datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NLU-Question-Answering
SEA Question Answering
SEA Question Answering evaluates a model's ability to predict a contiguous span of characters that answers the question about a given passage. It is sampled from TyDi QA-GoldP for Indonesian, IndicQA for Tamil, and XQuaD for Thai and Vietnamese.
Supported Tasks and Leaderboards
SEA Question Answering is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/NLU-Question-Answering.medical-question-answering-datasetsstackexchange_titlebody_best_and_down_voted_answer_jsonlThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.stackexchange_title_best_voted_answer_jsonlThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.stackexchange_titlebody_best_voted_answer_jsonlThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.text-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.answers-with-reasoning-mmlu-pro
answers-with-reasoning-mmlu-pro
Self-distillation SFT corpus: Qwen3-8B-Instruct's own correct
chain-of-thought rollouts on MMLU-Pro multiple-choice questions
(general-QA domain).
Generation
Source problems: TIGER-Lab/MMLU-Pro test split (12,032 multiple-choice questions across 14 subject categories).
Sampling model: qwen/qwen3-8b via OpenRouter (providers: Alibaba, AtlasCloud) with reasoning enabled.
Sampling parameters: temperature=0.6, top_p=0.95, max_tokens=8000.… See the full description on the dataset page: https://huggingface.co/datasets/abhayesian/answers-with-reasoning-mmlu-pro.answerable_tydiqa
Dataset Card for "answerable-tydiqa"
Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages.
Answerable TyDi QA is an extension of the GoldP subtask of the original TyDi QA dataset to also include unanswertable questions.
Dataset Structure
The dataset contains a train and a validation set, with 116067 and 13325 examples, respectively. Access them with
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/copenlu/answerable_tydiqa.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.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
Chinese_Question_Answering_DatasetMMLU-SemiProThis dataset is derived from TIGER-Lab/MMLU-Pro as part of our MMLU-Leagues Encoder benchmark series, containing:
MMLU-Amateur, where the train set contains all questions Llama-3-8B-Instruct (5-shot) gets wrong and the test set contains all questions it gets right. The aim is to measure the ability of an encoder, with relatively limited training data, to match the performance of a small frontier model.
MMLU-SemiPro (this dataset), where the data is evenly split between a train and a test set.… See the full description on the dataset page: https://huggingface.co/datasets/answerdotai/MMLU-SemiPro.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.IMO-AnswerBench-Verified
IMO AnswerBench Verified
IMO AnswerBench Verified is a human-expert-verified derivative of OpenEvals/IMO-AnswerBench, originally curated by the Google DeepMind Superhuman Reasoning team. Every record in the 400-problem benchmark was reviewed individually. The review identified and corrected 13 records while preserving the benchmark's balanced coverage of four major mathematical areas.
Dataset summary
Total records: 400
Verification method: record-by-record human… See the full description on the dataset page: https://huggingface.co/datasets/dots-studio/IMO-AnswerBench-Verified.chaii-hindi-and-tamil-question-answeringmedical-question-answering-datasetsquran-question-answer-context
Dataset Card for "quran-question-answer-context"
Dataset Summary
Translated the original dataset from Arabic to English and added the Surah ayahs to the context column.
Usage
from datasets import load_dataset
dataset = load_dataset("nazimali/quran-question-answer-context")
DatasetDict({
train: Dataset({
features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id'… See the full description on the dataset page: https://huggingface.co/datasets/nazimali/quran-question-answer-context.medical-question-answering-datasetsdocvqa-single-page-questions-answer-ocr
DocVQA with Answer Localization
This dataset provides answer-localization annotations produced by our pipeline on top of the DocVQA dataset.
Usage
from datasets import load_dataset
# Load the dataset with answer OCR annotations
ds = load_dataset("indrehus/docvqa-single-page-questions-answer-ocr", split="validation")
# Get a single sample
sample = ds[0]
# Available fields in each sample:
print("Image:", sample["image"]) # PIL.Image
print("Question:"… See the full description on the dataset page: https://huggingface.co/datasets/indrehus/docvqa-single-page-questions-answer-ocr.omnimcp_graphrag_grounded_answer_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_graphrag_grounded_answer_teaser.Question-Answering-Generation-Choices
The dataset is a merged compilation of QuAIL, RACE, and Cosmos QA datasets,
having undergone preprocessing.
psychology-question-answerA 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.bodo-legal-question-answering-ai4bharat
Bodo Legal Question Answering Dataset
Overview
This dataset is a Bodo-language legal Question Answering (QA) resource
created for research in low-resource Natural Language Processing (NLP)
and legal language processing.
