category string | split string | Name string | Subsets string | HF Link null | Link string | License string | Year int64 | Language string | Dialect string | Domain string | Form string | Collection Style null | Description string | Volume float64 | Unit string | Ethical Risks null | Provider string | Derived From null | Paper Title null | Paper Link null | Script string | Tokenized bool | Host string | Access string | Cost string | Test Split null | Tasks string | Venue Title null | Venue Type null | Venue Name null | Authors string | Affiliations string | Abstract string | Name_exist int64 | Subsets_exist int64 | HF Link_exist null | Link_exist int64 | License_exist int64 | Year_exist int64 | Language_exist int64 | Dialect_exist int64 | Domain_exist int64 | Form_exist int64 | Collection Style_exist null | Description_exist int64 | Volume_exist int64 | Unit_exist int64 | Ethical Risks_exist null | Provider_exist int64 | Derived From_exist null | Paper Title_exist null | Paper Link_exist null | Script_exist int64 | Tokenized_exist int64 | Host_exist int64 | Access_exist int64 | Cost_exist int64 | Test Split_exist null | Tasks_exist int64 | Venue Title_exist null | Venue Type_exist null | Venue Name_exist null | Authors_exist int64 | Affiliations_exist int64 | Abstract_exist int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fr | test | 20min-XD | null | null | https://github.com/ZurichNLP/20min-XD | custom | 2,025 | multilingual | null | ['news articles'] | text | null | A French-German, document-level comparable corpus of news articles from the Swiss online news outlet 20 Minuten/20 minutes. It contains 15,000 article pairs from 2015-2024, automatically aligned based on semantic similarity, exhibiting a broad spectrum of cross-lingual similarity. | 15,000 | documents | null | [' University of Zurich', '20 Minuten (TX Group)'] | null | null | null | null | false | GitHub | Free | null | ['machine translation', 'other'] | null | null | null | ['Michelle Wastl', 'Jannis Vamvas', 'Selena Calleri', 'Rico Sennrich'] | ['Department of Computational Linguistics, University of Zurich', '20 Minuten (TX Group)'] | We present 20min-XD (20 Minuten cross-lingual document-level), a French-German, document-level comparable corpus of news articles, sourced from the Swiss online news outlet 20 Minuten/20 minutes. Our dataset comprises around 15,000 article pairs spanning 2015 to 2024, automatically aligned based on semantic similarity.... | 1 | null | null | 1 | 1 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | Alloprof | null | null | https://huggingface.co/datasets/antoinelb7/alloprof | MIT License | 2,023 | multilingual | null | ['web pages'] | text | null | A French question-answering dataset from the Alloprof educational help website. It contains 29,349 questions from K-12 students and their explanations, often including images and links to 2,596 reference pages, covering various school subjects like math, French, and science. | 29,349 | sentences | null | ['Alloprof', 'Mila'] | null | null | null | null | false | HuggingFace | Free | null | ['question answering', 'information retrieval'] | null | null | null | ['Antoine Lefebvre-Brossard', 'Stephane Gazaille', 'Michel C. Desmarais'] | ['Mila-Quebec AI Institute', 'Polytechnique Montréal'] | Teachers and students are increasingly relying on online learning resources to supplement the ones provided in school. This increase in the breadth and depth of available resources is a great thing for students, but only provided they are able to find answers to their queries. Question-answering and information retriev... | 1 | null | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | FREDSum | null | null | https://github.com/linto-ai/FREDSum | CC BY-SA 4.0 | 2,023 | fr | null | ['TV Channels', 'web pages'] | text | null | A dataset of manually transcribed and annotated French political debates from 1974-2023. It is designed for multi-party dialogue summarization and includes abstractive/extractive summaries, topic segmentation, and abstractive communities annotations to support research in this area. | 142 | documents | null | ['Linagora Labs'] | null | null | null | null | false | GitHub | Free | null | ['summarization', 'speech recognition'] | null | null | null | ['Virgile Rennard', 'Guokan Shang', 'Damien Grari', 'Julie Hunter', 'Michalis Vazirgiannis'] | ['Linagora, France', 'École Polytechnique', 'Grenoble Ecole de Management'] | Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved the performance of abstractive summarization systems. The majority of research has focused on written documents, however, neglecting the problem of multi-party dialogue summarization. In this pape... | 1 | null | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | Vibravox | null | null | https://huggingface.co/datasets/Cnam-LMSSC/vibravox | CC BY 4.0 | 2,024 | fr | null | ['wikipedia'] | audio | null | Vibravox is a GDPR-compliant dataset containing audio recordings of French speech using five different body-conduction audio sensors and a reference airborne microphone. It includes 45 hours of speech per sensor from 188 participants under various