Omkar2118/datasets huggingface dataset:
HotpotQA

**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. 

A diverse range of reasoning strategies are featured in HotpotQA, including questions involving missing entities in the question, intersection questions (What satisfies property A and property B?), and comparison questions, where two entities are compared by a common attribute, among others. In the few-document distractor setting, the QA models are given ten paragraphs in which the gold paragraphs are guaranteed to be found; in the open-domain fullwiki setting, the models are only given the question and the entire Wikipedia. Models are evaluated on their answer accuracy and explainability, where the former is measured as overlap between the predicted and gold answers with exact match (EM) and unigram F1, and the latter concerns how well the predicted supporting fact sentences match human annotation (Supporting Fact EM/F1). A joint metric is also reported on this dataset, which encourages systems to perform well on both tasks simultaneously.

Source: [Answering Complex Open-domain Questions Through Iterative Query Generation](https://arxiv.org/abs/1910.07000)
Image Source: [Yang et al](https://arxiv.org/pdf/1809.09600v1.pdf)

Introducing paper: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
QuAC
Full name: Question Answering in Context

Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in total. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text.

Source: [https://paperswithcode.com/paper/quac-question-answering-in-context-1/](https://paperswithcode.com/paper/quac-question-answering-in-context-1/)
Image Source: [https://paperswithcode.com/paper/quac-question-answering-in-context-1/](https://paperswithcode.com/paper/quac-question-answering-in-context-1/)

Introducing paper: QuAC: Question Answering in Context