nkasmanoff/huggingface-datasets · Datasets at Hugging Face

id stringlengths 2 115	private bool 1 class	tags sequence	description stringlengths 0 5.93k ⌀	downloads int64 0 1.14M	likes int64 0 1.79k
wisesight1000	false	[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:extended\|wisesight_sentiment", "language:th", "license:cc0-1.0", "word-tokenization" ]	`wisesight1000` contains Thai social media texts randomly drawn from the full `wisesight-sentiment`, tokenized by human annotators. Out of the labels `neg` (negative), `neu` (neutral), `pos` (positive), `q` (question), 250 samples each. Some texts are removed because they look like spam.Because these samples are representative of real world content, we believe having these annotaed samples will allow the community to robustly evaluate tokenization algorithms.	269	0
wisesight_sentiment	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:th", "license:cc0-1.0" ]	Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question) * Released to public domain under Creative Commons Zero v1.0 Universal license. * Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3} * Size: 26,737 messages * Language: Central Thai * Style: Informal and conversational. With some news headlines and advertisement. * Time period: Around 2016 to early 2019. With small amount from other period. * Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. * Privacy: * Only messages that made available to the public on the internet (websites, blogs, social network sites). * For Facebook, this means the public comments (everyone can see) that made on a public page. * Private/protected messages and messages in groups, chat, and inbox are not included. * Alternations and modifications: * Keep in mind that this corpus does not statistically represent anything in the language register. * Large amount of messages are not in their original form. Personal data are removed or masked. * Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. (Mis)spellings are kept intact. * Messages longer than 2,000 characters are removed. * Long non-Thai messages are removed. Duplicated message (exact match) are removed. * More characteristics of the data can be explore: https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb	589	4
wmt14	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|giga_fren", "source_datasets:extended\|news_commentary", "source_datasets:extended\|un_multi", "source_datasets:extended\|hind_encorp", "language:cs", "language:de", "language:en", "language:fr", "language:hi", "language:ru", "license:unknown" ]	null	4,869	2
wmt15	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|giga_fren", "source_datasets:extended\|news_commentary", "source_datasets:extended\|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:fr", "language:ru", "license:unknown" ]	null	851	1
wmt16	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|news_commentary", "source_datasets:extended\|setimes", "source_datasets:extended\|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:ro", "language:ru", "language:tr", "license:unknown" ]	null	30,391	9
wmt17	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|news_commentary", "source_datasets:extended\|setimes", "source_datasets:extended\|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:lv", "language:ru", "language:tr", "language:zh", "license:unknown" ]	null	1,316	1
wmt18	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|news_commentary", "source_datasets:extended\|opus_paracrawl", "source_datasets:extended\|setimes", "source_datasets:extended\|un_multi", "language:cs", "language:de", "language:en", "language:et", "language:fi", "language:kk", "language:ru", "language:tr", "language:zh", "license:unknown" ]	null	1,373	3
wmt19	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|news_commentary", "source_datasets:extended\|opus_paracrawl", "source_datasets:extended\|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:fr", "language:gu", "language:kk", "language:lt", "language:ru", "language:zh", "license:unknown" ]	null	2,775	9
wmt20_mlqe_task1	false	[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended\|reddit", "source_datasets:extended\|wikipedia", "language:de", "language:en", "language:et", "language:ne", "language:ro", "language:ru", "language:si", "language:zh", "license:unknown" ]	This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.	1,190	1
wmt20_mlqe_task2	false	[ "task_categories:translation", "task_categories:text-classification", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended\|wikipedia", "language:de", "language:en", "language:zh", "license:unknown", "translation-quality-estimation", "arxiv:1902.08646" ]	This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. Task 2 evaluates the application of QE for post-editing purposes. It consists of predicting: - A/ Word-level tags. This is done both on source side (to detect which words caused errors) and target side (to detect mistranslated or missing words). - A1/ Each token is tagged as either `OK` or `BAD`. Additionally, each gap between two words is tagged as `BAD` if one or more missing words should have been there, and `OK` otherwise. Note that number of tags for each target sentence is 2*N+1, where N is the number of tokens in the sentence. - A2/ Tokens are tagged as `OK` if they were correctly translated, and `BAD` otherwise. Gaps are not tagged. - B/ Sentence-level HTER scores. HTER (Human Translation Error Rate) is the ratio between the number of edits (insertions/deletions/replacements) needed and the reference translation length.	481	2
wmt20_mlqe_task3	false	[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended\|amazon_us_reviews", "language:en", "language:fr", "license:unknown" ]	This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.	263	0
wmt_t2t	false	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended\|europarl_bilingual", "source_datasets:extended\|news_commentary", "source_datasets:extended\|opus_paracrawl", "source_datasets:extended\|un_multi", "language:de", "language:en", "license:unknown" ]	null	267	0
wnut_17	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0" ]	WNUT 17: Emerging and Rare entity recognition This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.	3,531	6
