datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
medical-question-answering-datasetsCEH_question_answerextractive_qa_question_answering_hr
Dataset Card
HR-Multiwoz is a fully-labeled dataset of 5980 extractive qa spanning 10 HR domains to evaluate LLM Agent. It is the first labeled open-sourced conversation dataset in the HR domain for NLP research.
Please refer to HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent for details about the dataset construction.
Dataset Sources
Repository: xwjzds/extractive_qa_question_answering_hr
Paper: HR-MultiWOZ: A Task Oriented Dialogue (TOD)… See the full description on the dataset page: https://huggingface.co/datasets/xwjzds/extractive_qa_question_answering_hr.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.Financial_Question_Answeringreddit_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
stackexchange-question-answering
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
cybersecurity_full_question_answersMedQuAD_47441_Question_Answer_Pairs
Dataset Card for "MedQuAD_47441_Question_Answer_Pairs"
More Information needed
medical-question-answering-splitnq-question-answeronlytask290_tellmewhy_question_answerability
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task290_tellmewhy_question_answerability
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task290_tellmewhy_question_answerability.tamil-question-answering-datasetthis dataset contains 5 columns
context, question, answer_start, answer_text, source
Column
Description
context
A general small paragraph in tamil language
question
question framed form the context
answer_text
text span that extracted from context
answer_start
index of answer_text
source
who framed this context, question, answer pair
source
team KBA => (Karthi, Balaji, Azeez) these people manually created
CHAII =>a kaggle competition
XQA => multilingual QA… See the full description on the dataset page: https://huggingface.co/datasets/AswiN037/tamil-question-answering-dataset.medical-question-answering-datasetsvisual-question-answering-checkpoint-downloadschaii-hindi-and-tamil-question-answeringvisual-question-answering-cocoChinese_Question_Answering_DatasetCFQA_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.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.task865_mawps_addsub_question_answering
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task865_mawps_addsub_question_answering
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task865_mawps_addsub_question_answering.Question-Answering-Generation-Choices
The dataset is a merged compilation of QuAIL, RACE, and Cosmos QA datasets,
having undergone preprocessing.
Question_answer_pairsvideo-game-question-answeringOWASP-question-answer-datasetquestion-answering-paul-grahamquestion-answering-ukrainianquestion-answering-state-of-the-unionquestion-answering-ukrainian-json-answersquora-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