datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
t2-ragbench
Dataset Card for T2-RAGBench
Project Page | Paper | Code
IMPORTANT NOTICE:
We deleted VQAonBD from the dataset due to low quality of the question reformulations. If you still want to use it you will find the data in the previous commit history.
Dataset Description
Dataset Summary
T2-RAGBench is a benchmark dataset designed to evaluate Retrieval-Augmented Generation (RAG) on financial documents containing both text and tables. It consists of 23,088… See the full description on the dataset page: https://huggingface.co/datasets/G4KMU/t2-ragbench.RAGDOLL
The RAGDOLL E-Commerce Webpage Dataset
This repository contains the RAGDOLL (Retrieval-Augmented Generation Deceived Ordering via AdversariaL materiaLs) dataset as well as its LLM-automated collection pipeline.
The RAGDOLL dataset is from the paper Ranking Manipulation for Conversational Search Engines from Samuel Pfrommer, Yatong Bai, Tanmay Gautam, and Somayeh Sojoudi. For experiment code associated with this paper, please refer to this repository.
The dataset consists of 10… See the full description on the dataset page: https://huggingface.co/datasets/Bai-YT/RAGDOLL.rag-mini-wikipediaIn this huggingface discussion you can share what you used the dataset for.
Derives from https://www.kaggle.com/datasets/rtatman/questionanswer-dataset?resource=download we generated our own subset using generate.py.
jeb-rag
JEB-Bench
Charging the Gate Rent: Measured-Energy Accounting for Adaptive Retrieval-Augmented Generation
⚠️ Status: under construction. Phase 0 (measurement validation) and Phase 1
(index construction) are landing now. The oracle matrix (bench/oracle/) is
populated in Phase 2 and this card will be revised when it is complete. Do not
cite numbers from this repository until the status line says complete.
What this is
The first public per-query × per-configuration… See the full description on the dataset page: https://huggingface.co/datasets/Shanmuk4622/jeb-rag.rag-dataset-12000
Retrieval-Augmented Generation (RAG) Dataset 12000
Retrieval-Augmented Generation (RAG) Dataset 12000 is an English dataset designed for RAG-optimized models, built by Neural Bridge AI, and released under Apache license 2.0.
Dataset Description
Dataset Summary
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by allowing them to consult an external authoritative knowledge base before generating responses. This approach significantly… See the full description on the dataset page: https://huggingface.co/datasets/neural-bridge/rag-dataset-12000.rag-mini-bioasqSee here for an updated version without nans in text-corpus.
In this huggingface discussion you can share what you used the dataset for.
Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using generate.py.
RAGognize
RAGognize Dataset Card
Resource
Link
Code
Paper
Demo
In Retrieval-Augmented Generation (RAG), ensuring accuracy and reliability is essential. RAGognize is a dataset created to help researchers and developers study and improve how AI systems use retrieved information. By offering structured examples and natural token-level closed-domain hallucination annotations, it provides a resource for analyzing model behavior and developing methods that might help make RAG… See the full description on the dataset page: https://huggingface.co/datasets/F4biian/RAGognize.legal-rag-bench
Legal RAG Bench ⚖️
Legal RAG Bench by Isaacus is a reasoning-intensive benchmark for assessing the end-to-end, real-world performance of production-grade legal RAG systems.
Legal RAG Bench is composed of 4,876 passages sampled from the Judicial College of Victoria’s Criminal Charge Book alongside 100 complex, handwritten questions demanding expert-level knowledge of Victorian criminal law and procedure to be answered correctly.
Legal RAG Bench is the first open dataset for the… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/legal-rag-bench.expert-rag-benchmarks
Expert RAG Benchmarks
A unified collection of four expert-level legal RAG benchmarks, exposed as six
named splits and three relational configurations: questions, documents, and
qrels.
The KCL split is named kcl_essay because Hugging Face split identifiers do not
permit hyphens; its source name remains kcl-essay.
Loading
from datasets import load_dataset
repo_id = "jinulee-v/expert-rag-benchmarks"
questions = load_dataset(repo_id, "questions", split="housing")… See the full description on the dataset page: https://huggingface.co/datasets/jinulee-v/expert-rag-benchmarks.t2-ragbench
Dataset Card for T2-RAGBench
Project Page | Paper | Code
IMPORTANT NOTICE:
We deleted VQAonBD from the dataset due to low quality of the question reformulations. If you still want to use it you will find the data in the previous commit history.