wongnai_reviews	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:th", "license:lgpl-3.0" ]	Wongnai's review dataset contains restaurant reviews and ratings, mainly in Thai language. The reviews are in 5 classes ranging from 1 to 5 stars.	473	1
woz_dialogue	false	[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:token-classification", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:multi-class-classification", "task_ids:parsing", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "language:en", "language:it", "license:unknown", "arxiv:1604.04562" ]	Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) that the user can ask a value for once a restaurant has been offered.	805	2
wrbsc	false	[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-3.0" ]	WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators.	263	0
x_stance	false	[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "language:en", "language:fr", "language:it", "license:cc-by-nc-4.0", "stance-detection", "arxiv:2003.08385" ]	The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems.	273	3
xcopa	false	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended\|copa", "language:et", "language:ht", "language:id", "language:it", "language:qu", "language:sw", "language:ta", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-4.0" ]	XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the creation of XCOPA and the implementation of the baselines are available in the paper.\n	9,049	2
xcsr	false	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended\|codah", "source_datasets:extended\|commonsense_qa", "language:ar", "language:de", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:ja", "language:nl", "language:pl", "language:pt", "language:ru", "language:sw", "language:ur", "language:vi", "language:zh", "license:mit", "arxiv:2106.06937" ]	To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.	4,337	2
xed_en_fi	false	[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:extended\|other-OpenSubtitles2016", "language:en", "language:fi", "license:cc-by-4.0", "arxiv:2011.01612" ]	A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.	653	4
xglue	false	[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-classification", "task_categories:text2text-generation", "task_categories:token-classification", "task_ids:acceptability-classification", "task_ids:extractive-qa", "task_ids:named-entity-recognition", "task_ids:natural-language-inference", "task_ids:news-articles-headline-generation", "task_ids:open-domain-qa", "task_ids:parsing", "task_ids:topic-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "annotations_creators:found", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "language_creators:found", "language_creators:machine-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:extended\|conll2003", "source_datasets:extended\|squad", "source_datasets:extended\|xnli", "source_datasets:original", "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh", "license:cc-by-nc-4.0", "license:cc-by-sa-4.0", "license:other", "paraphrase-identification", "question-answering", "arxiv:2004.01401" ]	XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation. The benchmark is composed of the following 11 tasks: - NER - POS Tagging (POS) - News Classification (NC) - MLQA - XNLI - PAWS-X - Query-Ad Matching (QADSM) - Web Page Ranking (WPR) - QA Matching (QAM) - Question Generation (QG) - News Title Generation (NTG) For more information, please take a look at https://microsoft.github.io/XGLUE/.	4,102	13
xnli	false	[ "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh" ]	XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).	43,897	16
xor_tydi_qa	false	[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "source_datasets:extended\|tydiqa", "language:ar", "language:bn", "language:fi", "language:ja", "language:ko", "language:ru", "language:te", "license:mit", "arxiv:2010.11856" ]	XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections. There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.	394	0
xquad	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended\|squad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ro", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "arxiv:1910.11856" ]	XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.	6,695	5
xquad_r	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended\|squad", "source_datasets:extended\|xquad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "arxiv:2004.05484" ]	XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.	3,593	2
xsum	false	[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:unknown", "arxiv:1808.08745" ]	Extreme Summarization (XSum) Dataset. There are three features: - document: Input news article. - summary: One sentence summary of the article. - id: BBC ID of the article.	37,094	23
xsum_factuality	false	[ "task_categories:summarization", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended\|other-xsum", "language:en", "license:cc-by-4.0", "hallucinations" ]	Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. We have crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.	408	3
xtreme	false	[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:token-classification", "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:natural-language-inference", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "multilinguality:translation", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "source_datasets:extended\|xnli", "source_datasets:extended\|paws-x", "source_datasets:extended\|wikiann", "source_datasets:extended\|xquad", "source_datasets:extended\|mlqa", "source_datasets:extended\|tydiqa", "source_datasets:extended\|tatoeba", "source_datasets:extended\|squad", "language:af", "language:ar", "language:bg", "language:bn", "language:de", "language:el", "language:en", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:id", "language:it", "language:ja", "language:jv", "language:ka", "language:kk", "language:ko", "language:ml", "language:mr", "language:ms", "language:my", "language:nl", "language:pt", "language:ru", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:ur", "language:vi", "language:yo", "language:zh", "license:apache-2.0", "license:cc-by-4.0", "license:cc-by-2.0", "license:cc-by-sa-4.0", "license:other", "license:cc-by-nc-4.0", "parallel-sentence-retrieval", "paraphrase-identification", "arxiv:2003.11080" ]	The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa.	42,291	28
