datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
enron-qa-questions-dasovich-jLSAT_Questionsjee-main-questions
JEE Main — Question Bank
A structured dataset of JEE Main examination questions with full metadata,
worked solutions, and diagrams. Built for education, ML training, and
question-generation use cases.
Subsets:
Chemistry — 738 questions from 28 papers
Physics — 768 questions from 28 papers
Mathematics — 801 questions from 28 papers
Over 2,300 questions across the three core JEE subjects.
Structure
Organised into subsets by subject and splits (train / test):… See the full description on the dataset page: https://huggingface.co/datasets/eQOURSE/jee-main-questions.reddit_question_best_answersQuestion & question body together with the best answers to that question from Reddit.
The score for the question / answer is the upvote count (i.e. positive-negative upvotes).
Only questions / answers that have these properties were extracted:
min_score = 3
min_title_len = 20
min_body_len = 100
Natural_Questions_HTMLThis is a dataset extracted from the Natural Questions dataset
This dataset is currently under development
student-question-categoriesThis is the IITJEE NEET AIIMS Students Questions Data dataset.
It categorizes university entry questions into 4 categories: Physics, Chemistry, Biology, and Mathematics.
cybersecurity_full_question_answersrakuda-questions
Rakuda - Questions for Japanese models
Repository: https://github.com/yuzu-ai/japanese-llm-ranking
This is a set of 40 questions in Japanese about Japanese-specific topics designed to evaluate the capabilities of AI Assistants in Japanese.
The questions are evenly distributed between four categories: history, society, government, and geography.
Questions in the first three categories are open-ended, while the geography questions are more specific.
Answers to these questions can be… See the full description on the dataset page: https://huggingface.co/datasets/yuzuai/rakuda-questions.insincere-questionsThis is a version of the Quora Insincere Questions Classification.
An insincere question is defined as a question intended to make a statement rather than look for helpful answers. About 6% of questions are labeled as insincere.
natural-questions-shortresearchy_questions
Introduction
Researchy Questions is a set of about 100k Bing queries that users spent the most effort on. After a labor-intensive filtering funnel from billions of queries, these "needles in the haystack" are non-factoid, multi-perspective questions that probably require a lot of sub-questions and research in order to answer adequetly. These questions are shown to be harder than other open domain QA datasets like Natural Questions.
The train dataset has about 90k samples.… See the full description on the dataset page: https://huggingface.co/datasets/corbyrosset/researchy_questions.natural_questions_cleanjee-main-questions
JEE Main — Question Bank
A structured dataset of JEE Main examination questions with full metadata,
worked solutions, and diagrams. Built for education, ML training, and
question-generation use cases.
Subsets:
Chemistry — 738 questions from 28 papers
Physics — 768 questions from 28 papers
Mathematics — 801 questions from 28 papers
Over 2,300 questions across the three core JEE subjects.
Structure
Organised into subsets by subject and splits (train / test):… See the full description on the dataset page: https://huggingface.co/datasets/soughed/jee-main-questions.jee-advanced-questions
JEE Advanced — Question Bank
A structured dataset of JEE Advanced examination questions with full
worked solutions and diagrams. JEE Advanced questions are more analytical
than JEE Main — many are subjective, integer, or numerical-answer type with
detailed multi-step solutions.
Subsets (PCM):
Physics — 50 questions
Chemistry — 21 questions
Mathematics — 48 questions
Structure
Organised into subsets by subject and splits (train / test):
mathematics/ physics/… See the full description on the dataset page: https://huggingface.co/datasets/eQOURSE/jee-advanced-questions.Natural_Questions_HTML_Toyscientific-question-outcomes
Scientific Question Outcomes
980 astronomy research questions, frozen at five historical cutoffs, each
labelled with what the following five years of literature actually did with
it.
Systems that propose research questions are usually evaluated by asking a
person or a model how good the questions sound. This dataset supplies the
alternative: questions frozen using only pre-cutoff literature, and outcome
labels drawn from the literature published afterwards. It is, to our… See the full description on the dataset page: https://huggingface.co/datasets/huiluckylucky/scientific-question-outcomes.multihop-question-decompositiontext-sft-questions-answers-only
text-sft: Questions and Answers
This dataset consists of question-and-answer pairs generated from short excerpts drawn from Wikipedia, Cosmopedia, and FineWeb-Edu. It is an adapted version of agentlans/text-sft.
Overview
The dataset provides compact examples of English question-and-answer relationships that can help models learn linguistic patterns, syntactic structures, and semantic associations between questions and their corresponding answers.
