blurb	2022-10-16T19:22:08.000Z	null	false	37f1b9805df6fed8f71d6f3176afc8bb57589dd5	[]	[ "language:en", "license:other", "multilinguality:monolingual" ]	https://huggingface.co/datasets/bigscience-biomedical/blurb/resolve/main/README.md	--- language: en license: other multilinguality: monolingual pretty_name: BLURB --- # Dataset Card for BLURB ## Dataset Description - Homepage: https://microsoft.github.io/BLURB/tasks.html - Pubmed: True - Public: True - Tasks: Named Entity Recognition BLURB is a collection of resources for biomedical natural language processing. In general domains, such as newswire and the Web, comprehensive benchmarks and leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. In biomedicine, however, such resources are ostensibly scarce. In the past, there have been a plethora of shared tasks in biomedical NLP, such as BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These efforts have played a significant role in fueling interest and progress by the research community, but they typically focus on individual tasks. The advent of neural language models, such as BERT provides a unifying foundation to leverage transfer learning from unlabeled text to support a wide range of NLP applications. To accelerate progress in biomedical pretraining strategies and task-specific methods, it is thus imperative to create a broad-coverage benchmark encompassing diverse biomedical tasks. Inspired by prior efforts toward this direction (e.g., BLUE), we have created BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP applications, as well as a leaderboard for tracking progress by the community. BLURB includes thirteen publicly available datasets in six diverse tasks. To avoid placing undue emphasis on tasks with many available datasets, such as named entity recognition (NER), BLURB reports the macro average across all tasks as the main score. The BLURB leaderboard is model-agnostic. Any system capable of producing the test predictions using the same training and development data can participate. The main goal of BLURB is to lower the entry barrier in biomedical NLP and help accelerate progress in this vitally important field for positive societal and human impact. This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits. ## Citation Information ``` @article{gu2021domain, title = { Domain-specific language model pretraining for biomedical natural language processing }, author = { Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung }, year = 2021, journal = {ACM Transactions on Computing for Healthcare (HEALTH)}, publisher = {ACM New York, NY}, volume = 3, number = 1, pages = {1--23} } ```
RAMILISON	null	null	null	false	6	false	RAMILISON/rajo	2022-10-03T13:15:44.000Z	null	false	3e395fa9420dd2c3389e541b073228ed2a8e3f9e	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/RAMILISON/rajo/resolve/main/README.md	--- license: apache-2.0 ---
kenobi	null	null	null	false	1	false	kenobi/SDO	2022-10-03T09:46:38.000Z	null	false	203a83696a54ec5a17ec6698884c32164f7293ee	[]	[ "annotations_creators:expert-generated", "language_creators:other", "license:other", "size_categories:n<1K", "tags:space research", "tags:solar research", "tags:heliophysics", "task_categories:image-classification", "task_categories:object-detection", "task_categories:image-segmentation", "task_...	https://huggingface.co/datasets/kenobi/SDO/resolve/main/README.md	--- annotations_creators: - expert-generated language: [] language_creators: - other license: - other multilinguality: [] pretty_name: SDO size_categories: - n<1K source_datasets: [] tags: - space research - solar research - heliophysics task_categories: - image-classification - object-detection - image-segmentation - image-to-text - image-to-image - visual-question-answering - zero-shot-image-classification task_ids: - multi-class-image-classification - semantic-segmentation - image-captioning ---
Gr3en	null	null	null	false	1	false	Gr3en/AM	2022-10-03T10:20:40.000Z	null	false	1d237a4a7425f3f836779431280ef23b36851ec4	[]	[]	https://huggingface.co/datasets/Gr3en/AM/resolve/main/README.md	annotations_creators: - no-annotation language: - en language_creators: - other license: - artistic-2.0 multilinguality: - monolingual pretty_name: AndreaMasucci size_categories: - n<1K source_datasets: - original tags: [] task_categories: - text-to-image task_ids: []
SaintSpirit	null	null	null	false	1	false	SaintSpirit/bermejo	2022-10-03T11:15:13.000Z	null	false	51730024741118e61660cd16fae1c046c053a769	[]	[ "license:cc-by-nc-4.0" ]	https://huggingface.co/datasets/SaintSpirit/bermejo/resolve/main/README.md	--- license: cc-by-nc-4.0 ---
Konst12	null	null	null	false	null	false	Konst12/1	2022-11-04T20:48:30.000Z	null	false	2c8f74168f56780085332ace10a118fea40220fa	[]	[]	https://huggingface.co/datasets/Konst12/1/resolve/main/README.md
VENF	null	null	null	false	null	false	VENF/me	2022-10-03T17:47:18.000Z	null	false	01b1e39f74eed0e5af70b76140f91f0311aa7ade	[]	[ "license:openrail" ]	https://huggingface.co/datasets/VENF/me/resolve/main/README.md	--- license: openrail ---
12f23eddde	null	null	null	false	null	false	12f23eddde/tester	2022-10-03T17:55:57.000Z	null	false	5081c990e242f1011cc3d5ad87f6b9436502217d	[]	[]	https://huggingface.co/datasets/12f23eddde/tester/resolve/main/README.md	## AAA AAA AAA ### AAA AAA