yahoo_answers_qa	false	[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended\|other-yahoo-webscope-l6", "language:en", "license:unknown" ]	Yahoo Non-Factoid Question Dataset is derived from Yahoo's Webscope L6 collection using machine learning techiques such that the questions would contain non-factoid answers.The dataset contains 87,361 questions and their corresponding answers. Each question contains its best answer along with additional other answers submitted by users. Only the best answer was reviewed in determining the quality of the question-answer pair.	885	8
yahoo_answers_topics	false	[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended\|other-yahoo-answers-corpus", "language:en", "license:unknown" ]	Yahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content and the main category information.	2,492	15
yelp_polarity	false	[ "language:en", "arxiv:1509.01626" ]	Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset_challenge The Yelp reviews polarity dataset is constructed by Xiang Zhang ([email protected]) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). DESCRIPTION The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".	4,529	4
yelp_review_full	false	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:other", "arxiv:1509.01626" ]	The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. The Yelp reviews full star dataset is constructed by Xiang Zhang ([email protected]) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).	18,154	16
yoruba_bbc_topics	false	[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:yo", "license:unknown" ]	A collection of news article headlines in Yoruba from BBC Yoruba. Each headline is labeled with one of the following classes: africa, entertainment, health, nigeria, politics, sport or world. The dataset was presented in the paper: Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (EMNLP 2020).	265	0
yoruba_gv_ner	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:yo", "license:cc-by-3.0" ]	The Yoruba GV NER dataset is a labeled dataset for named entity recognition in Yoruba. The texts were obtained from Yoruba Global Voices News articles https://yo.globalvoices.org/ . We concentrate on four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE]. The Yoruba GV NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme. For more details, see https://www.aclweb.org/anthology/2020.lrec-1.335/	263	0
yoruba_text_c3	false	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:yo", "license:cc-by-nc-4.0" ]	Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/	263	1
yoruba_wordsim353	false	[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:semantic-similarity-scoring", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:en", "language:yo", "license:unknown" ]	A translation of the word pair similarity dataset wordsim-353 to Yorùbá. The dataset was presented in the paper Alabi et al.: Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (LREC 2020).	265	0
youtube_caption_corrections	false	[ "task_categories:other", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:slot-filling", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "token-classification-of-text-errors" ]	Dataset built from pairs of YouTube captions where both 'auto-generated' and 'manually-corrected' captions are available for a single specified language. This dataset labels two-way (e.g. ignoring single-sided insertions) same-length token differences in the `diff_type` column. The `default_seq` is composed of tokens from the 'auto-generated' captions. When a difference occurs between the 'auto-generated' vs 'manually-corrected' captions types, the `correction_seq` contains tokens from the 'manually-corrected' captions.	269	1
zest	false	[ "task_categories:question-answering", "task_categories:token-classification", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "output-structure", "yes-no-qa", "arxiv:2011.08115" ]	ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize in five different ways.	857	1
0n1xus/codexglue	false	[]	CodeXGLUE is a benchmark dataset to foster machine learning research for program understanding and generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison.	467	2
0n1xus/pytorrent-standalone	false	[]	pytorrent-standalone is a subset of the PyTorrent dataset, where only functions that does not depend on external libraries are kept.	299	0
AConsApart/anime_subtitles_DialoGPT	false	[]	null	152	0
AHussain0418/day2_data	false	[]	null	301	0
AHussain0418/day4data	false	[]	null	299	0
AHussain0418/demo_data	false	[]	null	300	0
AI-Sweden/SuperLim	false	[ "task_categories:question-answering", "task_categories:text-classification", "task_categories:other", "multilinguality:monolingual", "language:sv" ]	\	1,934	2
ARKseal/YFCC14M_subset_webdataset	false	[]	null	300	0
ARTeLab/fanpage	false	[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100k", "source_datasets:original", "language:it", "license:unknown" ]	null	329	2
ARTeLab/ilpost	false	[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100k", "language:it" ]	null	307	0
ARTeLab/mlsum-it	false	[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100k", "language:it" ]	null	307	0
ASCCCCCCCC/amazon_zh	false	[ "license:apache-2.0" ]	null	300	1
ASCCCCCCCC/amazon_zh_simple	false	[ "license:apache-2.0" ]	null	302	1
Abdo1Kamr/Arabic_Hadith	false	[]	null	153	0
Abirate/code_net_dataset	false	[]	null	300	1
Abirate/code_net_dev_dataset	false	[]	null	301	0
Abirate/code_net_test_final_dataset	false	[]	null	300	0
Abirate/english_quotes	false	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en" ]	null	2,854	0
Abirate/french_book_reviews	false	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:fr" ]	null	354	2
AdWeeb/DravidianMT	false	[]	null	153	0
Adnan/Urdu_News_Headlines	false	[]	null	153	0
AhmadSawal/qa	false	[]	null	153	0
AhmedSSoliman/CoNaLa	false	[]	null	307	0
Aisha/BAAD16	false	[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:bn", "license:cc-by-4.0", "arxiv:2001.05316" ]	null	299	0