Dataset Description
Dataset Summary
T2-RAGBench is a benchmark dataset designed to evaluate Retrieval-Augmented Generation (RAG) on financial documents containing both text and tables. It consists of 23,088… See the full description on the dataset page: https://huggingface.co/datasets/botay/t2-ragbench.rag-mini-bioasqThis dataset is a subset of a training dataset by the BioASQ Challenge, which is available here.
It is derived from rag-datasets/rag-mini-bioasq.
Modifications include:
filling in missing passages (some of them contained "nan" instead of actual text),
changing relevant_passage_ids' type from string to sequence of ints,
deduplicating the passages (removed 40 duplicates) and fixing the relevant_passage_ids in QAP triplets to point to the corrected, deduplicated passages' ids,
splitting QAP… See the full description on the dataset page: https://huggingface.co/datasets/enelpol/rag-mini-bioasq.vietmed-rag-dataset
ViMedGraph
ViMedGraph is a Vietnamese medical GraphRAG resource released together with the paper:
Adaptive Directed Graph Retrieval for Graph-Based Retrieval-Augmented Generation
The dataset is designed to support research on:
Graph-based Retrieval-Augmented Generation (GraphRAG)
Medical Question Answering
Information Retrieval
Knowledge Graph Construction
Retrieval Evaluation
Low-Resource Language NLP
ViMedGraph provides a structured Vietnamese medical knowledge resource… See the full description on the dataset page: https://huggingface.co/datasets/vuduylinh150804/vietmed-rag-dataset.rag_hallucinationsProvides examples of hallucinated responses for RAG applications.
RAG-QA-Leaderboard
Dataset Description
This collection includes 6 widely-used datasets for open-domain question answering and retrieval evaluation:
2WikiMultihopQA, HotpotQA,Musique,PopQA,TrivialQA,PubMedQA
Our evaluation code is at https://github.com/AQ-MedAI/RagQALeaderboard.
Leaderboard
Overall Performance of Different Models on Various Tasks:
Model
AVG
Multi-hop
Single-hop
Medical Domain
DeepSeekR1-0528
79.5
80
92.4
66
GPT-4.1-2025-04-14
78.8
81.6
92.8
62… See the full description on the dataset page: https://huggingface.co/datasets/AQ-MedAI/RAG-QA-Leaderboard.spoken-multihop-rag
Spoken Multi-hop QA: ASR Transcripts Across Four English Accents
ASR transcriptions of 3,000 multi-hop QA questions, each spoken in four
English accents and transcribed with Whisper-large-v3. Released as the
data companion to Better Retrieval, Worse Robustness: How Multi-hop RAG
Amplifies Upstream ASR Errors
(EMNLP 2026, Main Conference).
The dataset exists to make one thing cheap to study: what happens to a
retrieval pipeline when its query arrives through ASR rather than as… See the full description on the dataset page: https://huggingface.co/datasets/orcarouter/spoken-multihop-rag.Indic-Rag-Suite
🌏 Multilingual Indic RAG Suite
A comprehensive multilingual question-answering dataset covering 18 Indian languages with 21,439,886 total samples, designed for RAG (Retrieval-Augmented Generation) applications and multilingual NLP research.
🚀 Quick Start
from datasets import load_dataset
# Load specific language (recommended)
dataset = load_dataset("ai4bharat/Indic-Rag-Suite", "as")
train_data = dataset['train']
print(f"Loaded {len(train_data)} samples")
# Access… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/Indic-Rag-Suite.medicalpark-rag
Medical Park Türkçe Sağlık Makaleleri — RAG Sistemi
Türkçe tıbbi makaleler üzerine kurulmuş, eşik (threshold) tabanlı bir Retrieval-Augmented Generation (RAG) altyapısı.