acoustic conditions, with linguistic and phonetic transcriptions. | 273.72 | hours | null | ['LMSSC'] | null | null | null | null | false | HuggingFace | Free | null | ['speaker identification', 'speech recognition'] | null | null | null | ['Julien Hauret', 'Malo Olivier', 'Thomas Joubaud', 'Christophe Langrenne', 'Sarah Poire´e', 'Ve´ronique Zimpfer', 'E´ric Bavu'] | ['Laboratoire de Me´canique des Structures et des Syste`mes Couple´s, Conservatoire national des arts et me´tiers, HESAM Universite´, 75003 Paris, France', 'Department of Acoustics and Soldier Protection, French-German Research Institute of Saint-Louis (ISL)'] | Vibravox is a dataset compliant with the General Data Protection Regulation (GDPR) containing audio recordings using five different body-conduction audio sensors: two in-ear microphones, two bone conduction vibration pickups, and a laryngophone. The dataset also includes audio data from an airborne microphone used as a... | 1 | null | null | 1 | 1 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | MTNT | null | null | https://github.com/pmichel31415/mtnt | MIT License | 2,018 | multilingual | null | ['social media', 'commentary'] | text | null | A benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit and professionally sourced translations. It includes English comments translated into French and Japanese, as well as French and Japanese comments translated into English, on the order of 7k-37k sentences per langua... | 37,930 | sentences | null | ['Carnegie Mellon University'] | null | null | null | null | false | GitHub | Free | null | ['machine translation'] | null | null | null | ['Paul Michel', 'Graham Neubig'] | ['Language Technologies Institute', 'Carnegie Mellon University'] | Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and transl... | 1 | null | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 0 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | PIAF | null | null | https://github.com/etalab/piaf | MIT License | 2,020 | fr | null | ['wikipedia'] | text | null | PIAF is a French Question Answering dataset that was collected through a participatory approach. The dataset consists of question-answer pairs extracted from Wikipedia articles. | 3,835 | sentences | null | ['Etalab'] | null | null | null | null | false | GitHub | Free | null | ['question answering'] | null | null | null | ['Rachel Keraron', 'Guillaume Lancrenon', 'Mathilde Bras', 'Frédéric Allary', 'Gilles Moyse', 'Thomas Scialom', 'Edmundo-Pavel Soriano-Morales', 'Jacopo Staiano'] | ['reciTAL', 'Etalab', "Sorbonne Universit'e"] | Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collectio... | 1 | null | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | FrenchToxicityPrompts | null | null | https://download.europe.naverlabs.com/FrenchToxicityPrompts/ | CC BY-SA 4.0 | 2,024 | fr | null | ['social media', 'public datasets'] | text | null | A dataset of 50,000 naturally occurring French prompts and their continuations, annotated with toxicity scores from a widely used toxicity classifier. It is designed to evaluate and mitigate toxicity in French language models. | 50,000 | sentences | null | ['NAVER LABS Europe'] | null | null | null | null | false | other | Free | null | ['offensive language detection'] | null | null | null | ['Caroline Brun', 'Vassilina Nikoulina'] | ['NAVER LABS Europe'] | Large language models (LLMs) are increasingly popular but are also prone to generating bias, toxic or harmful language, which can have detrimental effects on individuals and communities. Although most efforts is put to assess and mitigate toxicity in generated content, it is primarily concentrated on English, while it'... | 1 | null | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | OBSINFOX | null | null | https://github.com/obs-info/obsinfox | CC BY-NC 4.0 | 2,024 | fr | null | ['news articles'] | text | null | A corpus of 100 French press documents from 17 unreliable sources. The documents were annotated by 8 human annotators using 11 labels (e.g., FakeNews, Subjective, Exaggeration) to analyze the characteristics of fake news. | 100 | documents | null | ['Observatoire'] | null | null | null | null | false | GitHub | Free | null | ['fake news detection', 'topic classification'] | null | null | null | ['Benjamin Icard', 'François Maine', 'Morgane Casanova', 'Géraud Faye', 'Julien Chanson', 'Guillaume Gadek', 'Ghislain Atemezing', 'François Bancilhon', 'Paul Égré'] | ['Sorbonne Université', 'Institut Jean-Nicod', 'Freedom Partners', 'Université de Rennes', 'Airbus Defence and Space', 'Université Paris-Saclay', 'Mondeca', 'European Union Agency for Railways', 'Observatoire des Médias'] | We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic o... | 1 | null | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | CFDD | null | null | https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1 | CC BY-NC-SA 4.0 | 2,023 | fr | null | ['captions', 'public datasets', 'web pages'] | text | null | The Claire French Dialogue Dataset (CFDD) is a corpus containing roughly 160 million words from transcripts and stage plays in French. | 160,000,000 | tokens | null | ['LINAGORA Labs'] | null | null | null | null | false | HuggingFace | Free | null | ['language modeling', 'text generation'] | null | null | null | ['Julie Hunter', 'Jérôme Louradour', 'Virgile Rennard', 'Ismaïl Harrando', 'Guokan Shang', 'Jean-Pierre Lorré'] | ['LINAGORA'] | We present the Claire French Dialogue Dataset (CFDD), a resource created by members of LINAGORA Labs in the context of the OpenLLM France initiative. CFDD is a corpus containing roughly 160 million words from transcripts and stage plays in French that we have assembled and publicly released in an effort to further the ... | 1 | null | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | FREEMmax | null | null | https://github.com/FreEM-corpora/FreEMmax_OA | custom | 2,022 | fr | null | ['web pages', 'public datasets'] | text | null | FREEMmax is a large corpus of Early Modern French (16th-18th centuries), with some texts extending to the 1920s. It aggregates texts from various sources, including institutional databases, research projects, and web scraping, covering diverse genres like literature, correspondence, and plays. | 185,643,482 | tokens | null | ['Inria', 'Sorbonne Universite', 'Universite de Geneve', 'LIGM', 'Universite Gustage Eiffel', 'CNRS'] | null | null | null | null | false | zenodo | Free | null | ['language modeling'] | null | null | null | ['Simon Gabay', 'Pedro Ortiz Suarez', 'Alexandre Bartz', 'Alix Chague', 'Rachel Bawden', 'Philippe Gambette', 'Benoît Sagot'] | ['Inria', 'Sorbonne Universite', 'Universite de Geneve', 'LIGM', 'Universite Gustage Eiffel', 'CNRS'] | Language models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, specific efforts are necessary to train natu... | 1 | null | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
fr | test | FQuAD2.0 | null | null | https://huggingface.co/datasets/illuin/fquad | CC BY-NC-SA 3.0 | 2,021 | fr | null | ['wikipedia'] | text | null | A French Question Answering dataset that extends FQuAD1.1 with over 17,000 adversarially created unanswerable questions. The questions are extracted from Wikipedia articles, and the total dataset comprises almost 80,000 questions. It is designed to train models to distinguish answerable from unanswerable questions. | 79,768 | sentences | null | ['Illuin Technology'] | null | null | null | null | false | HuggingFace | Free | null | ['question answering'] | null | null | null | ['Quentin Heinrich', 'Gautier Viaud', 'Wacim Belblidia'] | ['Illuin Technology'] | Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the concomitant advances in Language Modeling. Most of these breakthroughs, however, are centered on the English language. In 2020, as a first stron... | 1 | null | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 0 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | XNLI | [{'Name': 'en', 'Volume': 7500.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'fr', 'Volume': 7500.0, 'Unit': 'sentences', 'Language': 'French'}, {'Name': 'es', 'Volume': 7500.0, 'Unit': 'sentences', 'Language': 'Spanish'}, {'Name': 'de', 'Volume': 7500.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': ... | null | https://github.com/facebookresearch/XNLI | CC BY-NC 4.0 | 2,018 | ['English', 'French', 'Spanish', 'German', 'Greek', 'Bulgarian', 'Russian', 'Turkish', 'Arabic', 'Vietnamese', 'Thai', 'Chinese', 'Hindi', 'Swahili', 'Urdu'] | null | ['public datasets'] | text | null | Evaluation set for NLI by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages | 112,500 | sentences | null | ['Facebook'] | null | null | null | null | false | GitHub | Free | null | ['natural language inference'] | null | null | null | ['Alexis Conneau', 'Guillaume Lample', 'Ruty Rinott', 'Adina Williams', 'Samuel R. Bowman', 'Holger Schwenk', 'Veselin Stoyanov'] | ['Facebook AI Research', 'New York University'] | State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic,... | 1 | 1 | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 0 | 0 | 0 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | X-stance | [{'Name': 'DE', 'Volume': 40200.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': 'FR', 'Volume': 14129.0, 'Unit': 'sentences', 'Language': 'French'}, {'Name': 'IT', 'Volume': 1172.7, 'Unit': 'sentences', 'Language': 'Italian'}] | null | http://doi.org/10.5281/zenodo.3831317 | CC BY-NC 4.0 | 2,020 | ['German', 'French', 'Italian'] | null | ['commentary'] | text | null | A large-scale, multilingual (German, French, Italian) dataset for stance detection. It contains over 67,000 comments from Swiss political candidates on more than 150 political issues, formatted as question-comment pairs. The dataset is designed for cross-lingual and cross-target evaluation. | 55,502 | sentences | null | ['Univeristy of Zurich'] | null | null | null | null | false | zenodo | Free | null | ['stance detection'] | null | null | null | ['Jannis Vamvas', 'Rico Sennrich'] | ['University of Zurich', 'University of Edinburgh'] | We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance ... | 1 | 1 | null | 1 | 1 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | DiS-ReX | [{'Name': 'English', 'Language': 'English', 'Volume': 532499.0, 'Unit': 