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HotpotQA is a question answering dataset collected on the English Wikipedia, containing about 113K crowd-sourced questions that are constructed to require the introduction paragraphs of two Wikipedia articles to answer. Each question in the dataset comes with the two gold paragraphs, as well as a list of sentences in these paragraphs that crowdworkers identify as supporting facts necessary to answer the question.
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DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets.
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FEVER is a publicly available dataset for fact extraction and verification against textual sources.
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MTEB is a benchmark that spans 8 embedding tasks covering a total of 56 datasets and 112 languages. The 8 task types are Bitext mining, Classification, Clustering, Pair Classification, Reranking, Retrieval, Semantic Textual Similarity and Summarisation. The 56 datasets contain varying text lengths and they are grouped into three categories: Sentence to sentence, Paragraph to paragraph, and Sentence to paragraph.
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SciFact is a dataset of 1.4K expert-written claims, paired with evidence-containing abstracts annotated with veracity labels and rationales.
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TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is the accelerated rate of change: the topics of interest evolve as the pandemic progresses and the scientific literature in the area explodes. The COVID-19 pandemic provides an opportunity to capture this progression as it happens. TREC-COVID, in creating a test collection around COVID-19 literature, is building infrastructure to support new research and technologies in pandemic search.
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The Remote Sensing Image Captioning Dataset (RSICD) is a dataset for remote sensing image captioning task. It contains more than ten thousands remote sensing images which are collected from Google Earth, Baidu Map, MapABC and Tianditu. The images are fixed to 224X224 pixels with various resolutions. The total number of remote sensing images is 10921, with five sentences descriptions per image.
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The Reuters-21578 dataset is a collection of documents with news articles. The original corpus has 10,369 documents and a vocabulary of 29,930 words.
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SciDocs evaluation framework consists of a suite of evaluation tasks designed for document-level tasks.
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Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs, and each question pair is annotated with a binary value indicating whether the two questions are paraphrase of each other.
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A new publicly available dataset for verification of climate change-related claims.
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The IMAGE-CHAT dataset is a large collection of (image, style trait for speaker A, style trait for speaker B, dialogue between A & B) tuples that we collected using crowd-workers, Each dialogue consists of consecutive turns by speaker A and B. No particular constraints are placed on the kinds of utterance, only that we ask the speakers to both use the provided style trait, and to respond to the given image and dialogue history in an engaging way. The goal is not just to build a diagnostic dataset but a basis for training models that humans actually want to engage with.
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The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups.
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DIOR-RSVG is a large-scale benchmark dataset of remote sensing data (RSVG). It aims to localize the referred objects in remote sensing (RS) images with the guidance of natural language. This new dataset includes image/expression/box triplets for training and evaluating visual grounding models.
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TripClick is a large-scale dataset of click logs in the health domain, obtained from user interactions of the Trip Database health web search engine.
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A large-scale video dataset, featuring clips from movies with detailed captions.
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We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The dataset contains 579 QA pairs from 208 academic articles, with a majority from ML and NLP, as well as a subset of other scientific communities like Geoscience and Public Health. PeerQA supports three critical tasks for developing practical QA systems: Evidence retrieval, unanswerable question classification, and answer generation. We provide a detailed analysis of the collected dataset and conduct experiments establishing baseline systems for all three tasks. Our experiments and analyses reveal the need for decontextualization in document-level retrieval, where we find that even simple decontextualization approaches consistently improve retrieval performance across architectur
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A large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing.
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The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native corpus for studying statutory article retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus.
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NFCorpus is a full-text English retrieval data set for Medical Information Retrieval. It contains a total of 3,244 natural language queries (written in non-technical English, harvested from the NutritionFacts.org site) with 169,756 automatically extracted relevance judgments for 9,964 medical documents (written in a complex terminology-heavy language), mostly from PubMed.
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LLeQA is a French native dataset for studying information retrieval and long-form question answering in the legal domain. It consists of a knowledge corpus of 27,941 statutory articles collected from the Belgian legislation, and 1,868 legal questions posed by Belgian citizens and labeled by experienced jurists with a comprehensive answer rooted in relevant articles from the corpus.
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A novel dataset of 19th-century Latin American press texts, which addresses the lack of specialized corpora for historical and linguistic analysis in this region.
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MAPS-KB is a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. It is designed for the tasks of simile detection and component extraction.
Dataset Card for the ACR Appropriateness Criteria Corpus This dataset contains chunked guidelines and narratives from the ACR Appropriateness Criteria, an set of societal guidelines from the American College of Radiology (ACR) to help clinicians order appropriate diagnostic imaging studies for patients. The corpus is formatted similarly to the corpuses introduced in MedRAG by Xiong et al. (2024), and can therefore be similarly used for medical Retrieval-Augmented Generation (RAG).
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CURE is a retrieval dataset with a monolingual and two cross-lingual conditions, with splits spanning ten medical domains. Queries in CURE are natural language questions formulated by healthcare providers. They express the information needs of practitioners consulting academic literature in the course of their duties. Queries are available in English, French and Spanish. The corpus is constructed by mining an index of english passages extracted from biomedical academic articles.
An evaluation test bed for assessing the robustness of sentence embedding models against user-informed misinformation edits. A dataset containing perturbed and unperturbed claim pairs used for improving embedding model robustness through knowledge distillation.
CompMix-IR Dataset Overview:
DialogCC is a large-scale multi-modal dialogue dataset, which covers diverse real-world topics and various images per dialogue. It contains 651k unique images and is designed for image and text retrieval tasks.
The Multi-Eup is a new multilingual benchmark dataset, comprising 22K multilingual documents collected from the European Parliament, spanning 24 languages. This dataset is designed to investigate fairness in a multilingual information retrieval (IR) context to analyze both language and demographic bias in a ranking context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages, as well as cross-lingual relevance judgments. Furthermore, it offers rich demographic information associated with its documents, facilitating the study of demographic bias.
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PolyNews is a multilingual parallel dataset containing news titles 833 language pairs, spanning in 64 languages and 17 scripts.
A large-scale dataset for proactive document retrieval that consists of over 2.8 million conversations from Reddit.
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annotations_creators: - no-annotation language: - fr language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: French Legal Cases Dataset size_categories: - n>1M source_datasets: - la-mousse/INCA-17-01-2025 - la-mousse/JADE-17-01-2025 - la-mousse/CASS-17-01-2025 - la-mousse/CAPP-17-01-2025 task_categories: - text-generation - text-classification task_ids: - language-modeling - entity-linking-classification - fact-checking - intent-classification - multi-label-classification - multi-input-text-classification - natural-language-inference - semantic-similarity-classification - sentiment-classification - topic-classification - sentiment-analysis - named-entity-recognition - parsing - extractive-qa - open-domain-qa - closed-domain-qa - news-articles-summarization - news-articles-headline-generation - dialogue-modeling - dialogue-generation - abstractive-qa - closed-domain-qa - keyword-spotting - semantic-segmentation - tabular-multi-class-classification -
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