Santta	null	null	null	false	null	false	Santta/SantasDB	2022-10-04T14:46:20.000Z	null	false	1831a38f305741dc7790ace1f2142838a18c8a56	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/Santta/SantasDB/resolve/main/README.md	--- license: afl-3.0 ---
RamAnanth1	null	null	null	false	1	false	RamAnanth1/lex-fridman-podcasts	2022-10-03T19:27:31.000Z	null	false	0257536498a35b6698026c7e48cf75ecc274bad9	[]	[ "lexicap:found", "language:en", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "task_categories:text-classification", "task_categories:text-generation", "task_categories:summarization", "task_ids:sentiment-analysis", "task_ids:dialogue-modeling", "task_ids:lang...	https://huggingface.co/datasets/RamAnanth1/lex-fridman-podcasts/resolve/main/README.md	--- lexicap: - found language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: 'Lex Fridman Podcasts ' size_categories: - n<1K task_categories: - text-classification - text-generation - summarization task_ids: - sentiment-analysis - dialogue-modeling - language-modeling --- # Dataset Card for Lex Fridman Podcasts Dataset This dataset is sourced from Andrej Karpathy's [Lexicap website](https://karpathy.ai/lexicap/) which contains English transcripts of Lex Fridman's wonderful podcast episodes. The transcripts were generated using OpenAI's large-sized [Whisper model]("https://github.com/openai/whisper")
LiveEvil	null	null	null	false	null	false	LiveEvil/deepfacev1	2022-10-03T19:05:59.000Z	null	false	e4c709f0aabb87a51a775f9319d3ee919cbe40d6	[]	[ "license:mit" ]	https://huggingface.co/datasets/LiveEvil/deepfacev1/resolve/main/README.md	--- license: mit ---
benlipkin	null	null	null	false	1	false	benlipkin/rnng-brainscore	2022-11-09T15:02:11.000Z	null	false	3f828259fe9e47479be8a275f40368d37c42b1e7	[]	[ "license:mit" ]	https://huggingface.co/datasets/benlipkin/rnng-brainscore/resolve/main/README.md	--- license: mit --- Pre-trained models and other files associated with the RNNG BrainScore repo. Check out the GitHub at https://github.com/benlipkin/rnng
Hamiltonhog	null	null	null	false	null	false	Hamiltonhog/Dalap	2022-10-03T20:10:33.000Z	null	false	1c4a8df556d922bfc7f65cbfdf2b3d9804a69052	[]	[ "license:other" ]	https://huggingface.co/datasets/Hamiltonhog/Dalap/resolve/main/README.md	--- license: other ---
khaclinh	null	@article{PP4AV2022, title = {PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving}, author = {Linh Trinh, Phuong Pham, Hoang Trinh, Nguyen Bach, Dung Nguyen, Giang Nguyen, Huy Nguyen}, booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, year = {2023} }	PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.	false	19	false	khaclinh/pp4av	2022-10-26T04:19:10.000Z	null	false	bcb26e69554574d87cc8286ed42b028183d0fc55	[]	[ "annotations_creators:expert-generated", "language_creators:found", "language:en", "license:cc-by-nc-nd-4.0", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended", "task_categories:object-detection", "task_ids:face-detection", "tags:license-plate-detection" ]	https://huggingface.co/datasets/khaclinh/pp4av/resolve/main/README.md	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-nd-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended task_categories: - object-detection task_ids: - face-detection pretty_name: PP4AV tags: - license-plate-detection --- # Dataset Card for PP4AV ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Annotations](#annotations) - [Dataset folder](#folder) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [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) - [Baseline Model](#baseline-model) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/khaclinh/pp4av - Repository: https://github.com/khaclinh/pp4av - Baseline model: https://huggingface.co/spaces/khaclinh/self-driving-anonymization - Paper: [PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving] - Point of Contact: linhtk.dhbk@gmail.com ### Dataset Summary PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving. ### Languages English ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. The source data from 6 cities in European was described as follow: - `Paris`: This subset contains 1450 images of the car driving down a Parisian street during the day. The video frame rate is 30 frames per second. The video is longer than one hour. We cut a shorter video for sampling and annotation. The original video can be found at the following URL: URL: [paris_youtube_video](https://www.youtube.com/watch?v=nqWtGWymV6c) - `Netherland day time`: This subset consists of 388 images of Hague, Amsterdam city in day time. The image of this subset are sampled from the bellow original video: URL: [netherland_youtube_video](https://www.youtube.com/watch?v=Xuo4uCZxNrE) The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour. - `Netherland night time`: This subset consists of 824 images of Hague, Amsterdam city in night time sampled by the following original video: URL: [netherland_youtube_video](https://www.youtube.com/watch?v=eAy9eHsynhM) The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour. - `Switzerland`: This subset consists of 372 images of Switzerland sampled by the following video: URL: [switzerland_youtube_video](https://www.youtube.com/watch?v=0iw5IP94m0Q) The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than one hour. - `Zurich`: This subset consists of 50 images of Zurich city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3) - `Stuttgart`: This subset consists of 69 images of Stuttgart city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3) - `Strasbourg`: This subset consists of 50 images of Strasbourg city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3) We use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. The source of fisheye data for sampling is located at WoodScape's [Fisheye images](https://woodscape.valeo.com/download). In total, 3,447 images were selected and annotated in PP4AV. ### Annotations #### Annotation process Annotators annotate facial and license plate objects in images. For facial objects, bounding boxes are defined by all detectable human faces from the forehead to the chin to the ears. Faces were labelled with diverse sizes, skin tones, and faces partially obscured by a transparent material, such as a car windshield. For license plate objects, bounding boxes consists of all recognizable license plates with high variability, such as different sizes, countries, vehicle types (motorcycle, automobile, bus, truck), and occlusions by other vehicles. License plates were annotated for vehicles involved in moving traffic. To ensure the quality of annotation, there are two-step process for annotation. In the first phase, two teams of annotators will independently annotate identical image sets. After their annotation output is complete, a merging method based on the IoU scores between the two bounding boxes of the two annotations will be applied. Pairs of annotations with IoU scores above a threshold will be merged and saved as a single annotation. Annotated pairs with IoU scores below a threshold will be considered conflicting. In the second phase, two teams of reviewers will inspect the conflicting pairs of annotations for revision before a second merging method similar to the first is applied. The results of these two phases will be combined to form the final annotation. All work is conducted on the CVAT tool https://github.com/openvinotoolkit/cvat. #### Who are the annotators? Vantix Data Science team ### Dataset Folder The `data` folder contains below files: - `images.zip`: contains all preprocessed images of PP4AV dataset. In this `zip` file, there are bellow folder included: `fisheye`: folder contains 244 fisheye images in `.png` file type `zurich`: folder contains 244 fisheye images in `.png` file type `strasbourg`: folder contains 244 fisheye images in `.png` file type `stuttgart`: folder contains 244 fisheye images in `.png` file type `switzerland`: folder contains 244 fisheye images in `.png` file type `netherlands_day`: folder contains 244 fisheye images in `.png` file type `netherlands_night`: folder contains 244 fisheye images in `.png` file type `paris`: folder contains 244 fisheye images in `.png` file type - `annotations.zip`: contains annotation data corresponding to `images.zip` data. In this file, there are bellow folder included: `fisheye`: folder contains 244 annotation `.txt` file type for fisheye image following `yolo v1.1` format. `zurich`: folder contains 50 file `.txt` annotation following `yolo v1.1` format, which corresponding to 50 images file of `zurich` subset. `strasbourg`: folder contains 50 file `.txt` annotation following `yolo v1.1` format, which corresponding to 50 images file of `strasbourg` subset. `stuttgart`: folder contains 69 file `.txt` annotation following `yolo v1.1` format, which corresponding to 69 images file of `stuttgart` subset. `switzerland`: folder contains 372 file `.txt` annotation following `yolo v1.1` format, which corresponding to 372 images file of `switzerland` subset. `netherlands_day`: folder contains 388 file `.txt` annotation following `yolo v1.1` format, which corresponding to 388 images file of `netherlands_day` subset. `netherlands_night`: folder contains 824 file `.txt` annotation following `yolo v1.1` format, which corresponding to 824 images file of `netherlands_night` subset. `paris`: folder contains 1450 file `.txt` annotation following `yolo v1.1` format, which corresponding to 1450 images file of `paris` subset. - `soiling_annotations.zip`: contain raw annotation data without filtering. The folder structure stored in this file is similar to format of `annotations.zip`. ### Personal and Sensitive Information [More Information Needed] ## Dataset Structure ### Data Instances A data point comprises an image and its face and license plate annotations. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x19FA12186D8>, 'objects': { 'bbox': [ [0 0.230078 0.317081 0.239062 0.331367], [1 0.5017185 0.0306425 0.5185935 0.0410975], [1 0.695078 0.0710145 0.7109375 0.0863355], [1 0.4089065 0.31646 0.414375 0.32764], [0 0.1843745 0.403416 0.201093 0.414182], [0 0.7132 0.3393474 0.717922 0.3514285] ] } } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `objects`: a dictionary of face and license plate bounding boxes present on the image - `bbox`: the bounding box of each face and license plate (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format). Basically, each row in annotation `.txt` file for each image `.png` file consists of data in format: `<object-class> <x_center> <y_center> <width> <height>`: - `object-class`: integer number of object from 0 to 1, where 0 indicate face object, and 1 indicate licese plate object - `x_center`: normalized x-axis coordinate of the center of the bounding box. `x_center = <absolute_x_center> / <image_width>` - `y_center`: normalized y-axis coordinate of the center of the bounding box. `y_center = <absolute_y_center> / <image_height>` - `width`: normalized width of the bounding box. `width = <absolute_width> / <image_width>` - `height`: normalized wheightdth of the bounding box. `height = <absolute_height> / <image_height>` - Example lines in YOLO v1.1 format `.txt' annotation file: `1 0.716797 0.395833 0.216406 0.147222 0 0.687109 0.379167 0.255469 0.158333 1 0.420312 0.395833 0.140625 0.166667 ` ## 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 ### Baseline Model Pretrained weight and demo of baseline model are available in [self-driving-anonymization huggingface spaces](https://huggingface.co/spaces/khaclinh/self-driving-anonymization) ### Dataset Curators Linh Trinh ### Licensing Information [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/). ### Citation Information ``` @article{PP4AV2022, title = {PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving}, author = {Linh Trinh, Phuong Pham, Hoang Trinh, Nguyen Bach, Dung Nguyen, Giang Nguyen, Huy Nguyen}, booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, year = {2023} } ``` ### Contributions Thanks to [@khaclinh](https://github.com/khaclinh) for adding this dataset.
amynechiban	null	null	null	false	null	false	amynechiban/chibano	2022-10-03T20:46:48.000Z	null	false	89493c7c73b6191186ce3f5ea92a3b9d2398cc91	[]	[ "license:openrail" ]	https://huggingface.co/datasets/amynechiban/chibano/resolve/main/README.md	--- license: openrail ---
suresh-subramanian	null	null	null	false	null	false	suresh-subramanian/autotrain-data-fake-news	2022-10-03T22:04:02.000Z	null	false	c9f2154be6ce8a9b9c3b6dd00b05ca4117a5e400	[]	[ "language:en", "task_categories:text-classification" ]	https://huggingface.co/datasets/suresh-subramanian/autotrain-data-fake-news/resolve/main/README.md	--- language: - en task_categories: - text-classification --- # AutoTrain Dataset for project: fake-news ## Dataset Description This dataset has been automatically processed by AutoTrain for project fake-news. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_author": "Brett Macdonald", "feat_published": "2016-10-28T00:58:00.000+03:00", "feat_title": "breaking hillary just lost the black vote trump is going all the way to the white house", "text": "dean james americas freedom fighters \nlast week the pentagon issued a defense department directive that allows department of defense dd personnel to carry firearms and employ deadly force while performing official duties \nthe defense department has been working on changing the gunfree zones on domestic military basis for several years in light of the deadly shootings at military sites in recent years \nmilitarycom reports that the directive also provides detailed guidance to the services for permitting soldiers sailors airmen marines and coast guard personnel to carry privately owned firearms on dod property it authorizes commanders and aboveto grant permission to dod personnel requesting to carry a privately owned firearm concealed or open carry on dod property for a personal protection purpose not related to performance of an official duty or status \nthe directive also makes clear that dod will consider further changes to grant standard authorizations for other dod personnel who are trained in the scaled use of force or who have been previously qualified to use a governmentissued firearm to carry a firearm in the performance of official duties on dod property this would allow dod with certain combat training to carry firearms without going through the additional step of making application with a commander \nkim smith at conservative tribune notes that the policy was a response to an nrabacked provision in the national defense authorization act that required the defense department to allow more service members to carry firearms on base \nit is a good first step in that it recognizes personal protection is a valid issue for service members but there are many roadblocks in the way of making that option available nra spokeswoman jennifer baker told the washington free beacon \nthose wishing to apply for permission to carry a firearm must be at least years old and meet all federal state and local laws the directive said \nit would appear that the pentagon saw no problems with implementing a policy for which presidentelect donald trump has expressed support \npresidentelect donald trump ran on removing gunfree zones from military bases on july breitbart news reported that trump pledged to end the gunfree scenarios for us troops by mandating that soldiers remain armed and on alert at our military bases \nthe immediate institution of this directive probably left president barack obama incensed but he undoubtedly realized that there was nothing he could do to prevent its implementation in a couple of months