The supplied source files contain legal judgment contexts together with
multiple questions and answers. For Hugging Face compatibility and
question-answering model training, each question-answer pair has been
flattened into a separate JSONL example… See the full description on the dataset page: https://huggingface.co/datasets/Mwnthai/bodo-legal-question-answering-ai4bharat.IELTs-Speaking-answer
Overview
This dataset consists of 2 json files named 'ielts_new.json' and 'ielts_old.json', which contain ielts questions and its corresponding answers for part 1 and part 2.
'ielts_new.json': new IELTs topics for 2024 September-December.
'ielts_old.json': remained IELTs topics for 2024 September-December.
Quality
Since the dataset is analysed and generated by ChatGPT based on my own pdf file, the answer may be incomplete(only part of the sentence is extracted, leading to… See the full description on the dataset page: https://huggingface.co/datasets/qwertyuiopasdfg/IELTs-Speaking-answer.mcqa-single-answer
Dataset Summary
EVE-mcqa-single-answer is a Multiple-Choice Question Answering (MCQA) dataset designed to evaluate the performance of language models in the domain of Earth Observation (EO). The dataset consists of questions related to EO concepts, technologies, and applications, each accompanied by multiple answer choices with exactly one correct answer.
Unlike multi-answer MCQA datasets, each question in this dataset has only a single correct choice, making it suitable for… See the full description on the dataset page: https://huggingface.co/datasets/eve-esa/mcqa-single-answer.lfqa-max-answer-length-512
Dataset Card
Dataset Description
The dataset contains simple, long-form answers to questions and corresponding contexts.
Similar to ELI5 but with context.
This dataset is a filtered version of LLukas22/lfqa_preprocessed,
which in turn is a processed and simplified version of of vblagoje's lfqa_support_docs and lfqa datasets.
I have filtered out overly long answers, based on the number of tokens in the answer using the LED tokenizer.
It can be reproduced with the notebook… See the full description on the dataset page: https://huggingface.co/datasets/stefanbschneider/lfqa-max-answer-length-512.aio-extended-answers
AIO with extended answers
AIO (AI王) is a Japanese quiz dataset.
This repository contains the validation set of Version 2.0, augmented with manually annotated valid answers for each question.
Source: AI王 〜クイズAI日本一決定戦〜
Data Fields
qid (str): Unique identifier for each entry in the dataset.
competition (str): The name of the competition that the data was first created for.
timestamp (str): The timestamp when the data was created.
section (str): The split the data belongs… See the full description on the dataset page: https://huggingface.co/datasets/sbintuitions/aio-extended-answers.civil-human-rights-question-answering
Dataset Card for rag-prompt
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/rag-prompt/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/sdiazlor/civil-human-rights-question-answering.enamed-2025-answers
ENAMED 2025 Answer Keys & Errata
Official answer key (Gabarito Oficial) and scoring metadata for INEP's ENAMED 2025 exam.
Dataset Summary
100 Answer Records matching IDs in gustavokch/enamed-2025.
Errata & Annulment Tracking: Includes is_annulled flag and errata notes.
Usage
from datasets import load_dataset
answers = load_dataset("gustavokch/enamed-2025-answers", split="test")
print(answers[0])
eli5_question_answer_danish
ELI5 question-answer pairs in Danish
About
This dataset is a version of the ELI5 question-answer pairs dataset machine-translated from English to Danish (link to original dataset).
Machine translation is performed using the Helsinki NLP English-to-Danish OPUS-MT model.
The dataset contains ~209k question-answer pairs and can be used to train embedding and question-answer models. Each pair consists of one question ('query') and one passage containing the answer… See the full description on the dataset page: https://huggingface.co/datasets/KennethTM/eli5_question_answer_danish.