'sentences'}, {'Name': 'French', 'Language': 'French', 'Unit': 'sentences', 'Volume': 409087.0}, {'Name': 'Spanish', 'Language': 'Spanish', 'Unit': 'sentences', 'Volume': 456418.0}, {'Volume': 438315.0, 'Language': 'German', 'Name': 'German', 'Unit... | null | https://github.com/dair-iitd/DiS-ReX | unknown | 2,021 | ['English', 'German', 'Spanish', 'French'] | null | ['wikipedia'] | text | null | DiS-ReX is a multilingual dataset for distantly supervised relation extraction (DS-RE) spanning English, German, Spanish, and French. It contains over 1.5 million sentences aligned with DBpedia, featuring 36 relation classes and a 'no relation' class, designed to be a challenging benchmark. | 1,836,319 | sentences | null | ['Indian Institute of Technology'] | null | null | null | null | false | GitHub | Free | null | ['relation extraction'] | null | null | null | ['Abhyuday Bhartiya', 'Kartikeya Badola', 'Mausam'] | ['Indian Institute of Technology'] | Distant supervision (DS) is a well established technique for creating large-scale datasets for relation extraction (RE) without using human annotations. However, research in DS-RE has been mostly limited to the English language. Constraining RE to a single language inhibits utilization of large amounts of data in other... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | RELX | [{'Name': 'English', 'Volume': 502.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'French', 'Volume': 502.0, 'Unit': 'sentences', 'Language': 'French'}, {'Name': 'German', 'Volume': 502.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': 'Spanish', 'Volume': 502.0, 'Unit': 'sentences', 'Language': 'Spanis... | null | https://github.com/boun-tabi/RELX | MIT License | 2,020 | ['English', 'French', 'German', 'Spanish', 'Turkish'] | null | ['public datasets'] | text | null | A public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish. It contains 502 parallel sentences created by selecting a subset from the KBP-37 test set and having them professionally translated and annotated. | 2,510 | sentences | null | ['Boğaziçi University'] | null | null | null | null | false | GitHub | Free | null | ['cross-lingual information retrieval'] | null | null | null | ['Abdullatif Köksal', 'Arzucan Özgür'] | ['Department of Computer Engineering, Boğaziçi University'] | Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annot... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | MultiSubs | [{'Language': 'English', 'Name': 'English', 'Volume': 2159635.0, 'Unit': 'sentences'}, {'Name': 'Spanish', 'Language': 'Spanish', 'Volume': 2159635.0, 'Unit': 'sentences'}, {'Name': 'Portuguese', 'Language': 'Portuguese', 'Volume': 1796095.0, 'Unit': 'sentences'}, {'Name': 'French', 'Volume': 1063071.0, 'Language': 'Fr... | null | https://doi.org/10.5281/zenodo.5034604 | CC BY 4.0 | 2,022 | ['English', 'Spanish', 'Portuguese', 'French', 'German'] | null | ['TV Channels', 'public datasets'] | text | null | A large-scale multimodal and multilingual dataset of images aligned to text fragments from movie subtitles. It aims to facilitate research on grounding words to images in their contextual usage in language. The images are aligned to text fragments rather than whole sentences, and the parallel texts are multilingual. | 5,403,281 | sentences | null | ['Imperial College London', 'Federal University of Mato Grosso'] | null | null | null | null | false | zenodo | Free | null | ['machine translation', 'fill-in-the blank'] | null | null | null | ['Josiah Wang', 'Pranava Madhyastha', 'Josiel Figueiredo', 'Chiraag Lala', 'Lucia Specia'] | ['Imperial College London', 'Federal University of Mato Grosso'] | This paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dataset consists of images selected to unambiguously illustrate concepts expressed in sentences from movie subtitles. The dataset is a valuable... | 1 | 1 | null | 1 | 1 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | MEE | [{'Name': 'English', 'Language': 'English', 'Volume': 13000.0, 'Unit': 'documents'}, {'Name': 'Portuguese', 'Language': 'Portuguese', 'Volume': 1500.0, 'Unit': 'documents'}, {'Name': 'Spanish', 'Language': 'Spanish', 'Volume': 3268.0, 'Unit': 'documents'}, {'Volume': 4479.0, 'Language': 'Polish', 'Name': 'Polish', 'Uni... | null | unknown | 2,022 | ['English', 'Spanish', 'Portuguese', 'Polish', 'Turkish', 'Hindi', 'Korean', 'Japanese'] | null | ['wikipedia'] | text | null | A large-scale Multilingual Event Extraction (MEE) dataset covering 8 typologically different languages. Sourced from Wikipedia, it provides comprehensive annotations for entity mentions, event triggers, and event arguments across diverse topics like politics, technology, and military. | 31,226 | documents | null | ['University of Oregon', 'Adobe Research'] | null | null | null | null | false | other | Free | null | ['named entity recognition'] | null | null | null | ['Amir Pouran Ben Veyseh', 'Javid Ebrahimi', 'Franck Dernoncourt', 'Thien Huu Nguyen'] | ['Department of Computer Science, University of Oregon', 'Adobe Research'] | Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for ... | 1 | 1 | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 0 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | ||