Aisha/BAAD6	false	[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:bn", "license:cc-by-4.0" ]	null	299	0
Akila/ForgottenRealmsWikiDataset	false	[]	null	302	2
Akshith/aa	false	[]	null	153	0
Akshith/g_rock	false	[]	null	153	0
Akshith/test	false	[]	null	301	0
adorkin/extended_tweet_emojis	false	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en" ]	null	267	1
AlekseyKorshuk/comedy-scripts	false	[]	This dataset is designed to generate lyrics with HuggingArtists.	300	1
AlekseyKorshuk/horror-scripts	false	[]	This dataset is designed to generate lyrics with HuggingArtists.	299	1
AlexMaclean/all-deletion-compressions	false	[]	null	300	1
AlexMaclean/wikipedia-deletion-compressions	false	[]	null	300	1
AlexZapolskii/zapolskii-amazon	false	[]	null	299	0
AlgoveraAI/CryptoPunks	false	[]	CryptoPunks is a non-fungible token (NFT) collection on the Ethereum blockchain. The dataset contains 10,000 CryptoPunk images, most of humans but also of three special types: Zombie (88), Ape (24) and Alien (9). They are provided with both clear backgrounds and teal backgrounds.	152	4
Aliseyfi/event_token_type	false	[]	null	149	0
Alvenir/nst-da-16khz	false	[]	null	297	1
AndrewMcDowell/de_corpora_parliament_processed	false	[]	null	297	0
Annabelleabbott/real-fake-news-workshop	false	[]	null	299	0
Annielytics/DoctorsNotes	false	[]	null	150	0
Anurag-Singh-creator/task	false	[]	null	297	0
Anurag-Singh-creator/tasks	false	[]	null	151	0
ApiInferenceTest/asr_dummy	false	[]	Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```	304	0
Arnold/hausa_common_voice	false	[]	null	299	0
AryanLala/autonlp-data-Scientific_Title_Generator	false	[]	null	301	1
Atsushi/fungi_diagnostic_chars_comparison_japanese	false	[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ja", "license:cc-by-4.0" ]	null	297	0
Atsushi/fungi_indexed_mycological_papers_japanese	false	[ "annotations_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ja", "license:cc-by-4.0" ]	null	297	0
Atsushi/fungi_trait_circus_database	false	[ "annotations_creators:other", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:ja", "license:cc-by-4.0" ]	null	300	0
Avishekavi/Avi	false	[]	null	151	0
BSC-LT/SQAC	false	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "arxiv:2107.07253", "arxiv:1606.05250" ]	This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment. The sources of the contexts are: * Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode). * News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/). * Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode). This dataset can be used to build extractive-QA.	297	4
BSC-LT/ancora-ca-ner	false	[ "language:ca" ]	AnCora Catalan NER. This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for Machine Learning and Language Model evaluation purposes. Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB) tags to mark and classify Named Entites. We did not filter out the different categories of NEs from Ancora (weak and strong). We did 6 minor edits by hand. AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence. This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).	297	1
BSC-LT/sts-ca	false	[ "language:ca" ]	Semantic Textual Similarity in Catalan. STS corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. It consists of more than 3000 sentence pairs, annotated with the semantic similarity between them, using a scale from 0 (no similarity at all) to 5 (semantic equivalence). It is done manually by 4 different annotators following our guidelines based on previous work from the SemEval challenges (https://www.aclweb.org/anthology/S13-1004.pdf). The source data are scraped sentences from the Catalan Textual Corpus (https://doi.org/10.5281/zenodo.4519349), used under CC-by-SA-4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/). The dataset is released under the same licence. This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB). This is the version 1.0.2 of the dataset with the complete human and automatic annotations and the analysis scripts. It also has a more accurate license. This dataset can be used to build and score semantic similiarity models.	297	0
BSC-LT/tecla	false	[ "language:ca" ]	TeCla: Text Classification Catalan dataset Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat	297	0
BSC-LT/viquiquad	false	[ "language:ca", "arxiv:1606.05250" ]	ViquiQuAD: an extractive QA dataset from Catalan Wikipedia. This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations) articles in the Catalan Wikipedia "Viquipèdia" (ca.wikipedia.org), and 1 to 5 questions with their answer for each fragment. Viquipedia articles are used under CC-by-sa licence. This dataset can be used to build extractive-QA and Language Models. Funded by the Generalitat de Catalunya, Departament de Polítiques Digitals i Administració Pública (AINA), MT4ALL and Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).	297	0
BSC-LT/xquad-ca	false	[ "language:ca", "arxiv:1910.11856" ]	Professional translation into Catalan of XQuAD dataset (https://github.com/deepmind/xquad). XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Rumanian was added later. We added the 13th language to the corpus using also professional native catalan translators. XQuAD and XQuAD-Ca datasets are released under CC-by-sa licence.	297	0
Babelscape/rebel-dataset	false	[ "task_categories:text-retrieval", "task_categories:text-generation", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "relation-extraction", "conditional-text-generation", "arxiv:2005.00614" ]	REBEL is a silver dataset created for the paper REBEL: Relation Extraction By End-to-end Language generation	317	10
Babelscape/wikineural	false	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "license:cc-by-nc-sa-4.0", "structure-prediction", "arxiv:1810.04805" ]	null	597	8
BatuhanYilmaz/github-issues	false	[]	null	151	0
Baybars/parla_text_corpus	false	[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:various", "multilinguality:monolingual", "size_categories:100k<n<1M", "source_datasets:found", "language:ca", "license:cc-by-4.0", "robust-speech-event" ]	null	297	0
BeIR/beir-corpus	false	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0" ]	null	445	1
BeIR/beir	false	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0" ]	null	603	3