1. Veri Seti
Kaynak: umutertugrul/turkish-hospital-medical-articles (CC BY 4.0)
Veri seti içeriği: 14 farklı Türk hastane/sağlık kuruluşunun web sitesinden çekilmiş Türkçe tıbbi makaleler, her kuruluş ayrı bir .parquet dosyası olarak sunuluyor (toplam ~25.000 makale, 14 kaynak: Acıbadem… See the full description on the dataset page: https://huggingface.co/datasets/Toivo0/medicalpark-rag.Multi-doc-2025
Dataset Card for Multi-Doc-2025
Dataset Summary
Multi-Doc-2025 is a financial question-answering benchmark built from SEC Form 10-K annual reports of S&P 500 companies. It is designed to evaluate retrieval-augmented generation (RAG) and financial QA systems under three reasoning settings that are not jointly covered by existing financial QA benchmarks: cross-company reasoning, cross-year reasoning, and hybrid-modal reasoning over both text and tables.
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/Anonymous-Team-HC-RAG/Multi-doc-2025.German-RAG-SFT-ShareGPT-HESSIAN-AI
German-RAG-SFT (Supervised Fine-Tuning) Share-GPT Format
German-RAG - German Retrieval Augmented Generation
Dataset Summary
The SFT Tasks Dataset represents a specialized collection for fine-tuning language models with a focus on RAG-specific capabilities. Most tasks were developed using synthetically enhanced data derived from the German Wikipedia, accessed through Cohere's dataset (wikipedia-22-12-de-embeddings). The data is structured in a training knowledge… See the full description on the dataset page: https://huggingface.co/datasets/avemio/German-RAG-SFT-ShareGPT-HESSIAN-AI.narrativeqa-rag
NarrativeQA RAG
Dataset for Retrieval-Augmented Generation (RAG) based on NarrativeQA.
Structure
Subset
Splits
Description
corpus
train (default)
Wikipedia plot summaries shared across all query splits
queries
train, dev, test
Reading comprehension questions
qrels
train, dev, test
Relevance judgments (query ↔ document)
answers
train, dev, test
Reference answers (longest annotated answer)
Dataset statistics
Split
Queries… See the full description on the dataset page: https://huggingface.co/datasets/DinoStackAI/narrativeqa-rag.t2-ragbench
Dataset Card for T2-RAGBench
Project Page | Paper | Code
IMPORTANT NOTICE:
We deleted VQAonBD from the dataset due to low quality of the question reformulations. If you still want to use it you will find the data in the previous commit history.
Dataset Description
Dataset Summary
T2-RAGBench is a benchmark dataset designed to evaluate Retrieval-Augmented Generation (RAG) on financial documents containing both text and tables. It consists of 23,088… See the full description on the dataset page: https://huggingface.co/datasets/grasson/t2-ragbench.simutrade-rag-sft-28k
📢 Domain & Email Migration Notice
From May 6th, 2026, Simutrade will transition to new domains as simutrade.app will not be renewed:
🌐 Website: simutrade.faizath.com (formerly simutrade.app)
⚙️ API: simutrade-api.faizath.com (formerly api.simutrade.app)
📧 Email: contact@simutrade.faizath.com (formerly contact@simutrade.app)
🛰️ CDN: simutrade-cdn.faizath.com (formerly cdn.simutrade.app)
📈 Status Pages:… See the full description on the dataset page: https://huggingface.co/datasets/simutrade/simutrade-rag-sft-28k.RAG-Instruct
Introduction
RAG-Instruct is a RAG dataset designed to comprehensively enhance LLM RAG capabilities, synthesized using GPT-4o. This dataset is based on the Wikipedia corpus and This dataset is based on the Wikipedia corpus and offers the advantages of query-document scenario diversity and task diversity.
The RAG-Instruct dataset can significantly enhance the RAG ability of LLMs and make remarkable improvements in RAG performance across various tasks.