Intended Use… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/text-sft-questions-answers-only.natural-questions-slim-short-answer
Natural Questions Slim Short Answer
This is a slim, flattened derived version of
google-research-datasets/natural_questions
for short-answer question answering experiments.
The conversion keeps examples with extractable short answers and removes the
original document HTML, token-level document spans, long answer candidates, and
yes/no-only examples. Each record is a simple question-answer pair. It is
intended for lightweight QA prompting and evaluation, not as a full replacement
for… See the full description on the dataset page: https://huggingface.co/datasets/BOB12311/natural-questions-slim-short-answer.Natural_Questions_HTML_reduced_alljee-main-questions
JEE Main — Question Bank
A structured dataset of JEE Main examination questions with full metadata,
worked solutions, and diagrams. Built for education, ML training, and
question-generation use cases.
Subsets:
Chemistry — 738 questions from 28 papers
Physics — 768 questions from 28 papers
Mathematics — 801 questions from 28 papers
Over 2,300 questions across the three core JEE subjects.
Structure
Organised into subsets by subject and splits (train / test):… See the full description on the dataset page: https://huggingface.co/datasets/Grass-G/jee-main-questions.quora-question-answer-datasetQuora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
Usage:
For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset
forecastbench-single_question
ForecastBench Single Questions
This dataset contains single-ID forecasting questions derived from the ForecastBench project. It includes two configurations:
forecastbench_single_questions_2024-12-08: Contains 429 forecasting questions with resolved real-world outcomes.
forecastbench_single_questions_human_2024-07-21: Contains 473 questions with resolved real-world outcomes, augmented with human forecast probabilities from public and superforecaster groups.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Duruo/forecastbench-single_question.CFQA_Chinese_Finance_Question_Answering
Citation
For the complete project, please check Here
If you use CFQA in your research, experiments, benchmarks, or publications, please cite the accompanying paper:
@inproceedings{zhu2026cfqa,
title = {CFQA: A Chinese Financial Question Answering Benchmark From Corporate Annual Reports},
author = {Tianning Zhu and Mo Liu and Murathan Kurfali},
booktitle = {Proceedings of The 7th Financial Narrative Processing Workshop (FNP 2026)},
year = {2026},
address =… See the full description on the dataset page: https://huggingface.co/datasets/ZackZhu00/CFQA_Chinese_Finance_Question_Answering.SQuAD-EN-Passage-to-Question
Dataset Card for SQuAD-EN-Passage-to-Question
Dataset Summary
SQuAD-EN-Passage-to-Question is a reformatted and reorganized version of the Stanford Question Answering Dataset (SQuAD). The dataset is designed for text generation and question generation research tasks.
In the original SQuAD dataset, each context passage is associated with multiple question-answer pairs stored as separate entries. In this modified version, all questions associated with the same context… See the full description on the dataset page: https://huggingface.co/datasets/Siam0703/SQuAD-EN-Passage-to-Question.aggregative_questions
AI21-Hotels and AI21-WorldCup datasets
The AI21-Hotels and AI21-WorldCup datasets were created to support research on aggregative question answering in open-book settings.
Aggregative questions require retrieving information from a large set of documents and applying reasoning over the collected text snippets.
For example, the question “What is the fewest number of total goals scored in any single World Cup?” cannot usually be answered by a single passage.
Instead, one must gather… See the full description on the dataset page: https://huggingface.co/datasets/ai21labs/aggregative_questions.minecraft-question-answer-700k
minecraft-question-answer-700k
Introducing the largest synthetic Minecraft Q&A dataset, covering every topic, game mechanic, item and craft in Minecraft. The dataset was generated by extracting over 18,000 Minecraft wiki pages, and using glaive.ai's synthetic data generation pipeline.
about the dataset
rows - 694,814
tokens - 47,133,624
source - https://minecraft.wiki/
Hit me up on twitter if you see a bug or need a synthetic dataset for your company:… See the full description on the dataset page: https://huggingface.co/datasets/naklecha/minecraft-question-answer-700k.stackoverflow_DL-related_questionsFinance-Questions-Essay_and_Calculation-Chinese
Overview
Finance-Questions-Essay_and_Calculation-Chinese is a carefully curated financial reasoning dataset containing 954 samples, each annotated with high-quality Chain-of-Thought (CoT) reasoning. It is designed to train and evaluate Chinese financial language models on complex essay and calculation tasks.
Stage 1: Data Collection & Standardization
Extract financial question samples from professional textbooks via Easy Dataset.
Manually label 30 seed samples, then use… See the full description on the dataset page: https://huggingface.co/datasets/Anson1110/Finance-Questions-Essay_and_Calculation-Chinese.chaii-hindi-and-tamil-question-answering