multi | test | XCOPA | [{'Name': 'Estonian', 'Volume': 600.0, 'Unit': 'sentences', 'Language': 'Estonian'}, {'Name': 'Haitian Creole', 'Volume': 600.0, 'Unit': 'sentences', 'Language': 'Haitian Creole'}, {'Name': 'Indonesian', 'Volume': 600.0, 'Unit': 'sentences', 'Language': 'Indonesian'}, {'Name': 'Italian', 'Volume': 600.0, 'Unit': 'sente... | null | https://github.com/cambridgeltl/xcopa | CC BY 4.0 | 2,020 | ['Indonesian', 'Italian', 'Swahili', 'Thai', 'Turkish', 'Vietnamese', 'Chinese', 'Estonian', 'Haitian Creole', 'Eastern Apurímac Quechua', 'Tamil'] | null | ['public datasets'] | text | null | XCOPA is a typologically diverse multilingual dataset for causal commonsense reasoning. It was created by translating and re-annotating the English COPA dataset's validation and test sets into 11 languages. The task is to choose the more plausible cause or effect for a given premise. | 6,600 | sentences | null | ['Cambridge'] | null | null | null | null | false | GitHub | Free | null | ['commonsense reasoning'] | null | null | null | ['Edoardo M. Ponti', 'Goran Glavasˇ', 'Olga Majewska', 'Qianchu Liu', 'Ivan Vulic´', 'Anna Korhonen'] | ['Language Technology Lab, TAL, University of Cambridge, UK', 'Data and Web Science Group, University of Mannheim, Germany'] | In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects. Moreover, they should be able to generalise the acquired world knowledge to new languages, modulo cultural differences. Advance... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | MLQA | [{'Name': 'en', 'Volume': 12738.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'ar', 'Volume': 5852.0, 'Unit': 'sentences', 'Language': 'Arabic'}, {'Name': 'de', 'Volume': 5029.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': 'vi', 'Volume': 6006.0, 'Unit': 'sentences', 'Language': 'Vietnamese'}, {'Nam... | null | https://github.com/facebookresearch/mlqa | CC BY-SA 3.0 | 2,020 | ['English', 'Arabic', 'German', 'Vietnamese', 'Spanish', 'Simplified Chinese', 'Hindi'] | null | ['wikipedia'] | text | null | MLQA has over 12K instances in English and 5K in each other language, with each instance parallel between 4 languages on average. | 46,461 | documents | null | ['Facebook'] | null | null | null | null | false | GitHub | Free | null | ['question answering'] | null | null | null | ['Patrick Lewis', 'Barlas Oğuz', 'Ruty Rinott', 'S. Riedel', 'Holger Schwenk'] | ['Facebook AI Research;University College London'] | Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making training QA systems in other languages challenging. An alternative to buil... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | M2DS | [{'Name': 'English', 'Volume': 67000.0, 'Unit': 'documents', 'Language': 'English'}, {'Name': 'Tamil', 'Volume': 32000.0, 'Unit': 'documents', 'Language': 'Tamil'}, {'Name': 'Japanese', 'Volume': 29000.0, 'Unit': 'documents', 'Language': 'Japanese'}, {'Name': 'Korean', 'Volume': 27000.0, 'Unit': 'documents', 'Language'... | null | https://huggingface.co/datasets/KushanH/m2ds | unknown | 2,024 | ['English', 'Tamil', 'Japanese', 'Korean', 'Sinhala'] | null | ['news articles', 'public datasets'] | text | null | M2DS is a multilingual multi-document summarization (MDS) dataset. It contains 180,000 news articles from the BBC, organized into 51,500 clusters across five languages: English, Japanese, Korean, Tamil, and Sinhala. The data covers the period from 2010 to 2023. | 180,000 | documents | null | ['University of Moratuwa', 'ConscientAI'] | null | null | null | null | false | HuggingFace | Free | null | ['summarization'] | null | null | null | ['Kushan Hewapathirana', 'Nisansa de Silva', 'C.D. Athuraliya'] | ['Dept. of Computer Science & Engineering, University of Moratuwa, Sri Lanka', 'ConscientAI, Sri Lanka'] | In the rapidly evolving digital era, there is an increasing demand for concise information as individuals seek to distil key insights from various sources. Recent attention from researchers on Multi-document Summarisation (MDS) has resulted in diverse datasets covering customer reviews, academic papers, medical and leg... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | XOR-TyDi | [{'Name': 'Ar', 'Volume': 17218.0, 'Unit': 'sentences', 'Language': 'Arabic'}, {'Name': 'Bn', 'Volume': 2682.0, 'Unit': 'sentences', 'Language': 'Bengali'}, {'Name': 'Fi', 'Volume': 9132.0, 'Unit': 'sentences', 'Language': 'Finnish'}, {'Name': 'Ja', 'Volume': 6531.0, 'Unit': 'sentences', 'Language': 'Japanese'}, {'Name... | null | https://nlp.cs.washington.edu/xorqa/ | CC BY-SA 4.0 | 2,021 | ['Arabic', 'Bengali', 'Finnish', 'Japanese', 'Korean', 'Russian', 'Telugu'] | null | ['public datasets'] | text | null | XOR-TyDi QA brings together 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 annota... | 53,059 | sentences | null | [] | null | null | null | null | false | other | Free | null | ['cross-lingual information retrieval', 'question answering'] | null | null | null | ['Akari Asai', 'Jungo Kasai', 'Jonathan H. Clark', 'Kenton Lee', 'Eunsol Choi', 'Hannaneh Hajishirzi'] | ['University of Washington', 'University of Washington', 'Google Research', 'The University of Texas at Austin; Allen Institute for AI'] | Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where languages have few reference articles -- and information asymmetry -- where questions reference concepts from other cultures. This work exten... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 0 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | Multilingual Hate Speech Detection Dataset | [{'Name': 'Arabic', 'Volume': 5790.0, 'Unit': 'sentences', 'Language': 'Arabic'}, {'Name': 'English', 'Volume': 96323.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'German', 'Volume': 6155.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': 'Indonesian', 'Volume': 13882.0, 'Unit': 'sentences', 'Language'... | null | https://github.com/hate-alert/DE-LIMIT | MIT License | 2,020 | ['Arabic', 'English', 'German', 'Indonesian', 'Italian', 'Polish', 'Portuguese', 'Spanish', 'French'] | null | ['public datasets', 'social media'] | text | null | Combined MLMA and L-HSAB datasets | 159,753 | sentences | null | ['Indian Institute of Technology Kharagpur'] | null | null | null | null | false | GitHub | Free | null | ['offensive language detection'] | null | null | null | ['Sai Saket Aluru', 'Binny Mathew', 'Punyajoy Saha', 'Animesh Mukherjee'] | ['Indian Institute of Technology Kharagpur'] | Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with log... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | MINION | [{'Name': 'English', 'Volume': 13000.0, 'Unit': 'documents', 'Language': 'English'}, {'Name': 'Spanish', 'Volume': 1500.0, 'Unit': 'documents', 'Language': 'Spanish'}, {'Name': 'Portuguese', 'Volume': 3268.0, 'Unit': 'documents', 'Language': 'Portuguese'}, {'Name': 'Polish', 'Volume': 4479.0, 'Unit': 'documents', 'Lang... | null | unknown | 2,022 | ['English', 'Spanish', 'Portuguese', 'Polish', 'Turkish', 'Hindi', 'Japanese', 'Korean'] | null | ['wikipedia'] | text | null | MINION is a large-scale, multilingual dataset for Event Detection (ED). It contains over 50,000 manually annotated event triggers in 8 languages (English, Spanish, Portuguese, Polish, Turkish, Hindi, Japanese, Korean) sourced from Wikipedia articles. The annotation schema is a pruned version of the ACE 2005 ontology. | 31,226 | documents | null | ['University of Oregon'] | null | null | null | null | false | other | Free | null | ['other'] | null | null | null | ['AmirPouranBenVeyseh', 'MinhVanNguyen', 'FranckDernoncourt', 'ThienHuuNguyen'] | ['Dept. of Computer and Information Science, University of Oregon, Eugene, OR, USA', 'Adobe Research, Seattle, WA, USA'] | Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English languages, important research questions for ED... | 1 | 1 | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 0 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | ||
multi | test | SEAHORSE | [{'Name': 'de', 'Language': 'German', 'Volume': 14591.0, 'Unit': 'sentences'}, {'Name': 'en', 'Language': 'English', 'Volume': 22339.0, 'Unit': 'sentences'}, {'Name': 'es', 'Language': 'Spanish', 'Volume': 14749.0, 'Unit': 'sentences'}, {'Name': 'ru', 'Language': 'Russian', 'Volume': 14542.0, 'Unit': 'sentences'}, {'Na... | null | https://goo.gle/seahorse | CC BY 4.0 | 2,023 | ['German', 'English', 'Spanish', 'Russian', 'Turkish', 'Vietnamese'] | null | ['public datasets'] | text | null | SEAHORSE is a large-scale dataset for multilingual, multifaceted summarization evaluation. It consists of 96,645 summaries with human ratings along 6 quality dimensions: comprehensibility, repetition, grammar, attribution, main ideas, and conciseness. It covers 6 languages, 9 systems, and 4 summarization datasets. | 96,645 | sentences | null | ['Google'] | null | null | null | null | false | GitHub | Free | null | ['summarization'] | null | null | null | ['Elizabeth Clark', 'Shruti Rijhwani', 'Sebastian Gehrmann', 'Joshua Maynez', 'Roee Aharoni', 'Vitaly Nikolaev', 'Thibault Sellam', 'Aditya Siddhant', 'Dipanjan Das', 'Ankur P. Parikh'] | ['Google DeepMind', 'Google Research'] | Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task. This is especially the case for languages other than English, where human evaluations are scarce. In this work, we introduce SEAHORSE, a dataset for multilingual, multifaceted summarization e... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | Mintaka | [{'Name': 'English', 'Language': 'English', 'Volume': 20000.0, 'Unit': 'sentences'}, {'Name': 'Arabic', 'Language': 'Arabic', 'Volume': 20000.0, 'Unit': 'sentences'}, {'Name': 'French', 'Language': 'French', 'Volume': 20000.0, 'Unit': 'sentences'}, {'Name': 'German', 'Volume': 20000.0, 'Language': 'German', 'Unit': 'se... | null | https://github.com/amazon-research/mintaka | CC BY 4.0 | 2,022 | ['English', 'Arabic', 'French', 'German', 'Hindi', 'Italian', 'Japanese', 'Portuguese', 'Spanish'] | null | ['wikipedia'] | text | null | Mintaka is a large, complex, naturally-elicited, and multilingual question answering dataset. It contains 20,000 English question-answer pairs, which have been translated into 8 other languages, totaling 180,000 samples. The dataset is annotated with Wikidata entities and includes 8 types of complex questions. | 180,000 | sentences | null | ['Amazon'] | null | null | null | null | false | GitHub | Free | null | ['question answering'] | null | null | null | ['Priyanka Sen', 'Alham Fikri Aji', 'Amir Saffari'] | ['Amazon Alexa AI'] | We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portu... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | Multi2WOZ | [{'Name': 'Arabic', 'Language': 'Arabic', 'Volume': 29500.0, 'Unit': 'sentences'}, {'Name': 'Chinese', 'Language': 'Chinese', 'Volume': 29500.0, 'Unit': 'sentences'}, {'Name': 'German', 'Language': 'German', 'Volume': 29500.0, 'Unit': 'sentences'}, {'Name': 'Russian', 'Language': 'Russian', 'Volume': 29500.0, 'Unit': '... | null | https://github.com/umanlp/Multi2WOZ | MIT License | 2,022 | ['Arabic', 'Chinese', 'German', 'Russian'] | null | ['public datasets'] | text | null | A multilingual, multi-domain task-oriented dialog (TOD) dataset in Arabic, Chinese, German, and Russian. It was created by translating and manually post-editing the 2,000 development and test dialogs from the English MultiWOZ 2.1 dataset, enabling reliable cross-lingual transfer evaluation. | 118,000 | sentences | null | ['University of Mannheim'] | null | null | null | null | false | GitHub | Free | null | ['instruction tuning'] | null | null | null | ['Chia-Chien Hung', 'Anne Lauscher', 'Ivan Vulic´', 'Simone Paolo Ponzetto', 'Goran Glavasˇ'] | ['Data and Web Science Group, University of Mannheim, Germany', 'MilaNLP, Bocconi University, Italy', 'LTL, University of Cambridge, UK', 'CAIDAS, University of Wu¨rzburg, Germany'] | Research on (multi-domain) task-oriented dialog (TOD) has predominantly focused on the English language, primarily due to the shortage of robust TOD datasets in other languages, preventing the systematic investigation of cross-lingual transfer for this crucial NLP application area. In this work, we introduce Multi2WOZ,... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | MTOP | [{'Name': 'English', 'Volume': 22288.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'German', 'Volume': 18788.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': 'French', 'Volume': 16584.0, 'Unit': 'sentences', 'Language': 'French'}, {'Name': 'Spanish', 'Volume': 15459.0, 'Unit': 'sentences', 'Language':... | null | https://fb.me/mtop_dataset | unknown | 2,021 | ['English', 'German', 'French', 'Spanish', 'Hindi', 'Thai'] | null | ['other'] | text | null | MTOP is a multilingual, almost-parallel dataset for task-oriented semantic parsing. It comprises 100k annotated utterances in 6 languages (English, German, French, Spanish, Hindi, Thai) across 11 domains. The dataset is designed to handle complex, nested queries through a compositional representation scheme. | 104,445 | sentences | null | ['Facebook'] | null | null | null | null | true | other | Free | null | ['named entity recognition', 'intent classification'] | null | null | null | ['Haoran Li', 'Abhinav Arora', 'Shuohui Chen', 'Anchit Gupta', 'Sonal Gupta', 'Yashar Mehdad'] | ['Facebook'] | Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are bas... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | X-RiSAWOZ | [{'Name': 'Chinese', 'Language': 'Chinese', 'Volume': 18000.0, 'Unit': 'sentences'}, {'Name': 'English', 'Language': 'English', 'Volume': 18000.0, 'Unit': 'sentences'}, {'Name': 'French', 'Language': 'French', 'Volume': 18000.0, 'Unit': 'sentences'}, {'Name': 'Hindi', 'Language': 'Hindi', 'Volume': 18000.0, 'Unit': 'se... | null | https://github.com/stanford-oval/dialogues | custom | 2,023 | ['Chinese', 'English', 'French', 'Hindi', 'Korean'] | null | ['public datasets'] | text | null | A multi-domain, large-scale, and high-quality task-oriented dialogue benchmark, produced by translating the Chinese RiSAWOZ data to four diverse languages: English, French, Hindi, and Korean; and one code-mixed English-Hindi language. It is an end-to-end dataset for building fully-functioning agents. | 90,000 | sentences | null | ['Stanford University'] | null | null | null | null | false | GitHub | Free | null | ['instruction tuning'] | null | null | null | ['Mehrad Moradshahi', 'Tianhao Shen', 'Kalika Bali', 'Monojit Choudhury', 'Gaël de Chalendar', 'Anmol Goel', 'Sungkyun Kim', 'Prashant Kodali', 'Ponnurangam Kumaraguru', 'Nasredine Semmar', 'Sina J. Semnani', 'Jiwon Seo', 'Vivek Seshadri', 'Manish Shrivastava', 'Michael Sun', 'Aditya Yadavalli', 'Chaobin You', 'Deyi Xi... | ['Stanford University', 'Tianjin University', 'Microsoft', 'Université Paris-Saclay', 'International Institute of Information Technology, Hyderabad', 'Hanyang University', 'Karya Inc.'] | Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese Ri... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | PRESTO | [{'Language': 'German', 'Name': 'German', 'Volume': 83584.0, 'Unit': 'sentences'}, {'Name': 'English', 'Unit': 'sentences', 'Language': 'English', 'Volume': 95671.0}, {'Unit': 'sentences', 'Name': 'Spanish', 'Language': 'Spanish', 'Volume': 96164.0}, {'Volume': 95870.0, 'Unit': 'sentences', 'Language': 'French', 'Name'... | null | https://github.com/google-research-datasets/presto | CC BY 4.0 | 2,023 | ['German', 'English', 'Spanish', 'French', 'Hindi', 'Japanese'] | null | ['other'] | text | null | PRESTO is a public, multilingual dataset of over 550K contextual conversations between humans and virtual assistants for parsing realistic task-oriented dialogs. It contains challenges like disfluencies, code-switching, and user revisions, and provides structured context (contacts, lists) for each example across six la... | 552,924 | sentences | null | ['Google Inc.'] | null | null | null | null | false | GitHub | Free | null | ['intent classification', 'instruction tuning'] | null | null | null | ['Rahul Goel', 'Waleed Ammar', 'Aditya Gupta', 'Siddharth Vashishtha', 'Motoki Sano', 'Faiz Surani', 'Max Chang', 'HyunJeong Choe', 'David Greene', 'Kyle He', 'Rattima Nitisaroj', 'Anna Trukhina', 'Shachi Paul', 'Pararth Shah', 'Rushin Shah', 'Zhou Yu'] | ['Google Inc.', 'University of Rochester', 'University of California, Santa Barbara', 'Columbia University'] | Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academic research in this area has been limited by the lack of datasets that realistically capture the wide array of user pain points. To enable re... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | LAHM | [{'Name': 'English', 'Volume': 105120.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'Hindi', 'Volume': 32734.0, 'Unit': 'sentences', 'Language': 'Hindi'}, {'Name': 'Arabic', 'Volume': 5394.0, 'Unit': 'sentences', 'Language': 'Arabic'}, {'Name': 'French', 'Volume': 20809.0, 'Unit': 'sentences', 'Language': 'F... | null | unknown | 2,023 | ['English', 'Hindi', 'Arabic', 'French', 'German', 'Spanish'] | null | ['social media', 'news articles', 'public datasets'] | text | null | A large-scale, semi-supervised dataset for multilingual and multi-domain hate speech identification. It contains nearly 300k tweets across 6 languages (English, Hindi, Arabic, French, German, Spanish) and 5 domains (Abuse, Racism, Sexism, Religious Hate, Extremism), created using a 3-layer annotation pipeline. | 227,836 | sentences | null | ['Logically.ai'] | null | null | null | null | false | other | Free | null | ['offensive language detection'] | null | null | null | ['Ankit Yadav', 'Shubham Chandel', 'Sushant Chatufale', 'Anil Bandhakavi'] | ['Logically.ai'] | Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual hate speech analysis dataset for English, Hindi, Arabic, French, German and Spanish languages for multiple domains across hate speech - Abuse, Racism, Sexism, ... | 1 | 1 | null | 0 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 0 | 0 | 0 | null | 1 | null | null | null | 1 | 1 | 1 | ||
multi | test | MARC | [{'Name': 'English', 'Volume': 2100000.0, 'Unit': 'sentences', 'Language': 'English'}, {'Name': 'Japanese', 'Volume': 2100000.0, 'Unit': 'sentences', 'Language': 'Japanese'}, {'Name': 'German', 'Volume': 2100000.0, 'Unit': 'sentences', 'Language': 'German'}, {'Name': 'French', 'Volume': 2100000.0, 'Unit': 'sentences', ... | null | https://registry.opendata.aws/amazon-reviews-ml | custom | 2,020 | ['Japanese', 'English', 'German', 'French', 'Spanish', 'Chinese'] | null | ['reviews'] | text | null | Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019 | 12,600,000 | sentences | null | ['Amazon'] | null | null | null | null | false | other | Free | null | ['sentiment analysis', 'review classification'] | null | null | null | ['Phillip Keung', 'Yichao Lu', 'Gyorgy Szarvas', 'Noah A. Smith'] | ['Amazon', 'Washington University'] | We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review te... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 | |
multi | test | MLSUM | [{'Name': 'FR', 'Volume': 424763.0, 'Unit': 'documents', 'Language': 'French'}, {'Name': 'DE', 'Volume': 242982.0, 'Unit': 'documents', 'Language': 'German'}, {'Name': 'ES', 'Volume': 290645.0, 'Unit': 'documents', 'Language': 'Spanish'}, {'Name': 'RU', 'Volume': 27063.0, 'Unit': 'documents', 'Language': 'Russian'}, {'... | null | https://github.com/recitalAI/MLSUM | custom | 2,020 | ['French', 'German', 'Spanish', 'Russian', 'Turkish'] | null | ['news articles', 'web pages'] | text | null | MLSUM is a large-scale multilingual summarization dataset with over 1.5 million article/summary pairs in French, German, Spanish, Russian, and Turkish. Collected from online newspapers, it is designed to complement the English CNN/Daily Mail dataset, enabling new research in cross-lingual summarization. | 1,259,070 | documents | null | ['reciTAL', 'Sorbonne Université', 'CNRS'] | null | null | null | null | false | GitHub | Free | null | ['summarization'] | null | null | null | ['Thomas Scialom', 'Paul-Alexis Dray', 'Sylvain Lamprier', 'Benjamin Piwowarski', 'Jacopo Staiano'] | ['reciTAL, Paris, France', 'Sorbonne Université, CNRS, LIP6, F-75005 Paris, France', 'CNRS, France'] | We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected d... | 1 | 1 | null | 1 | 0 | 1 | 1 | null | 1 | 1 | null | 1 | 1 | 1 | null | 1 | null | null | null | null | 1 | 1 | 1 | 1 | null | 1 | null | null | null | 1 | 1 | 1 |
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