datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
prompts.chat
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts.
📢 Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
🌐 Website: prompts.chat
📦 GitHub: github.com/f/awesome-chatgpt-prompts
About
prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community. The project can… See the full description on the dataset page: https://huggingface.co/datasets/fka/prompts.chat.ChatGPT-Jailbreak-Prompts
Dataset Card for Dataset Name
Name
ChatGPT Jailbreak Prompts
Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
Languages
[English]
balanced-copa
Dataset Card for "Balanced COPA"
Dataset Summary
Bala-COPA: An English language Dataset for Training Robust Commonsense Causal Reasoning Models
The Balanced Choice of Plausible Alternatives dataset is a benchmark for training machine learning models that are robust to superficial cues/spurious correlations. The dataset extends the COPA dataset(Roemmele et al. 2011) with mirrored instances that mitigate against token-level superficial cues in the original COPA answers. The… See the full description on the dataset page: https://huggingface.co/datasets/pkavumba/balanced-copa.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.MMAD
MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
💡 This dataset is the full version of MMAD
Content：Containing both questions, images, and captions.
Questions: All questions are presented in a multiple-choice format with manual verification, including options and answers.
Images：Images are collected from the following links：
DS-MVTec
, MVTec-AD
, MVTec-LOCO
, VisA
, GoodsAD.
We retained the mask… See the full description on the dataset page: https://huggingface.co/datasets/jiang-cc/MMAD.customer-support-tickets
Featuring Labeled Customer Emails and Support Responses
🔧 Synthetic IT Ticket Generator — Custom Dataset
Create a dataset tailored to your own queues & priorities (no PII).
👉 Generate custom data
Define your queues, priorities, language
Need an on-prem AI to auto-classify tickets?→ Open Ticket AI
There are 2 Versions of the dataset, the new version has more tickets, but only languages english and german. So please look at both files, to find what best fits… See the full description on the dataset page: https://huggingface.co/datasets/Tobi-Bueck/customer-support-tickets.cti-bench
Dataset Card for CTIBench
A set of benchmark tasks designed to evaluate large language models (LLMs) on cyber threat intelligence (CTI) tasks.
Dataset Details
Dataset Description
CTIBench is a comprehensive suite of benchmark tasks and datasets designed to evaluate LLMs in the field of CTI.
Components:
CTI-MCQ: A knowledge evaluation dataset with multiple-choice questions to assess the LLMs' understanding of CTI standards, threats, detection strategies… See the full description on the dataset page: https://huggingface.co/datasets/AI4Sec/cti-bench.function_calling_extended
Trelis Function Calling Dataset
UPDATE: As of Dec 5th 2023, there is a v3 of this dataset now available from here.
Allows models to be fine-tuned for function-calling.
The dataset is human generated and does not make use of Llama 2 or OpenAI!
Contains 59 training and 17 test rows
Based on eight functions: search_bing, search_arxiv, save_chat, read_json_file, list_files, get_current_weather, delete_file, clear_chat
Access this dataset by purchasing a license HERE.
Alternatively… See the full description on the dataset page: https://huggingface.co/datasets/Trelis/function_calling_extended.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.CogStream
CogStream Dataset
Dataset for CogStream: Context-guided Streaming Video Question Answering.
Overview
CogStream is a streaming video QA dataset designed to evaluate context-guided video reasoning. Models must identify and utilize relevant historical context to answer questions about ongoing video streams.
Statistics:
Split
Videos
QA Pairs
Train
852
55,623
Test
236
15,364
Total
1,088
70,987
Sources: MovieChat (40.2%), MECD (16.8%), QVhighlights (9.8%)… See the full description on the dataset page: https://huggingface.co/datasets/SII-KYW/CogStream.Bitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.Asclepius-Synthetic-Clinical-Notes
Asclepius: Synthetic Clincal Notes & Instruction Dataset
Dataset Summary
This dataset is official dataset for Asclepius (arxiv)
This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs.
We first synthesized synthetic notes from PMC-Patients case reports with GPT-3.5
Then, we generate instruction-answer pairs for 157k synthetic discharge summaries
Supported Tasks
This dataset covers below 8 tasks
Named Entity… See the full description on the dataset page: https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes.ClawBenchPro
ClawBenchPro
ClawBenchPro is a compact, builder-based workplace-agent benchmark package exported from Nanoclaw.