anyway and thats good news because it works to ensure the safety of our troops which should always be a priority \nlet us know what you think about this in the comments below \ngod bless", "feat_language": "english", "feat_site_url": "americasfreedomfighters.com", "feat_main_img_url": "http://www.americasfreedomfighters.com/wp-content/uploads/2016/10/22-1.jpg", "feat_type": "bs", "target": 0, "feat_title_without_stopwords": "breaking hillary lost black vote trump going way white house", "feat_text_without_stopwords": "dean james americas freedom fighters last week pentagon issued defense department directive allows department defense dd personnel carry firearms employ deadly force performing official duties defense department working changing gunfree zones domestic military basis several years light deadly shootings military sites recent years militarycom reports directive also provides detailed guidance services permitting soldiers sailors airmen marines coast guard personnel carry privately owned firearms dod property authorizes commanders aboveto grant permission dod personnel requesting carry privately owned firearm concealed open carry dod property personal protection purpose related performance official duty status directive also makes clear dod consider changes grant standard authorizations dod personnel trained scaled use force previously qualified use governmentissued firearm carry firearm performance official duties dod property would allow dod certain combat training carry firearms without going additional step making application commander kim smith conservative tribune notes policy response nrabacked provision national defense authorization act required defense department allow service members carry firearms base good first step recognizes personal protection valid issue service members many roadblocks way making option available nra spokeswoman jennifer baker told washington free beacon wishing apply permission carry firearm must least years old meet federal state local laws directive said would appear pentagon saw problems implementing policy presidentelect donald trump expressed support presidentelect donald trump ran removing gunfree zones military bases july breitbart news reported trump pledged end gunfree scenarios us troops mandating soldiers remain armed alert military bases immediate institution directive probably left president barack obama incensed undoubtedly realized nothing could prevent implementation couple months anyway thats good news works ensure safety troops always priority let us know think comments god bless", "feat_hasImage": 1.0 }, { "feat_author": "Joel Ross Taylor", "feat_published": "2016-10-26T22:46:37.443+03:00", "feat_title": "no title", "text": "announcement \nthe wrh server continues to be under intense attack by hillarys tantrum squad \nbut the site keeps bouncing back so if during the day you cannot connect wait a minute or two and try again thank you for your patience it is obvious the bad guys are in a state of total panic to act like this thought for the day we seek peace knowing that peace is the climate of freedom dwight d eisenhower your random dhs monitored phrase of the day dera \npaid advertising at what really happened may not represent the views and opinions of this website and its contributors no endorsement of products and services advertised is either expressed or implied \nhillary the spy updated info \nlet us start with an historical fact treason and betrayal by the highest levels is a common feature of history whether it is judas vs jesus brutus vs julius caesar benedict arnold the rosenbergs jonathan pollard aldrich ames robert hanssen it is just a fact of life it does happen \nback in when bill clinton was running for reelection he authorized the transfer of highly sensitive technology to china this technology had military applications and allowed china to close the gap in missile performance with the united states the transfers were opposed and severely criticized by the defense department \nat the same time bill clinton was transferring this technology to china huge donations began to pour into his reelection campaign from the us companies allowed to sell the technology to china and from american citizens of chinese descent the fact that they were us citizens allowed them to donate to political campaigns but it later emerged that they were acting as conduits for cash coming in from asian sources including chinese intelligence agencies the scandal eventually became known as chinagate \njohn huang \na close associate of indonesian industrialist james riady huang initially was appointed deputy secretary of commerce in by however he moved to the democratic national committee where he generated hundreds of thousands of dollars in illegal contributions from foreign sources huang later pleaded guilty to one felony count of campaign finance violations \ncharlie trie \nlike john huang trie raised hundreds of thousands of dollars in illegal contributions from foreign sources to democratic campaign entities he was a regular white house visitor and arranged meetings of foreign operators with clinton including one who was a chinese arms dealer his contribution to clintons legal defense fund was returned