wisesight1000

false

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:extended|wisesight_sentiment", "language:th", "license:cc0-1.0", "word-tokenization" ]

`wisesight1000` contains Thai social media texts randomly drawn from the full `wisesight-sentiment`, tokenized by human annotators. Out of the labels `neg` (negative), `neu` (neutral), `pos` (positive), `q` (question), 250 samples each. Some texts are removed because they look like spam.Because these samples are representative of real world content, we believe having these annotaed samples will allow the community to robustly evaluate tokenization algorithms.

269

0

wisesight_sentiment

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:th", "license:cc0-1.0" ]

Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question) * Released to public domain under Creative Commons Zero v1.0 Universal license. * Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3} * Size: 26,737 messages * Language: Central Thai * Style: Informal and conversational. With some news headlines and advertisement. * Time period: Around 2016 to early 2019. With small amount from other period. * Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. * Privacy: * Only messages that made available to the public on the internet (websites, blogs, social network sites). * For Facebook, this means the public comments (everyone can see) that made on a public page. * Private/protected messages and messages in groups, chat, and inbox are not included. * Alternations and modifications: * Keep in mind that this corpus does not statistically represent anything in the language register. * Large amount of messages are not in their original form. Personal data are removed or masked. * Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. (Mis)spellings are kept intact. * Messages longer than 2,000 characters are removed. * Long non-Thai messages are removed. Duplicated message (exact match) are removed. * More characteristics of the data can be explore: https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb

589

4

wmt14

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|giga_fren", "source_datasets:extended|news_commentary", "source_datasets:extended|un_multi", "source_datasets:extended|hind_encorp", "language:cs", "language:de", "language:en", "language:fr", "language:hi", "language:ru", "license:unknown" ]

null

4,869

2

wmt15

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|giga_fren", "source_datasets:extended|news_commentary", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:fr", "language:ru", "license:unknown" ]

null

851

1

wmt16

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|setimes", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:ro", "language:ru", "language:tr", "license:unknown" ]

null

30,391

9

wmt17

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|setimes", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:lv", "language:ru", "language:tr", "language:zh", "license:unknown" ]

null

1,316

1

wmt18

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|opus_paracrawl", "source_datasets:extended|setimes", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:et", "language:fi", "language:kk", "language:ru", "language:tr", "language:zh", "license:unknown" ]

null

1,373

3

wmt19

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|opus_paracrawl", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:fr", "language:gu", "language:kk", "language:lt", "language:ru", "language:zh", "license:unknown" ]

null

2,775

9

wmt20_mlqe_task1

false

[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended|reddit", "source_datasets:extended|wikipedia", "language:de", "language:en", "language:et", "language:ne", "language:ro", "language:ru", "language:si", "language:zh", "license:unknown" ]

This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.

1,190

1

wmt20_mlqe_task2

false

[ "task_categories:translation", "task_categories:text-classification", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended|wikipedia", "language:de", "language:en", "language:zh", "license:unknown", "translation-quality-estimation", "arxiv:1902.08646" ]

This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. Task 2 evaluates the application of QE for post-editing purposes. It consists of predicting: - A/ Word-level tags. This is done both on source side (to detect which words caused errors) and target side (to detect mistranslated or missing words). - A1/ Each token is tagged as either `OK` or `BAD`. Additionally, each gap between two words is tagged as `BAD` if one or more missing words should have been there, and `OK` otherwise. Note that number of tags for each target sentence is 2*N+1, where N is the number of tokens in the sentence. - A2/ Tokens are tagged as `OK` if they were correctly translated, and `BAD` otherwise. Gaps are not tagged. - B/ Sentence-level HTER scores. HTER (Human Translation Error Rate) is the ratio between the number of edits (insertions/deletions/replacements) needed and the reference translation length.

481

2

wmt20_mlqe_task3

false

[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended|amazon_us_reviews", "language:en", "language:fr", "license:unknown" ]

This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task. The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.

263

0

wmt_t2t

false

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|opus_paracrawl", "source_datasets:extended|un_multi", "language:de", "language:en", "license:unknown" ]

null

267

0

wnut_17

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0" ]

WNUT 17: Emerging and Rare entity recognition This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.

3,531

6

wongnai_reviews

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:th", "license:lgpl-3.0" ]

Wongnai's review dataset contains restaurant reviews and ratings, mainly in Thai language. The reviews are in 5 classes ranging from 1 to 5 stars.

473

1

woz_dialogue

false

[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:token-classification", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:multi-class-classification", "task_ids:parsing", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "language:en", "language:it", "license:unknown", "arxiv:1604.04562" ]

Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) that the user can ask a value for once a restaurant has been offered.

805

2

wrbsc

false

[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-3.0" ]

WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators.