It contains task YAML files, prompts, task-local environment builders, skills, evaluation manifests,
provenance metadata, and checksums.
Included Splits
Dataset
Tasks
Groups
round_01_aligned_mix_800
800
base, hard_aligned, multi_turn_aligned, skills_aligned
persona_aligned_mix_200
200
base, hard, multi_turn, skills
Directory Layout… See the full description on the dataset page: https://huggingface.co/datasets/ErenJaegerYeager/ClawBenchPro.Traditional-Chinese-Medicine-Multiple_choice_question
Discription
This dataset is sourced from the website of the Ministry of Examination, R.O.C (Taiwan) and contains past exam questions from the national Traditional Chinese Medicine examinations in Taiwan. The exam comprises six subjects. This dataset specifically includes questions from two subjects, including the History of Traditional Chinese Medicine, Basic Theories of Traditional Chinese Medicine, Neijing, Nanjing, Traditional Chinese Medicine Prescription Studies, and… See the full description on the dataset page: https://huggingface.co/datasets/Liavan/Traditional-Chinese-Medicine-Multiple_choice_question.Wikipedia_contradict_benchmark
Wikipedia contradict benchmark
Wikipedia contradict benchmark is a dataset consisting of 253 high-quality, human-annotated instances designed to assess LLM performance when augmented with retrieved passages containing real-world knowledge conflicts. The dataset was created intentionally with that task in mind, focusing on a benchmark consisting of high-quality, human-annotated instances.
Dataset Details
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/Wikipedia_contradict_benchmark.ChemistryQAChemistryQA is a complex QA task which cannot be solved by end-to-end neural networks. To answer chemical questions, machines need to understand questions, apply chemistry and math knowledge, and do calculation and reasoning. ChemistryQA contains about 4500 questions covering around 200 chemistry topics, which are collected from https://socratic.org/chemistry.
All credits go to chemistry-qa project by Microsoft (https://github.com/microsoft/chemistry-qa)
Trademarks
This project may contain… See the full description on the dataset page: https://huggingface.co/datasets/avaliev/ChemistryQA.OR-Space
OR-Space
A full-lifecycle workspace benchmark for industrial optimization agents.
OR-Space evaluates whether language-model agents can work reliably with
operations research problems represented as executable, multi-file workspaces.
Rather than presenting a self-contained mathematical prompt, each task
distributes evidence across business requirements, structured data, source
code, execution logs, and solver records.
The benchmark contains 100 optimization topologies. Each… See the full description on the dataset page: https://huggingface.co/datasets/Chenyu-Zhou/OR-Space.Python-Code-Solutions
Python Code Solutions
Features
1000k of Python Code Solutions for Text Generation and Question Answering
Python Coding Problems labelled by topic and difficulty
Recommendations
Train your Model on Logical Operations and Mathematical Problems Before Training it on this. This is optional for Fine Tuning 2B parameter + models.
Format the prompts in a orderly way when formatting data eg. {question} Solution: {solution} Topic: {topic}
CausalReasoningBenchmark
Automated Causal Reasoning Benchmark
Overview
The Automated Causal Reasoning Benchmark is a collection of real-world causal inference tasks drawn from 85 peer-reviewed research papers and three textbook-style collections (see CausalBenchmark.pdf). The benchmark contains 173 queries over 138 datasets. Each task is designed to evaluate both (i) identification, i.e., selecting an appropriate causal estimand and identification strategy given the study context, and (ii)… See the full description on the dataset page: https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark.Shifaa_Arabic_Medical_Consultations
Shifaa Arabic Medical Consultations 🏥📊
Overview 🌍
Shifaa is revolutionizing Arabic medical AI by addressing the critical gap in Arabic medical datasets. Our first contribution is the Shifaa Arabic Medical Consultations dataset, a comprehensive collection of 84,422 real-world medical consultations covering 16 Main Specializations and 585 Hierarchical Diagnoses.
🔍 Why is this dataset important?
First large-scale Arabic medical dataset for AI applications.… See the full description on the dataset page: https://huggingface.co/datasets/Ahmed-Selem/Shifaa_Arabic_Medical_Consultations.persian-poetry-metersPersian poems with their corresponding meters, from ganjoor.net.