after it was found to have been largely funded by asian interests trie was convicted of violating campaign finance laws in \none of tries main sources of cash was chinese billionaire ng lap seng according to a senate report ng lap seng had connections to the chinese government seng was arrested in over an unrelated bribery case but this gave investigators the opportunity to question seng about the chinagate scandal former united nations general assembly president john ashe was also caught in the bribery case and was about to testify to the links between the clintons and seng when he was found dead that very morning initially reported as having died from a heart attack johns throat had obviously been crushed at that point the official story changed to him accidentally dropping a barbell on his own throat \nng lap seng with the clintons \njohnny chung \ngave more than to the democratic national committee prior to the campaign but it was returned after officials learned it came from illegal foreign sources chung later told a special senate committee investigating clinton campaign fundraising that of his contributions came from individuals in chinese intelligence chung pleaded guilty to bank fraud tax evasion and campaign finance violations \nchinagate documented by judicial watch was uncovered by judicial watch founder larry klayman technology companies allegedly made donations of millions of dollars to various democratic party entities including president bill clintons reelection campaign in return for permission to sell hightech secrets to china bernard schwartz and his loral space communication ltd later allegedly helped china to identify the cause of a rocket failure thereby advancing chinas missile program and threatening us national security according to records \nthis establishes a history of the clintons treating us secrets as their own personal property and selling them to raise money for campaigns \nis history repeating itself it appears so \nlet us consider a private email server with weak security at least one known totally open access point no encryption at all and outside the control and monitoring systems of the us government on which are parked many of the nations most closely guarded secrets as well as those of the united nations and other foreign governments it is already established that hillarys email was hacked one hacker named guccifer provided copies of emails to russia today which published them", "feat_language": "english", "feat_site_url": "westernjournalism.com", "feat_main_img_url": "http://static.westernjournalism.com/wp-content/uploads/2016/10/earnest-obama.jpg", "feat_type": "bias", "target": 1, "feat_title_without_stopwords": "title", "feat_text_without_stopwords": "maggie hassan left kelly ayotte hassan declares victory us senate race ayotte paul feelynew hampshire union leader update gov maggie hassan declared shes new hampshires us senate race unseating republican sen kelly ayotteduring hastilycalled press conference outside state house hassan said shes ahead enough votes survive returns outstanding towns lefti proud stand next united states senator new hampshire hassan said cheers large group supporters led congresswoman annie kuster hassans husband tomthe twoterm governor said hadnt spoken ayotteits clear maintained lead race hassan saidsen ayotte issued brief statement hassans event concede deferred secretary state bill gardners final resultsthis closely contested race beginning look forward results announced secretary state ensuring every vote counted race received historic level interest ayotte saidhassan said called congratulate govelect chris sununu newfields republican vowed work together smooth transition power states corner officewith percent vote counted hassan led ayotte nashua republican votes much less percent two voting precincts left reporta recount statewide race seems like real possibility margin small enough ayotte pay earlier story follows concord republican incumbent sen kelly ayotte told supporters early wednesday feeling really upbeat chances one closely watched expensive us senate races country wasnt ready claim victory democratic challenger gov maggie hassan earn return washington representing granite stateat ayotte took podium grappone conference center concord address supporters victory party dead heat hassan percent percent votes votes percent precincts state reportingjoe excited see tonight said ayotte feel really upbeat tonightayotte went thank supporters next gov sununuwe know hard worked grateful humbled fact would believe us right upbeat race believe strongly fact want every vote come talk every vote matters every person matters stategov hassan said race close call campaign maintained vote lead according numbers compiled staffwe still small sustainable lead saidhassan told crowd number smaller towns yet report numbers confident lead would hold campaign said numbers show hassan vote ayottes percent vote campaign said numbers include results big communities associated press yet count like salem derry lebanon portsmouth cities manchester nashua concord included hassan numbersthe governor headed home night urged supporters go home get sleepelection day marked end long campaign cycle granite state