263

0

x_stance

false

[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "language:en", "language:fr", "language:it", "license:cc-by-nc-4.0", "stance-detection", "arxiv:2003.08385" ]

The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems.

273

3

xcopa

false

[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|copa", "language:et", "language:ht", "language:id", "language:it", "language:qu", "language:sw", "language:ta", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-4.0" ]

XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the creation of XCOPA and the implementation of the baselines are available in the paper.\n

9,049

2

xcsr

false

[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended|codah", "source_datasets:extended|commonsense_qa", "language:ar", "language:de", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:ja", "language:nl", "language:pl", "language:pt", "language:ru", "language:sw", "language:ur", "language:vi", "language:zh", "license:mit", "arxiv:2106.06937" ]

To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.

4,337

2

xed_en_fi

false

[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:extended|other-OpenSubtitles2016", "language:en", "language:fi", "license:cc-by-4.0", "arxiv:2011.01612" ]

A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.

653

4

xglue

false

[ "task_categories:question-answering", "task_categories:summarization", "task_categories:text-classification", "task_categories:text2text-generation", "task_categories:token-classification", "task_ids:acceptability-classification", "task_ids:extractive-qa", "task_ids:named-entity-recognition", "task_ids:natural-language-inference", "task_ids:news-articles-headline-generation", "task_ids:open-domain-qa", "task_ids:parsing", "task_ids:topic-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "annotations_creators:found", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "language_creators:found", "language_creators:machine-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:extended|conll2003", "source_datasets:extended|squad", "source_datasets:extended|xnli", "source_datasets:original", "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh", "license:cc-by-nc-4.0", "license:cc-by-sa-4.0", "license:other", "paraphrase-identification", "question-answering", "arxiv:2004.01401" ]

XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation. The benchmark is composed of the following 11 tasks: - NER - POS Tagging (POS) - News Classification (NC) - MLQA - XNLI - PAWS-X - Query-Ad Matching (QADSM) - Web Page Ranking (WPR) - QA Matching (QAM) - Question Generation (QG) - News Title Generation (NTG) For more information, please take a look at https://microsoft.github.io/XGLUE/.

4,102

13

xnli

false

[ "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh" ]

XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).

43,897

16

xor_tydi_qa

false

[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "source_datasets:extended|tydiqa", "language:ar", "language:bn", "language:fi", "language:ja", "language:ko", "language:ru", "language:te", "license:mit", "arxiv:2010.11856" ]

XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections. There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.

394

0

xquad

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|squad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ro", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "arxiv:1910.11856" ]

XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.

6,695

5

xquad_r

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended|squad", "source_datasets:extended|xquad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "arxiv:2004.05484" ]

XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.

3,593

2

xsum

false

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:unknown", "arxiv:1808.08745" ]

Extreme Summarization (XSum) Dataset. There are three features: - document: Input news article. - summary: One sentence summary of the article. - id: BBC ID of the article.

37,094

23

xsum_factuality

false

[ "task_categories:summarization", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|other-xsum", "language:en", "license:cc-by-4.0", "hallucinations" ]

Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. We have crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.

408

3

xtreme

false

[ "task_categories:multiple-choice", "task_categories:question-answering", "task_categories:token-classification", "task_categories:text-classification", "task_categories:text-retrieval", "task_ids:multiple-choice-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:natural-language-inference", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "multilinguality:translation", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "source_datasets:extended|xnli", "source_datasets:extended|paws-x", "source_datasets:extended|wikiann", "source_datasets:extended|xquad", "source_datasets:extended|mlqa", "source_datasets:extended|tydiqa", "source_datasets:extended|tatoeba", "source_datasets:extended|squad", "language:af", "language:ar", "language:bg", "language:bn", "language:de", "language:el", "language:en", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:id", "language:it", "language:ja", "language:jv", "language:ka", "language:kk", "language:ko", "language:ml", "language:mr", "language:ms", "language:my", "language:nl", "language:pt", "language:ru", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:ur", "language:vi", "language:yo", "language:zh", "license:apache-2.0", "license:cc-by-4.0", "license:cc-by-2.0", "license:cc-by-sa-4.0", "license:other", "license:cc-by-nc-4.0", "parallel-sentence-retrieval", "paraphrase-identification", "arxiv:2003.11080" ]

The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa.

42,291

28

yahoo_answers_qa

false

[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-yahoo-webscope-l6", "language:en", "license:unknown" ]

Yahoo Non-Factoid Question Dataset is derived from Yahoo's Webscope L6 collection using machine learning techiques such that the questions would contain non-factoid answers.The dataset contains 87,361 questions and their corresponding answers. Each question contains its best answer along with additional other answers submitted by users. Only the best answer was reviewed in determining the quality of the question-answer pair.

885

8

yahoo_answers_topics

false

[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended|other-yahoo-answers-corpus", "language:en", "license:unknown" ]

Yahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content and the main category information.