Model
WQA (acc)
PQA (acc)… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/RAG-Instruct.Japanese-RAG-Generator-Benchmark
Japanese RAG Generator Benchmark: 日本語 RAG における Generator 評価ベンチマーク
Japanese RAG Generator Benchmark (J-RAGBench) は日本語RAGにおけるGeneratorに用いるLLMの評価データセットを提供する。
実運用時のRAGに求められる多様な評価カテゴリを同一条件下で評価可能であり、複数の評価カテゴリが同時に出現する問題が含まれるQAデータセットを人手および、補助的にOpenAI API(gpt-4.1-2025-04-14)を用いて構築した。
J-RAGBenchの評価カテゴリ
Integration: 2~3文書程度の複数の情報源から適切な根拠を抽出・統合して回答を導く
Reasoning: 抽出された情報を踏まえて多段階の推論や数値計算などを実行する
Logical: 質問・関連文書間での語彙や表現の差異を解釈し、適切な回答を導く
Table:… See the full description on the dataset page: https://huggingface.co/datasets/neoai-inc/Japanese-RAG-Generator-Benchmark.RAG_Multilingual
Dataset Card for RAG_Multilingual
Dataset Summary
RAG_Multilingual is an instruction-following synthetic QA dataset created from extractive QA datasets from Catalan, English and Spanish reference sets.
The reference datasets were: SQAD (https://huggingface.co/datasets/rajpurkar/squad), Catalanqa (https://huggingface.co/datasets/projecte-aina/catalanqa) and SQAC (https://huggingface.co/datasets/PlanTL-GOB-ES/SQAC).
This dataset, of 56.406 instances, was created by… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/RAG_Multilingual.rag-dx
RAG-Dx: a diagnostic benchmark for retrieval
This dataset is for evaluation. It is not training data and should not be used to train
or fine-tune models.
Most retrieval benchmarks give you a number. A number tells you that something is wrong,
not what. RAG-Dx reports how much a retrieval stack degrades on each of eight specific
failure modes, so the output points at a fix.
Code, harness and reproduction scripts: https://github.com/chakshu-dhannawat/rag-dx
What is in… See the full description on the dataset page: https://huggingface.co/datasets/Chakshu123/rag-dx.bioasq-12b-rag
BioASQ 12B RAG Dataset
A processed version of the BioASQ 12B dataset optimized for Retrieval-Augmented Generation (RAG) applications in biomedical question answering.
This dataset contains two distinct subsets specifically designed for RAG applications:
A text corpus of PubMed abstracts ready for indexing and retrieval, containing detailed metadata and full abstract text.
An evaluation dataset consisting of biomedical questions, each paired with an ideal answer and a list of… See the full description on the dataset page: https://huggingface.co/datasets/mattmorgis/bioasq-12b-rag.RAG-Grounded-QA-188k
🎯 RAG Grounded QA 186K
The Anti-Hallucination Dataset
Teach language models to answer from context — or shut up trying.
Built by NovachronoAI — Precision AI for the real world.
Full Dataset (186K) · 20K Subset · Schema · Sources · Usage Guide
🧠 Why This Dataset Exists
Most QA datasets teach models what to say. This one also teaches them when to stay silent.
RAG (Retrieval-Augmented Generation) systems have a fatal flaw: the model hallucinates when… See the full description on the dataset page: https://huggingface.co/datasets/NovachronoAI/RAG-Grounded-QA-188k.turkish-medical-rag
🩺 Turkish Medical RAG
Hierarchical Parent–Child Retrieval-Augmented Generation for Turkish Medical Documents
📌 Proje Hakkında
Bu proje, Türkçe tıbbi dokümanlar üzerinde çalışan uçtan uca bir
Retrieval-Augmented Generation (RAG) sistemi geliştirmek amacıyla hazırlanmıştır.
Sistem bir kullanıcı sorusu aldığında önce doküman koleksiyonundaki küçük ve
anlamsal olarak odaklı parçalar (child chunks)… See the full description on the dataset page: https://huggingface.co/datasets/sedayzc/turkish-medical-rag.German-RAG-SFT-Alpaca-HESSIAN-AI
German-RAG-SFT (Supervised Fine-Tuning) Alpaca-Format
German-RAG - German Retrieval Augmented Generation
Dataset Summary
The SFT Tasks Dataset represents a specialized collection for fine-tuning language models with a focus on RAG-specific capabilities. Most tasks were developed using synthetically enhanced data derived from the German Wikipedia, accessed through Cohere's dataset (wikipedia-22-12-de-embeddings). The data is structured in a training knowledge… See the full description on the dataset page: https://huggingface.co/datasets/avemio/German-RAG-SFT-Alpaca-HESSIAN-AI.