kicked nine months ago presidential primaries nine months ago didnt let final ballots cast around pm tuesdaythe ayottehassan contest expensive political race ever new hampshire million spent took center stage cycle alongside presidential race republican nominee donald trump democratic nominee hillary clinton cementing new hampshires status battleground state four electoral votes grabs race one half dozen around us closely watched tuesday outcome likely playing part deciding republicans retain control senate democrats regain majority lost two years agoit great night republicans new hampshire across country said nh gop chair jennifer horn new hampshire know republicans stand together republicans fight together win", "feat_hasImage": 1.0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_author": "Value(dtype='string', id=None)", "feat_published": "Value(dtype='string', id=None)", "feat_title": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "feat_language": "Value(dtype='string', id=None)", "feat_site_url": "Value(dtype='string', id=None)", "feat_main_img_url": "Value(dtype='string', id=None)", "feat_type": "Value(dtype='string', id=None)", "target": "ClassLabel(num_classes=2, names=['Fake', 'Real'], id=None)", "feat_title_without_stopwords": "Value(dtype='string', id=None)", "feat_text_without_stopwords": "Value(dtype='string', id=None)", "feat_hasImage": "Value(dtype='float64', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 1639 \| \| valid \| 411 \|
UnknownBot	null	null	null	false	1	false	UnknownBot/Tobys-Lively-Tunes	2022-10-04T02:24:01.000Z	null	false	1dfeec0b7c8bf55da1c38d1ea6cf3c0aadb09dc8	[]	[ "license:gpl-3.0" ]	https://huggingface.co/datasets/UnknownBot/Tobys-Lively-Tunes/resolve/main/README.md	--- license: gpl-3.0 ---
Kesin60	null	null	null	false	1	false	Kesin60/vico	2022-10-04T03:36:00.000Z	null	false	b4fbee715bae2f34da0c0c88f85ac54b508e59be	[]	[ "license:openrail" ]	https://huggingface.co/datasets/Kesin60/vico/resolve/main/README.md	--- license: openrail ---
zcw607	null	null	null	false	1	false	zcw607/dj_piggy	2022-10-04T04:45:39.000Z	null	false	67fbe7fc1598373f0f81d1b5192ac8d424f0e94a	[]	[ "license:mit" ]	https://huggingface.co/datasets/zcw607/dj_piggy/resolve/main/README.md	--- license: mit ---
Drewd	null	null	This new dataset is meant to fine tune a model on how lex would talk. It's meant to support Q+A style models as well as encoders.	false	1	false	Drewd/lex_fridman_podcast_transcripts	2022-10-05T01:41:30.000Z	null	false	a60d8420a2e5ec790b23d3a6349f6cdb9a1b7934	[]	[ "annotations_creators:found", "language:en", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "tags:podcast", "tags:ai", "tags:interviews" ]	https://huggingface.co/datasets/Drewd/lex_fridman_podcast_transcripts/resolve/main/README.md	--- annotations_creators: - found language: - en language_creators: - machine-generated license: [] multilinguality: - monolingual pretty_name: The transcripts from Lex Fridman podcast episodes on Youtube. size_categories: - n<1K source_datasets: [] tags: - podcast - ai - interviews task_categories: [] task_ids: [] --- # Dataset Card for Lex Fridman Podcast Transcripts ## Table of Contents - [Dataset Card for Lex Fridman Podcast Transcripts](#dataset-card-for-lex-fridman-podcast-transcripts) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://karpathy.ai/lexicap/ - Repository: - Paper: - Leaderboard: - Point of Contact: [@drewdresser](https://twitter.com/drewdresser) ### Dataset Summary These are transcripts from the Lex Fridman podcast. The podcast is hosted by Lex Fridman, a computer scientist at MIT. The podcast is a mix of interviews with researchers in AI and other fields, and discussions of current events in AI. The transcripts are generated using [OpenAI Whisper](https://github.com/openai/whisper), then made available on [Karpathy AI](https://karpathy.ai/lexicap/). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances ~325 ### 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.
pedroluizmossi	null	null	null	false	1	false	pedroluizmossi/teste	2022-10-04T04:09:09.000Z	null	false	c4af5b6ba50b638b5c7a9eb2f6005da650dc43e7	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/pedroluizmossi/teste/resolve/main/README.md	--- license: afl-3.0 ---
alexoamber	null	null	null	false	1	false	alexoamber/testing	2022-10-04T06:15:07.000Z	null	false	63ecef72baeb35040818d19a131488f55a63ea48	[]	[ "license:afl-3.0" ]	https://huggingface.co/datasets/alexoamber/testing/resolve/main/README.md	--- license: afl-3.0 ---
ImageIN	null	null	null	false	1	false	ImageIN/IA_loaded	2022-10-13T09:07:00.000Z	null	false	5157534de40e425f8c719ee7bac7c51cdebfcef9	[]	[]	https://huggingface.co/datasets/ImageIN/IA_loaded/resolve/main/README.md
shjwudp	null	null	shu is a chinese book dataset.	false	5	false	shjwudp/shu	2022-10-06T07:14:04.000Z	null	false	8e9cadb54275dbc5abf31a1a1f7f14e9b3d87e9c	[]	[ "language:zh", "license:mit" ]	https://huggingface.co/datasets/shjwudp/shu/resolve/main/README.md	--- language: zh license: mit --- The dataset constructed from Chinese books. Is still collecting corpus, welcome to join the construction! This repo is only for publication, data errors and corpus contributions, please submit an issue on github https://github.com/shjwudp/shu.
