Dataset Card for TweetQA

  Dataset Summary

With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure… See the full description on the dataset page: https://huggingface.co/datasets/ucsbnlp/tweet_qa.

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 COVID-QA

  Dataset Summary

COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

The text in the dataset is in English.

  Dataset Structure





  Data… See the full description on the dataset page: https://huggingface.co/datasets/deepset/covid_qa_deepset.

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.

Dataset Card for Narrative QA

  Dataset Summary

NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents.

  Supported Tasks and Leaderboards

The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question.… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/narrativeqa.

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.

HotpotQA An MTEB dataset Massive Text Embedding Benchmark

HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems.

Task category t2t

Domains Web, Written

Reference https://hotpotqa.github.io/

  How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code: import mteb

task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/hotpotqa.

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.

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.

We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives.

Dataset Card for Alpaca

  Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

The text-davinci-003 engine to generate the instruction data instead… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.

Dataset Card for LLM-Generated QA Dataset for Sentence Transformers

  Dataset Summary

This dataset contains question-answer pairs generated by a large language model (LLM) for training sentence transformer models. Each entry includes a query, a main response, and various metadata fields to provide context and facilitate different downstream tasks.

  Supported Tasks and Leaderboards

The dataset is primarily designed for:

Open-domain question answering Text… See the full description on the dataset page: https://huggingface.co/datasets/bobox/multi-signals-QA-dataset.

🧠 Generative AI QA Dataset

  📝 Dataset Overview

This dataset consists of 10,000 high-quality question-answer pairs focused on Generative AI. It is designed for instruction tuning of text-to-text generation models, enabling improved performance on AI-related question-answering tasks.

  ⚠️ Important Note

This dataset was originally intended for use in AWS AI Singapore 2025. However, the fine-tuned model performed very inaccurately because of my lack of prompt… See the full description on the dataset page: https://huggingface.co/datasets/nglif/GenerativeAI-QA-Dataset.

Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context

Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions

  Dataset Summary

AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions containing… See the full description on the dataset page: https://huggingface.co/datasets/sewon/ambig_qa.

abhayesian/principles-qa-llama-formatted-text dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for WEBNLG-QA

  Dataset Summary

WEBNLG-QA is a conversational question answering dataset grounded on WEBNLG. It consists in a set of question-answering dialogues (follow-up question-answer pairs) based on short paragraphs of text. Each paragraph is associated a knowledge graph (from WEBNLG). The questions are associated with SPARQL queries.

  Supported tasks

Knowledge-based question-answering SPARQL-to-Text conversion

  Knowledge based… See the full description on the dataset page: https://huggingface.co/datasets/Orange/webnlg-qa.

SStanford 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. This Hebrew dataset is an automatic translation of the English SQuAD dataset.

Dataset Card for GPQA

GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google. We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.