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.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.Synthetic-Dataset-Childrens-Stories**Status: released 13-09-2026, repacked 14-09-2026.** The 14-09-2026 repack replaced 58
items after the acceptance gates were strengthened (prompt-instruction leaks, markdown
bullet lists and blockquotes); the other 29,942 are unchanged. Development stopped, pipeline released 17/09/26.
SAMPLE RELEASE: 30,000 synthetic children's short stories for early-reader language modelling.
Metrics
Value
genres
26
stories per genre
1.153-1.154K stories
total characters
38… See the full description on the dataset page: https://huggingface.co/datasets/ContextReq/Synthetic-Dataset-Childrens-Stories.in-context-grid-reasoning
In-Context Grid Reasoning (ICGR)
A small, fully synthetic benchmark for demonstration-conditioned rule induction:
each task shows 2–4 (input grid → output grid) support pairs that share one
hidden transformation, and the model must apply the same transformation to a
held-out query input.
It targets the same behaviour probed by recent in-context / latent-reasoning work
on ARC-AGI (e.g. BDH-CQ: In-Context Learning with Recurrent Latent Reasoning,
arXiv:2608.09888), but is… See the full description on the dataset page: https://huggingface.co/datasets/WhySoCodius/in-context-grid-reasoning.kana-kanji-context
kana-kanji-context
Japanese kana-to-kanji conversion dataset with context for disambiguation.
Overview
Metric
Value
Total entries
77,277,970
File size
~7.4GB
Format
JSONL
Data Format
{
"input": "神経 [---]かがく",
"output": ["科学"],
"count": 1
}
{
"input": "この [---]さいご",
"output": ["最後", "最期"],
"count": 2
}
Fields
Field
Description
input
Context + [---] + reading (hiragana)
output
Correct kanji candidates (max… See the full description on the dataset page: https://huggingface.co/datasets/katsukiono/kana-kanji-context.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.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.kana-kanji-context
kana-kanji-context
Japanese kana-to-kanji conversion dataset with context for disambiguation.
Overview
Metric
Value
Total entries
77,277,970
File size
~7.4GB
Format
JSONL
Data Format
{
"input": "神経 [---]かがく",
"output": ["科学"],
"count": 1
}
{
"input": "この [---]さいご",
"output": ["最後", "最期"],
"count": 2
}
Fields
Field
Description
input
Context + [---] + reading (hiragana)
output
Correct kanji… See the full description on the dataset page: https://huggingface.co/datasets/0x3/kana-kanji-context.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.long-context-llm-papers
Long-Context LLM Papers — FineSet
A research-paper dataset on Long-Context LLM Papers, assembled, deduplicated, and quality-scored by
FineSet from arXiv and Semantic Scholar.
📸 This is a dated snapshot — generated 2026-06-19.
It is not auto-updated. Research on Long-Context LLM Papers moves fast — new papers land on arXiv every
week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓
Why this dataset
Quality-scored: quality_score… See the full description on the dataset page: https://huggingface.co/datasets/fineset-io/long-context-llm-papers.Wikipedia-Persian-Context-3.3klambada-context
Dataset Summary
This is the LAMBADA test split modified for bidirectional language models (for example BERT). The original is appended by punctuation symbols (for example ."), as predicted by GPT-2 (small). The original is the LAMBADA test split as pre-processed by OpenAI,
LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human… See the full description on the dataset page: https://huggingface.co/datasets/ltg/lambada-context.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}.},
}
gemma3-reasoning-dropin-context
Gemma3 Reasoning Drop-in (Context Preserved)
Drop-in dataset with task/input/expected_output where input includes prior turns so follow-up replies remain coherent.
Use:
from datasets import load_dataset
dataset = load_dataset("Cyleux/gemma3-reasoning-dropin-context", split="train[:10000]")
Stats:
{
"input": "data/functiongemma_upload/train.jsonl",
"output": "data/gemma3_reasoning_dropin_context/train.jsonl",
"rows": 2225,
"samples_total": 1214,
"rows_written": 2225… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3-reasoning-dropin-context.odia_domain_context_train_v1
Dataset Card for odia_domain_context_train_v1
Dataset Summary
This dataset contains 10K instructions that span various facets of Odisha's unique identity.