2,492

15

yelp_polarity

false

[ "language:en", "arxiv:1509.01626" ]

Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset_challenge The Yelp reviews polarity dataset is constructed by Xiang Zhang ([email protected]) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). DESCRIPTION The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".

4,529

4

yelp_review_full

false

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:other", "arxiv:1509.01626" ]

The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. The Yelp reviews full star dataset is constructed by Xiang Zhang ([email protected]) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

18,154

16

yoruba_bbc_topics

false

[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:yo", "license:unknown" ]

A collection of news article headlines in Yoruba from BBC Yoruba. Each headline is labeled with one of the following classes: africa, entertainment, health, nigeria, politics, sport or world. The dataset was presented in the paper: Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (EMNLP 2020).

265

0

yoruba_gv_ner

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:yo", "license:cc-by-3.0" ]

The Yoruba GV NER dataset is a labeled dataset for named entity recognition in Yoruba. The texts were obtained from Yoruba Global Voices News articles https://yo.globalvoices.org/ . We concentrate on four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE]. The Yoruba GV NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme. For more details, see https://www.aclweb.org/anthology/2020.lrec-1.335/

263

0

yoruba_text_c3

false

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:yo", "license:cc-by-nc-4.0" ]

Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/

263

1

yoruba_wordsim353

false

[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:semantic-similarity-scoring", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:en", "language:yo", "license:unknown" ]

A translation of the word pair similarity dataset wordsim-353 to Yorùbá. The dataset was presented in the paper Alabi et al.: Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (LREC 2020).

265

0

youtube_caption_corrections

false

[ "task_categories:other", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:slot-filling", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "token-classification-of-text-errors" ]

Dataset built from pairs of YouTube captions where both 'auto-generated' and 'manually-corrected' captions are available for a single specified language. This dataset labels two-way (e.g. ignoring single-sided insertions) same-length token differences in the `diff_type` column. The `default_seq` is composed of tokens from the 'auto-generated' captions. When a difference occurs between the 'auto-generated' vs 'manually-corrected' captions types, the `correction_seq` contains tokens from the 'manually-corrected' captions.

269

1

zest

false

[ "task_categories:question-answering", "task_categories:token-classification", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "output-structure", "yes-no-qa", "arxiv:2011.08115" ]

ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize in five different ways.

857

1

0n1xus/codexglue

false

[]

CodeXGLUE is a benchmark dataset to foster machine learning research for program understanding and generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison.

467

2

0n1xus/pytorrent-standalone

false

[]

pytorrent-standalone is a subset of the PyTorrent dataset, where only functions that does not depend on external libraries are kept.

299

0

AConsApart/anime_subtitles_DialoGPT

false

[]

null

152

0

AHussain0418/day2_data

false

[]

null

301

0

AHussain0418/day4data

false

[]

null

299

0

AHussain0418/demo_data

false

[]

null

300

0

AI-Sweden/SuperLim

false

[ "task_categories:question-answering", "task_categories:text-classification", "task_categories:other", "multilinguality:monolingual", "language:sv" ]

\

1,934

2

ARKseal/YFCC14M_subset_webdataset

false

[]

null

300

0

ARTeLab/fanpage

false

[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100k", "source_datasets:original", "language:it", "license:unknown" ]

null

329

2

ARTeLab/ilpost

false

[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100k", "language:it" ]

null

307

0

ARTeLab/mlsum-it

false

[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100k", "language:it" ]

null

307

0

ASCCCCCCCC/amazon_zh

false

[ "license:apache-2.0" ]

null

300

1

ASCCCCCCCC/amazon_zh_simple

false

[ "license:apache-2.0" ]

null

302

1

Abdo1Kamr/Arabic_Hadith

false

[]

null

153

0

Abirate/code_net_dataset

false

[]

null

300

1

Abirate/code_net_dev_dataset

false

[]

null

301

0

Abirate/code_net_test_final_dataset

false

[]

null

300

0

Abirate/english_quotes

false

[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en" ]

null

2,854

0

Abirate/french_book_reviews

false

[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:fr" ]

null

354

2

AdWeeb/DravidianMT

false

[]

null

153

0

Adnan/Urdu_News_Headlines

false

[]

null

153

0

AhmadSawal/qa

false

[]

null

153

0

AhmedSSoliman/CoNaLa

false

[]

null

307

0

Aisha/BAAD16

false

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:bn", "license:cc-by-4.0", "arxiv:2001.05316" ]

null

299

0

Aisha/BAAD6

false

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:bn", "license:cc-by-4.0" ]

null

299

0

Akila/ForgottenRealmsWikiDataset

false

[]

null

302

2

Akshith/aa

false

[]

null

153

0

Akshith/g_rock

false

[]

null

153

0

Akshith/test

false

[]

null

301

0

adorkin/extended_tweet_emojis

false

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en" ]

null

267

1

AlekseyKorshuk/comedy-scripts

false

[]

This dataset is designed to generate lyrics with HuggingArtists.

300

1

AlekseyKorshuk/horror-scripts

false

[]

This dataset is designed to generate lyrics with HuggingArtists.

