Malikeh1375/medical-question-answering-datasets dataset hosted on Hugging Face and contributed by the HF Datasets community

SubjQA is a question answering dataset that focuses on subjective questions and answers. The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants.

A JSON formatted dataset comprising 197,180 question and answer pairs covering a wide range of topics encountered in a Bachelor level psychology course. I have included a broad range of question types, topics, and answer styles. The dataset was created using personal notes and several LLMs (such as GPT4) and manually assessed for veracity and completeness of response. Despite this, the size of the dataset prohibits me from ensuring every single answer is 100% accurate and up-to-date. As such… See the full description on the dataset page: https://huggingface.co/datasets/BoltMonkey/psychology-question-answer.

LangChainDatasets/question-answering-paul-graham dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for SQuAD

  Dataset Summary

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles.

  Supported Tasks and Leaderboards

Question Answering.… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.

MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average.

petkopetkov/medical-question-answering-all dataset hosted on Hugging Face and contributed by the HF Datasets community

LangChainDatasets/question-answering-state-of-the-union dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "wiki_qa"

  Dataset Summary

Wiki Question Answering corpus from Microsoft. The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure





  Data Instances





  default

Size of downloaded dataset files: 7.10 MB Size… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/wiki_qa.

FQuAD: French Question Answering Dataset We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs. Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.

taesiri/video-game-question-answering dataset hosted on Hugging Face and contributed by the HF Datasets community

SYNTHETIC-1

This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here

open-source-metrics/document-question-answering-checkpoint-downloads dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "qasc"

  Dataset Summary

QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure





  Data Instances





  default

Size of… See the full description on the dataset page: https://huggingface.co/datasets/allenai/qasc.

Dataset Card for "coqa"

  Dataset Summary

CoQA is a large-scale dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage.

  Supported Tasks and Leaderboards

More Information Needed

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/coqa.

Dataset Card for OpenBookQA

  Dataset Summary

OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of… See the full description on the dataset page: https://huggingface.co/datasets/allenai/openbookqa.

Dataset Card for Natural Questions

  Dataset Summary

The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.

  Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/rongzhangibm/NaturalQuestionsV2.

Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. 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. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.

Dataset Card for SQuAD 2.0

  Dataset Summary

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2.

This dataset:

This is a visual question-answering dataset cleaned from "Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension". I only keep the questions that require the diagrams. For the whole dataset including more annotations such as captions, non-diagram questions, please check their webpage: https://prior.allenai.org/projects/tqa

  Citation

Please cite the paper if you use this dataset.… See the full description on the dataset page: https://huggingface.co/datasets/yyyyifan/TQA.