datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column… See the full description on the dataset page: https://huggingface.co/datasets/b-mc2/sql-create-context.long-context-qa-curated-20
Dataset Card / 数据集卡
Dataset Description / 数据集简介
This public release contains 20 curated samples selected from a 10,000-record long-context QA collection. It targets retrieval over long documents, cross-section evidence synthesis, numerical reasoning, timeline reconstruction, and structured answer evaluation. The public subset contains 15 short-answer questions and 5 multiple-choice questions, balanced across Chinese and English.
本公开版本从 10,000 条长上下文问答数据中精选 20… See the full description on the dataset page: https://huggingface.co/datasets/LianeMarilin/long-context-qa-curated-20.Multi-turn_Long-context_Benchmark_for_LLMs
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Arxiv: https://www.arxiv.org/abs/2507.13681
Huggingface: https://huggingface.co/papers/2507.13681
Introduction
LoopServe Multi-Turn Dialogue Benchmark is a comprehensive evaluation dataset comprising multiple diverse datasets designed to assess large language model performance in realistic conversational scenarios.
Unlike traditional benchmarks that place queries only at the end… See the full description on the dataset page: https://huggingface.co/datasets/TreeAILab/Multi-turn_Long-context_Benchmark_for_LLMs.context
On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Charlie Zhang, Graham Neubig,
Xiang Yue
Carnegie Mellon University, Language Technologies Institute
Does Reinforcement Learning Truly Extend Reasoning?
This work explores the discrepancy in views on RL's effectiveness in extending language models' reasoning abilities. Some characterize RL as a capability refiner, while others see it as inducing new compositional skills. This challenge… See the full description on the dataset page: https://huggingface.co/datasets/Interplay-LM-Reasoning/context.ascp-context-attribution
ASCP: Causal Context Attribution and Probe Benchmark
Released artifacts for The Laws of Context Allocation: Causal Measurement and
Closed-Loop Orchestration in Generative Search.
📄 Paper: https://arxiv.org/abs/2608.23252
💻 Code: https://github.com/PeiYangLiu/ascp
Retrieval-augmented generation is usually measured with relevance proxies —
BM25, query–document cosine, output overlap — that score how related a passage
looks, not whether the generator used it. This dataset ships… See the full description on the dataset page: https://huggingface.co/datasets/PeiyangLiu/ascp-context-attribution.sql-create-context-copy
Fork of b-mc2/sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/sql-create-context-copy.context-as-a-service
CaaS Benchmark Corpus v1
A diverse collection of synthetic enterprise documents for benchmarking context extraction and RAG systems.
Dataset Description
This dataset contains 16 representative enterprise documents spanning multiple formats and domains, designed to evaluate:
Structure-aware indexing - Can the system identify high-value vs. low-value content?
Time decay relevance - Does the system properly weight recent vs. old information?
Pragmatic truth detection - Can… See the full description on the dataset page: https://huggingface.co/datasets/imran-siddique/context-as-a-service.sql-create-context-id
Overview
This dataset is a fork from sql-create-context
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/detakarang/sql-create-context-id.pandas-create-context
Overview
This dataset is built from sql-create-context, which in itself builds from WikiSQL and Spider.
I have used GPT4 to translate the SQL schema into pandas DataFrame schem initialization statements and to translate the SQL queries into pandas queries.
There are 862 examples of natural language queries, pandas DataFrame creation statements, and pandas query answering the question using the DataFrame creation statement as context. This dataset was built with text-to-pandas… See the full description on the dataset page: https://huggingface.co/datasets/hiltch/pandas-create-context.German-RAG-ORPO-Long-Context-ShareGPT-HESSIAN-AI
German-RAG-ORPO (Odds Ratio Preference Optimization) Long Context ShareGPT-Format
German-RAG - German Retrieval Augmented Generation
Dataset Summary
The ORPO Long Context Tasks Dataset represents a specialized collection for fine-tuning language models with a focus on RAG-specific capabilities.
The subsets are derived from Synthetic generation inspired by Tencent's (“Scaling Synthetic Data Creation with 1,000,000,000 Personas”).
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/avemio/German-RAG-ORPO-Long-Context-ShareGPT-HESSIAN-AI.German-RAG-ORPO-Long-Context-Alpaca-HESSIAN-AI
German-RAG-ORPO (Odds Ratio Preference Optimization) Long-Context Alpaca-Format
German-RAG - German Retrieval Augmented Generation
Dataset Summary
The ORPO Long Context Tasks Dataset represents a specialized collection for fine-tuning language models with a focus on RAG-specific capabilities.
The subsets are derived from Synthetic generation inspired by Tencent's (“Scaling Synthetic Data Creation with 1,000,000,000 Personas”).
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/avemio/German-RAG-ORPO-Long-Context-Alpaca-HESSIAN-AI.sql-create-context-thai
Overview
This dataset builds from sql-create-context.
@misc{b-mc2_2023_sql-create-context,
title = {sql-create-context Dataset},
author = {b-mc2},
year = {2023},
url = {https://huggingface.co/datasets/b-mc2/sql-create-context},
note = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.},
}
Contextualized_Privacy_Defense_Trajectory
Contextualized Privacy Defense
Paper: Contextualized Privacy Defense for LLM Agents
Code: https://github.com/SALT-NLP/contextual_privacy_defense
Abstract:
Abstract LLM agents increasingly act on users’ personal information, yet existing privacy defenses remain limited in both design and adaptability. Most prior approaches rely on static or passive defenses, such as prompting and guarding. These paradigms are insufficient for supporting contextual, proactive privacy… See the full description on the dataset page: https://huggingface.co/datasets/SALT-NLP/Contextualized_Privacy_Defense_Trajectory.in-car-context-benchmark
Benchmarking contextual understanding for in-car conversational systems
This dataset contains the complete evaluation benchmarks, user utterances, venue recommendations, and failure-annotated responses for evaluating in-car Conversational Question Answering (ConvQA) systems.
Official Code & Implementation: github.com/saydemr/judgebench
Paper (Journal of Systems and Software, 2026): doi.org/10.1016/j.jss.2026.112915 or arxiv.org/abs/2512.12042
📌 Quickstart
from… See the full description on the dataset page: https://huggingface.co/datasets/saydemr/in-car-context-benchmark.sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column… See the full description on the dataset page: https://huggingface.co/datasets/dipanjanS/sql-create-context.KGQA_prompt_contextGarud_puran_FlanT5_with_context
Garuda Purana Q&A for FLAN-T5
This dataset contains question-answer pairs from the Garuda Purana, with a summarization context for each pair generated by FLAN-T5.
Fields:
question: The input question in natural language.
answer: The answer to the question.
context: A short summary (generated by FLAN-T5) of the Q&A pair, usable as context or for semantic retrieval.
Intended Use:
Supervised fine-tuning for Question Answering, Retrieval, and Instruction-based LLMs.
The question… See the full description on the dataset page: https://huggingface.co/datasets/Binoddai/Garud_puran_FlanT5_with_context.contextual_refusal_dataset
Usage in Python
from datasets import load_dataset
# Load data
train_data = load_dataset("yaopaul/contextual_refusal_dataset",split="train")
# Filter entity
entity = "ENTITY_NAME"
entity_train = train_data.filter(
lambda x: x["target_entity"] == entity
)
sah-context-qaДатасет представляет собой набор данных из контекста (paragraph), вопроса (question) и ответа (answer) на якутском языке.