299

1

AlexMaclean/all-deletion-compressions

false

[]

null

300

1

AlexMaclean/wikipedia-deletion-compressions

false

[]

null

300

1

AlexZapolskii/zapolskii-amazon

false

[]

null

299

0

AlgoveraAI/CryptoPunks

false

[]

CryptoPunks is a non-fungible token (NFT) collection on the Ethereum blockchain. The dataset contains 10,000 CryptoPunk images, most of humans but also of three special types: Zombie (88), Ape (24) and Alien (9). They are provided with both clear backgrounds and teal backgrounds.

152

4

Aliseyfi/event_token_type

false

[]

null

149

0

Alvenir/nst-da-16khz

false

[]

null

297

1

AndrewMcDowell/de_corpora_parliament_processed

false

[]

null

297

0

Annabelleabbott/real-fake-news-workshop

false

[]

null

299

0

Annielytics/DoctorsNotes

false

[]

null

150

0

Anurag-Singh-creator/task

false

[]

null

297

0

Anurag-Singh-creator/tasks

false

[]

null

151

0

ApiInferenceTest/asr_dummy

false

[]

Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

304

0

Arnold/hausa_common_voice

false

[]

null

299

0

AryanLala/autonlp-data-Scientific_Title_Generator

false

[]

null

301

1

Atsushi/fungi_diagnostic_chars_comparison_japanese

false

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ja", "license:cc-by-4.0" ]

null

297

0

Atsushi/fungi_indexed_mycological_papers_japanese

false

[ "annotations_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ja", "license:cc-by-4.0" ]

null

297

0

Atsushi/fungi_trait_circus_database

false

[ "annotations_creators:other", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:ja", "license:cc-by-4.0" ]

null

300

0

Avishekavi/Avi

false

[]

null

151

0

BSC-LT/SQAC

false

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "arxiv:2107.07253", "arxiv:1606.05250" ]

This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment. The sources of the contexts are: * Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode). * News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/). * Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode). This dataset can be used to build extractive-QA.

297

4

BSC-LT/ancora-ca-ner

false

[ "language:ca" ]

AnCora Catalan NER. This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for Machine Learning and Language Model evaluation purposes. Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB) tags to mark and classify Named Entites. We did not filter out the different categories of NEs from Ancora (weak and strong). We did 6 minor edits by hand. AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence. This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).

297

1

BSC-LT/sts-ca

false

[ "language:ca" ]

Semantic Textual Similarity in Catalan. STS corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. It consists of more than 3000 sentence pairs, annotated with the semantic similarity between them, using a scale from 0 (no similarity at all) to 5 (semantic equivalence). It is done manually by 4 different annotators following our guidelines based on previous work from the SemEval challenges (https://www.aclweb.org/anthology/S13-1004.pdf). The source data are scraped sentences from the Catalan Textual Corpus (https://doi.org/10.5281/zenodo.4519349), used under CC-by-SA-4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/). The dataset is released under the same licence. This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB). This is the version 1.0.2 of the dataset with the complete human and automatic annotations and the analysis scripts. It also has a more accurate license. This dataset can be used to build and score semantic similiarity models.

297

0

BSC-LT/tecla

false

[ "language:ca" ]

TeCla: Text Classification Catalan dataset Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat

297

0

BSC-LT/viquiquad

false

[ "language:ca", "arxiv:1606.05250" ]

ViquiQuAD: an extractive QA dataset from Catalan Wikipedia. This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations) articles in the Catalan Wikipedia "Viquipèdia" (ca.wikipedia.org), and 1 to 5 questions with their answer for each fragment. Viquipedia articles are used under CC-by-sa licence. This dataset can be used to build extractive-QA and Language Models. Funded by the Generalitat de Catalunya, Departament de Polítiques Digitals i Administració Pública (AINA), MT4ALL and Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).

297

0

BSC-LT/xquad-ca

false

[ "language:ca", "arxiv:1910.11856" ]

Professional translation into Catalan of XQuAD dataset (https://github.com/deepmind/xquad). XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Rumanian was added later. We added the 13th language to the corpus using also professional native catalan translators. XQuAD and XQuAD-Ca datasets are released under CC-by-sa licence.

297

0

Babelscape/rebel-dataset

false

[ "task_categories:text-retrieval", "task_categories:text-generation", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "relation-extraction", "conditional-text-generation", "arxiv:2005.00614" ]

REBEL is a silver dataset created for the paper REBEL: Relation Extraction By End-to-end Language generation

317

10

Babelscape/wikineural

false

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "license:cc-by-nc-sa-4.0", "structure-prediction", "arxiv:1810.04805" ]

null

597

8

BatuhanYilmaz/github-issues

false

[]

null

151

0

Baybars/parla_text_corpus

false

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:various", "multilinguality:monolingual", "size_categories:100k<n<1M", "source_datasets:found", "language:ca", "license:cc-by-4.0", "robust-speech-event" ]

null

297

0

BeIR/beir-corpus

false

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0" ]

null

445

1

BeIR/beir

false

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0" ]

null

603

3