The instructions cover a wide array of subjects, ranging from the culinary delights in 'RECIPES,' the historical significance of 'HISTORICAL PLACES,' and
'TEMPLES OF ODISHA,' to the intellectual pursuits in 'ARITHMETIC,' 'HEALTH,' and 'GEOGRAPHY.'
It also explores the artistic tapestry of Odisha through 'ART AND… See the full description on the dataset page: https://huggingface.co/datasets/OdiaGenAI/odia_domain_context_train_v1.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.Wikipedia-Persian-Context-50ksql-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.tw-law-context-summary
Dataset Card for tw-law-context-summary
本資料集為中華民國（臺灣）法規條文之 LLM 摘要集，每筆樣本提供「法規名稱」對應的條列式摘要文字，可作為法規 RAG 系統的索引／簡介，或法律 chatbot 的初步說明資料。
Dataset Details
Dataset Description
資料以法規為單位，由 LLM 對每部法規生成「立法依據、規範重點、施行日期、適用範圍、廢止狀態」等結構化摘要。每筆樣本欄位：
text：摘要內容（條列式）。
name：法規名稱。
abandon_note：廢止／修訂註記，若為現行法規則為空字串。
token_count / word_count：保留為字串欄位（部分樣本為空）。
主要使用者為法規檢索、法律入門教育場景。
Curated by: Huang Liang Hsun
Language(s) (NLP): Traditional Chinese
License: cc-by-nc-sa-4.0… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/tw-law-context-summary.tw-law-context-keywords
Dataset Card for tw-law-context-keywords
本資料集為中華民國（臺灣）法規之 LLM 關鍵字抽取結果，每筆樣本對應一部法規的條列式關鍵字清單，可作為法規檢索、Tag 化、向量化前置處理之素材。
Dataset Details
Dataset Description
資料以法規為單位，由 LLM 對每部法規抽取「核心概念、條文要點、重要術語、特殊註記（如『廢止』）」等關鍵字。可用於：
法規檢索系統的 keyword index。
對 RAG 流程中的 chunk 預先附加關鍵字 metadata。
訓練法律術語抽取／NER 模型的 weak supervision 資料。
每筆樣本欄位：
text：條列式關鍵字清單。
name：法規名稱。
abandon_note：廢止／修訂註記（如 廢 表示已廢止）。
token_count / word_count：保留欄位。
Curated by: Huang Liang Hsun
Language(s) (NLP):… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/tw-law-context-keywords.odia_context_qa_98k
Dataset Card for odia-qa-98K
Dataset Summary
Supported Tasks and Leaderboards
Large Language Model (LLM)
Languages
Odia
Dataset Structure
JSON
Data Fields
instruction (string)
english_instruction (string)
input (string)
english_input (string)
output (string)
english_output (string)
Licensing Information
This work is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.… See the full description on the dataset page: https://huggingface.co/datasets/OdiaGenAI/odia_context_qa_98k.radon-test-long_context
radon-test-long_context
Description
Long context test dataset for RADON model evaluation with extended text samples
Usage
Load Dataset
from datasets import load_dataset
dataset = load_dataset("MagistrTheOne/radon-test-long_context")
print(dataset)
Use with RADON Model
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load RADON model
model = AutoModelForCausalLM.from_pretrained("MagistrTheOne/RadonSAI")
tokenizer =… See the full description on the dataset page: https://huggingface.co/datasets/MagistrTheOne/radon-test-long_context.odia_domain_context_train_v1
Dataset Card for odia_domain_context_train_v1
Dataset Summary
This dataset contains 10K instructions that span various facets of Odisha's unique identity.
The instructions cover a wide array of subjects, ranging from the culinary delights in 'RECIPES,' the historical significance of 'HISTORICAL PLACES,' and
'TEMPLES OF ODISHA,' to the intellectual pursuits in 'ARITHMETIC,' 'HEALTH,' and 'GEOGRAPHY.'
It also explores the artistic tapestry of Odisha through 'ART AND… See the full description on the dataset page: https://huggingface.co/datasets/sarthakprassidh/odia_domain_context_train_v1.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
)