datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
codah
Dataset Card for COmmonsense Dataset Adversarially-authored by Humans
Dataset Summary
The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense
question-answering in the sentence completion style of SWAG. As opposed to other automatically generated
NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model
and use this information to design challenging commonsense questions.… See the full description on the dataset page: https://huggingface.co/datasets/jaredfern/codah.distilabel-capybara-dpo-7k-binarized
Capybara-DPO 7K binarized
A DPO dataset built with distilabel atop the awesome LDJnr/Capybara
This is a preview version to collect feedback from the community. v2 will include the full base dataset and responses from more powerful models.
Why?
Multi-turn dialogue data is key to fine-tune capable chat models. Multi-turn preference data has been used by the most relevant RLHF works (Anthropic, Meta Llama2, etc.). Unfortunately, there are very few… See the full description on the dataset page: https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized.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]
xcopa
Dataset Card for "xcopa"
Dataset Summary
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around
the globe. The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/cambridgeltl/xcopa.Legal_Corpus_QA_SynDeepThink
🧠 Legal Corpus QA SynDeepThink Dataset
This repository contains a high-intelligence Legal Question-and-Answer dataset, generated through an advanced Iterative and Recursive Thinking process. It bridges the gap between static legal corpora and the dynamic "check-and-recheck" nature of human legal expertise. 🏛️
💡 The Concept: Iterative & Recursive Legal Logic
While standard synthetic datasets are often generated in a single pass, Legal_Corpus_QA_SynDeepThink mimics the… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/Legal_Corpus_QA_SynDeepThink.Gaokao-Compass-11M
English
GaokaoCompass — China College Admission Dataset
GaokaoCompass is a structured dataset of China's national college entrance examination (Gaokao) admission records, covering all 31 provinces from 2017 to 2025. It includes enrollment plans, university admission cutoff scores, major-level admission scores, and score-ranking tables. The dataset is designed to help students, parents, and researchers make informed decisions with… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/Gaokao-Compass-11M.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.car-bench-dataset
CAR-Bench Dataset
CAR-Bench is a benchmark for evaluating AI voice assistants in a realistic automotive (car) environment.
It tests an agent's ability to correctly use vehicle control tools, handle disambiguation, and avoid hallucinations.
Dataset Structure
The dataset is organized into task configs and mock data configs:
Tasks
Each task defines a user persona, an instruction, the initial vehicle/environment context, and the ground-truth sequence of tool-call… See the full description on the dataset page: https://huggingface.co/datasets/johanneskirmayr/car-bench-dataset.PromptEval_MMLU_correctness
MMLU Multi-Prompt Evaluation Data (correctness scores)
Overview
This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multitask Language Understanding (MMLU) benchmark. The data is introduced in
Maia Polo, Felipe, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, and Mikhail Yurochkin. "Efficient multi-prompt… See the full description on the dataset page: https://huggingface.co/datasets/PromptEval/PromptEval_MMLU_correctness.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/SHSLab/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.CXM_Arena
Dataset Card for CXM Arena Benchmark Suite
Dataset Description
This dataset, "CXM Arena Benchmark Suite," is a comprehensive collection designed to evaluate various AI capabilities within the Customer Experience Management (CXM) domain. It consolidates five distinct tasks into a unified benchmark, enabling robust testing of models and pipelines in business contexts. The entire suite was synthetically generated using advanced large language models, primarily… See the full description on the dataset page: https://huggingface.co/datasets/sprinklr-huggingface/CXM_Arena.CareQA
CareQA
Dataset Summary
CareQA is a healthcare QA dataset with two versions:
Closed-Ended Version: A multichoice question answering (MCQA) dataset containing 5,621 QA pairs across six categories. Available in English and Spanish.
Open-Ended Version: A free-response dataset derived from the closed version, containing 2,769 QA pairs (English only).
The dataset originates from… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/CareQA.code-alchemy
CodeAlchemy
CodeAlchemy is a synthetic code dataset (~976.6B tokens, ~162M rows) designed for training and evaluating code language models. It consists of 5 training subsets covering a range of code-related tasks, and 2 evaluation subsets. All files are Parquet with zstd compression with on-disk size ~873 GB. Raw source files are not included due to ownership considerations and must be manually fetched as instructed below.
Dataset Statistics
Config… See the full description on the dataset page: https://huggingface.co/datasets/open-alchemy/code-alchemy.ledger-long-context-KPI-QA
LEDGER — Long-Context KPI Question Answering & Page Retrieval
This dataset is part of the LEDGER (Long-context Evaluation of Documents for
Grounded Extraction and Retrieval) benchmark.
It supports two of the three LEDGER tasks:
Page-level KPI retrieval — given a natural-language question about a financial
KPI and the corresponding annual report, retrieve the relevant page(s). Each row
includes TREC-style graded relevance judgments (qrels) over all candidate pages.… See the full description on the dataset page: https://huggingface.co/datasets/artefactory/ledger-long-context-KPI-QA.JMedBench
Maintainers
Junfeng Jiang@Aizawa Lab: jiangjf (at) is.s.u-tokyo.ac.jp
Jiahao Huang@Aizawa Lab: jiahao-huang (at) g.ecc.u-tokyo.ac.jp
If you find any error in this benchmark or want to contribute to this benchmark, please feel free to contact us.
Introduction
This is a dataset collection of JMedBench, which is a benchmark for evaluating Japanese biomedical large language models (LLMs).