chamuditha	null	null	null	false	1	false	chamuditha/szasw	2022-10-04T07:41:39.000Z	null	false	90cf503c83a03984f6f2a6750639c7f58a0833d5	[]	[]	https://huggingface.co/datasets/chamuditha/szasw/resolve/main/README.md	5381607451 oya clne eke lidar rp gahala dennam kiyala gaththa echchrama thama oyata mathaka athi uwa
azuu	null	null	null	false	1	false	azuu/testing	2022-10-04T07:52:12.000Z	null	false	37d2038fba67e97e448c9e984ade602ae317c533	[]	[ "license:apache-2.0" ]	https://huggingface.co/datasets/azuu/testing/resolve/main/README.md	--- license: apache-2.0 ---
youngdicey	null	null	null	false	1	false	youngdicey/rico-raw	2022-10-05T08:58:04.000Z	null	false	568933cffcef96b919c9f8ddc566fb85f74e5a86	[]	[ "license:openrail" ]	https://huggingface.co/datasets/youngdicey/rico-raw/resolve/main/README.md	--- license: openrail ---
youngdicey	null	null	null	false	1	false	youngdicey/sample	2022-10-05T05:26:39.000Z	null	false	3924c3784902b37fa27585e6f58905369e79a451	[]	[ "license:openrail" ]	https://huggingface.co/datasets/youngdicey/sample/resolve/main/README.md	--- license: openrail ---
autoevaluate	null	null	null	false	1	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758608	2022-10-04T09:42:23.000Z	null	false	95dd4ccbc4bc09e0c99e374f99a1e15f444acaf5	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758608/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: ChuVN/longformer-base-4096-finetuned-squad2-length-1024-128window metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: ChuVN/longformer-base-4096-finetuned-squad2-length-1024-128window * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758610	2022-10-04T09:28:54.000Z	null	false	fdbfb7c35482e11fbaeab6d4905b2679327a19b3	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758610/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Palak/xlm-roberta-base_squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Palak/xlm-roberta-base_squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	1	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758611	2022-10-04T09:28:50.000Z	null	false	0be5cbce4748125b4f1860a3dc90f2c89a852321	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758611/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: SupriyaArun/bert-base-uncased-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: SupriyaArun/bert-base-uncased-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	1	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758612	2022-10-04T09:28:54.000Z	null	false	e0fb058fe85c2d3d6f9135ff6400df42f646fdda	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758612/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: SiraH/bert-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: SiraH/bert-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	1	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758613	2022-10-04T09:29:08.000Z	null	false	83e32e07ee901f7b6153c3e0d607086b71f0c5cc	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758613/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Paul-Vinh/bert-base-multilingual-cased-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Paul-Vinh/bert-base-multilingual-cased-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758614	2022-10-04T09:30:00.000Z	null	false	9f16d72a6db9ff9c6d67d92f2cea347459a05362	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758614/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Palak/microsoft_deberta-base_squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Palak/microsoft_deberta-base_squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	1	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758615	2022-10-04T09:28:41.000Z	null	false	02c96fa323a571539245c92428dc06a7e0da1cd1	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758615/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Sangita/distilbert-base-uncased-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Sangita/distilbert-base-uncased-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	1	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758616	2022-10-04T09:28:48.000Z	null	false	5e638585d3d005f0fbbcc40471618f1d39c25c1a	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758616/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Plimpton/distilbert-base-uncased-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Plimpton/distilbert-base-uncased-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
autoevaluate	null	null	null	false	null	false	autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758617	2022-10-04T09:29:27.000Z	null	false	fcc9866d0841a9d1eac276f2a53d0d9c5c584ad3	[]	[ "type:predictions", "tags:autotrain", "tags:evaluation", "datasets:squad_v2" ]	https://huggingface.co/datasets/autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758617/resolve/main/README.md	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Neulvo/bert-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Neulvo/bert-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
Gr3en	null	null	null	false	null	false	Gr3en/Goebbels_Liberte_daction	2022-10-04T09:44:35.000Z	null	false	b0e4884ec8ea6ef65e22f7409f3962060c4ae169	[]	[]	https://huggingface.co/datasets/Gr3en/Goebbels_Liberte_daction/resolve/main/README.md	annotations_creators: - no-annotation language: - en language_creators: - other license: - artistic-2.0 multilinguality: - monolingual pretty_name: "Libert\xE8 d'action by Heiner Goebbels" size_categories: - n<1K source_datasets: - original tags: [] task_categories: - text-to-image task_ids: []
Besedo	null	null	null	false	4	false	Besedo/artificial_weapon	2022-10-04T12:24:34.000Z	null	false	6dfd409e61158ef29abfcc842f77136121575c8c	[]	[ "annotations_creators:machine-generated", "language_creators:machine-generated", "size_categories:1K<n<10K", "tags:weapon", "tags:image", "task_categories:image-classification" ]	https://huggingface.co/datasets/Besedo/artificial_weapon/resolve/main/README.md	--- annotations_creators: - machine-generated language: [] language_creators: - machine-generated license: [] multilinguality: [] pretty_name: artificial_weapon size_categories: - 1K<n<10K source_datasets: [] tags: - weapon - image task_categories: - image-classification task_ids: [] --- # 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.
ImageIN	null	null	null	false	4	false	ImageIN/unlabelled_IA_with_snorkel_labels	2022-10-13T09:06:42.000Z	null	false	2417b2b6d421eb45345432b59fcee4f0ba35f076	[]	[ "annotations_creators:machine-generated", "license:cc0-1.0", "tags:lam", "tags:historic", "tags:glam", "tags:books", "task_categories:image-classification", "task_ids:multi-class-image-classification" ]	https://huggingface.co/datasets/ImageIN/unlabelled_IA_with_snorkel_labels/resolve/main/README.md	--- annotations_creators: - machine-generated language: [] language_creators: [] license: - cc0-1.0 multilinguality: [] pretty_name: 'Historic book pages illustration weak annotations' size_categories: [] source_datasets: [] tags: - lam - historic - glam - books task_categories: - image-classification task_ids: - multi-class-image-classification --- # Historic book pages illustration weak annotations

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