LongDA
LongDA Dataset Card
Dataset Description
LongDA is a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. It features authentic U.S. government survey data with complete, long documentation, testing LLMs' ability to navigate complex real-world datasets before performing analysis.
Dataset Summary
505 queries extracted from 30 expert-written publications
17 U.S. national surveys covering health… See the full description on the dataset page: https://huggingface.co/datasets/callmephilip/LongDA.CommonsenseQA-Explained-by-ChatGPTThis is a dataset with explanations from ChatGPT for the correct and incorrect answers in CommonsenseQA. The explanations are generated by prompting ChatGPT with answer keys and in-context examples. We expect this dataset to be an useful source for understanding the commonsense reasoning ability of LLMs or training other LMs.
korean-bar-exam-hard-current-law-precedent-sft-1000
Korean Current-Law Bar Exam Hard SFT 1000
대한민국 현행 법령을 기준으로 만든 변호사시험 선택형 고난도 스타일 SFT 데이터 1,000문항입니다.
초기 직접 조문확인형 생성본은 실제 제14ㆍ15회 변호사시험보다 쉬워서, 이 버전은 다음 기준으로 다시 만들었습니다.
ㄱ/ㄴ/ㄷ/ㄹ 복합정오형 중심
甲/乙/丙, 검사ㆍ사법경찰관ㆍ행정청ㆍ회사ㆍ소송당사자 등이 등장하는 사례형 비중 확대
단순 근거 조문 선택형 제거
정답뿐 아니라 각 지문별 O/X 이유와 참고 법령 조문 제공
제15회 변호사시험 data/questions.csv와 높은 유사도 문항 제외
Files
data/questions.csv: Hugging Face preview용 메인 CSV입니다.
sft/train.jsonl: messages 형식 SFT용 JSONL입니다.
metadata/qa_report.json: 생성 수량, 난도 관련… See the full description on the dataset page: https://huggingface.co/datasets/gyung/korean-bar-exam-hard-current-law-precedent-sft-1000.VivaBench
VivaBench: Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in LLMs
This repository is the official implementation of VivaBench—“Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models.”
VivaBench is a multi-turn benchmark of 1,152 physician-curated clinical vignettes that simulates a viva voce (oral) exam: agents must iteratively gather H&P findings and order investigations to arrive at a diagnosis.
📋 Requirements… See the full description on the dataset page: https://huggingface.co/datasets/chychiu/VivaBench.CrosswordQA
Dataset Card for CrosswordQA
Dataset Summary
The CrosswordQA dataset is a set of over 6 million clue-answer pairs scraped from the New York Times and many other crossword publishers. The dataset was created to train the Berkeley Crossword Solver's QA model. See our paper for more information. Answers are automatically segmented (e.g., BUZZLIGHTYEAR -> Buzz Lightyear), and thus may occasionally be segmented incorrectly.
Supported Tasks and Leaderboards
[Needs… See the full description on the dataset page: https://huggingface.co/datasets/albertxu/CrosswordQA.question-type-and-complexity
Question Type and Complexity (QTC) Dataset
Dataset Overview
The Question Type and Complexity (QTC) dataset is a comprehensive resource for linguistics/NLP research focusing on question classification and linguistic complexity analysis across multiple languages. It contains questions from two distinct sources (TyDi QA and Universal Dependencies v2.15), automatically annotated with question types (polar/content) and a set of linguistic complexity features.
Key Features:
2… See the full description on the dataset page: https://huggingface.co/datasets/rokokot/question-type-and-complexity.lm-pragmatics
Citation
@misc{hu2023finegrainedcomparisonpragmaticlanguage, title={A fine-grained comparison of pragmatic language understanding in humans and language models}, author={Jennifer Hu and Sammy Floyd and Olessia Jouravlev and Evelina Fedorenko and Edward Gibson}, year={2023}, eprint={2212.06801}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2212.06801},}
Bitext-telco-llm-chatbot-training-dataset
Bitext - Telco Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset.CodeMixBench
ℹ️Dataset Card for CodeMixBench
[EMNLP'25] CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages
Code-mixing is a linguistic phenomenon where multilingual speakers switch or mix two or more languages within a single utterance or conversation.
To evaluate LLMs’ comprehension of multilingual code-mixed texts, we introduce CodeMixBench, a benchmark comprising eight tasks across 18 languages.… See the full description on the dataset page: https://huggingface.co/datasets/CodeMixBench/CodeMixBench.