Details can be found in this paper. We also provide an evaluation framework, med-eval… See the full description on the dataset page: https://huggingface.co/datasets/Coldog2333/JMedBench.mimo-claude-code-traces-1k
MIMO Claude Code Traces
MIMO Claude Code Traces is a collection of coding-agent trajectories in a Claude Code-style environment. Each record contains a user coding task, the full multi-turn message trace, available tool schemas, assistant reasoning fields, tool calls, tool outputs, and metadata such as model name, category, duration, cost, token usage, and whether the trace used tools.
The traces were generated with mimo-v2.5-pro, MiMo's most capable model at the time of… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/mimo-claude-code-traces-1k.copycolors_mcqaThis dataset consists of formatted n-way multiple choice questions, where n is in [2,10]. The task itself is simply to copy the prototypical color from the context and produce the corresponding color's answer choice letter.
The "prototypical colors" dataset instances themselves come from Memory Colors (Norland et al. 2021) and corypaik/coda (instances whose object_group is 0, indicating participants agreed on a prototypical color of that object).
casimedicos-exp
Antidote CasiMedicos Dataset - Possible Answers Explanations in Resident Medical Exams
We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments
for the correct answer but also arguments to explain why the remaining possible answers are incorrect.
This dataset can be used for various NLP tasks including: Medical Question Answering, Explanatory Argument Extraction or Explanation Generation.
The… See the full description on the dataset page: https://huggingface.co/datasets/HiTZ/casimedicos-exp.vsr_random
VSR: Visual Spatial Reasoning
This is the random set of VSR: Visual Spatial Reasoning (TACL 2023) [paper].
Usage
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_random", data_files=data_files)
Note that the image files still need to be downloaded separately. See data/ for details.
Go to our github repo for more introductions.
Citation
If you find VSR… See the full description on the dataset page: https://huggingface.co/datasets/cambridgeltl/vsr_random.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.vsr_zeroshot
VSR: Visual Spatial Reasoning
This is the zero-shot set of VSR: Visual Spatial Reasoning (TACL 2023) [paper].
Usage
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_zeroshot", data_files=data_files)
Note that the image files still need to be downloaded separately. See data/ for details.
Go to our github repo for more introductions.
Citation
If you find… See the full description on the dataset page: https://huggingface.co/datasets/cambridgeltl/vsr_zeroshot.SCOPE-OOD-set
SCOPE-60K-OOD: Out-of-Distribution LLM Routing Dataset
Dataset Description
SCOPE-60K-OOD is an out-of-distribution (OOD) evaluation dataset for LLM routing systems. It contains evaluation results from 5 frontier language models that were not seen during training, designed to test the generalization capabilities of routing methods.
Authors
Qi Cao - UC San Diego, PXie Lab
Shuhao Zhang - UC San Diego, PXie Lab
Affiliation
University of California, San… See the full description on the dataset page: https://huggingface.co/datasets/Cooolder/SCOPE-OOD-set.super_tweeteval
SuperTweetEval
Dataset Card for "super_tweeteval"
Dataset Summary
This is the oficial repository for SuperTweetEval, a unified benchmark of 12 heterogeneous NLP tasks.
More details on the task and an evaluation of language models can be found on the reference paper, published in EMNLP 2023 (Findings).
Data Splits
All tasks provide custom training, validation and test splits.
task
dataset
load dataset
description
number of instances
Topic… See the full description on the dataset page: https://huggingface.co/datasets/cardiffnlp/super_tweeteval.Magpie-Qwen2-Pro-200K-Chinese
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese.mfaqWe present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages.gspc-swarm
GSPC — swarm bank (SwarmBench v2b)
Council of AI measurement bank. Measurement, not certification.
Bank. Frozen split. Live n is the matching axis on GET https://councilof.ai/api/gspc, not a Hub score. Not a certificate. Art 50 (EUR-Lex): 2 August 2026 live; marking grace 2 December 2026.
Live measurement. This bank stands behind the swarm row of the live GSPC board: GET https://councilof.ai/api/gspc?axis=swarm (family, kind, status and n are on that row, never typed here; the… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-swarm.turkish-court-decisions
Türk İçtihat Korpusu — 11.045.085 Mahkeme Kararı
Türkiye'nin kamuya açık mahkeme kararlarından derlenmiş, bilinen en büyük Türkçe
hukuk metni veri seti. 11.045.085 karar, 31.5 milyar karakter düz metin (5.50 GB Parquet),
1962'den 2026'ya. Yargıtay, Danıştay, Anayasa Mahkemesi ve UYAP Emsal üzerinden
yerel/istinaf mahkemeleri.
Kapsam
Kaynak
Karar sayısı
Yıl aralığı
Metin
Dosya
Yargıtay (yargitay)
9.820.145
1997–2026
19.5 milyar karakter
17
Danıştay… See the full description on the dataset page: https://huggingface.co/datasets/mrfg/turkish-court-decisions.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M.Dr-CiK
Dr-CiK: A Testbed for Foresight-Driven Agents
Dr-CiK is a benchmark for evaluating whether agents can retrieve
forecasting-relevant context from a noisy document corpus, filter out
distractors, distill the retrieved context into forecast-useful evidence, and
produce forecasts grounded in that evidence.
Real-world time-series forecasting often depends not only on historical
observations but also on external context that must be actively discovered
from heterogeneous, noisy… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/Dr-CiK.chess-evaluations
Chess Evaluations Dataset
This dataset contains chess positions represented in FEN (Forsyth-Edwards Notation) along with their evaluations and next moves for tactical evals. The dataset is divided into three configurations:
tactics: Includes chess positions, their evaluations, and the best move in the position.
randoms: Contains random chess positions and their evaluations.
chess_data: General chess positions with evaluations.
This is an in progress dataset which contains millions… See the full description on the dataset page: https://huggingface.co/datasets/ssingh22/chess-evaluations.