datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
imagescertificatessql-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.barbet-long-context-sft
Barbet long-context SFT
Release b005b2be0e02812d657b2c61a3cb9b19fb7adbe3f729448cfa442780f26d01e6 preserves 4623 active records. This is one joint
assistant-only SFT dataset; no Barbet model training has been run.
The skill-prefill migration has revised 1245
of 1254 records from its fixed base snapshot.
Revisions replace their original records in the explicit shard lists above. Old
bundles and releases remain available at their pinned commits. Additional records
from other… See the full description on the dataset page: https://huggingface.co/datasets/OpenFormosa/barbet-long-context-sft.context_qa_sum_qwen3_synthetic
Context-based QA and Summarization Synthetic Dataset
Overview
This dataset contains synthetic context-based question-answering (QA) and summarization data. The data was synthesized using:
Source context: openbmb/Ultra-FineWeb
Synthesis model: Qwen3-30B-A3B-Instruct-2507
Each context is obtained by taking the initial segment of raw pretraining text from Ultra-FineWeb, truncated to at most the corresponding number of tokens, while ensuring the truncation does not occur in… See the full description on the dataset page: https://huggingface.co/datasets/yuyijiong/context_qa_sum_qwen3_synthetic.paracrawl_context
Dataset Card for ParaCrawl_Context
This is a dataset for document-level machine translation introduced in the ACL 2024 paper Document-Level Machine Translation with Large-Scale Public Parallel Data. It is a dataset consisting of parallel sentence pairs from the ParaCrawl dataset along with corresponding preceding context extracted from the webpages the sentences were crawled from.
Dataset Details
Dataset Description
This dataset adds document-level… See the full description on the dataset page: https://huggingface.co/datasets/Proyag/paracrawl_context.activating_contexts_16kledger-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.ledger-long-context-multi-kpi
the LEDGER Long-Context Multi-KPI extraction datasets and benchmarks.
OCR'd annual reports with ground-truth KPI values for financial information extraction benchmarking.
Dataset Description
This dataset pairs OCR-extracted annual report text (from DeepSeek OCR) with structured KPI ground-truth values. It is designed for evaluating LLM-based financial information extraction, retrieval, and needle-in-a-haystack tasks.
Configs
Config
Reports… See the full description on the dataset page: https://huggingface.co/datasets/artefactory/ledger-long-context-multi-kpi.ContextASR-Bench
ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark
Automatic Speech Recognition (ASR) has been extensively investigated, yet prior benchmarks have largely focused on assessing the acoustic robustness of ASR models, leaving evaluations of their linguistic capabilities relatively underexplored. This largely stems from the limited parameter sizes and training corpora of conventional ASR models, leaving them with insufficient world knowledge, which is crucial for… See the full description on the dataset page: https://huggingface.co/datasets/MrSupW/ContextASR-Bench.hle-context-baseline-deep2026-10-01-mask-qwen36-0-da-otherai-context-15
mask eval of dougalldeepmind/2026-10-01-qwen36-0-da-otherai-context-15 (mode=think)
field
value
experiment
mask eval of dougalldeepmind/2026-10-01-qwen36-0-da-otherai-context-15 (mode=think)
date_generated
2026-10-01
constitution
none
source_repo
teaching_claude_why_replication @ 9792e4119fa654160ec269aa384a945fd670ec0b
models
{"target": "dougalldeepmind/2026-10-01-qwen36-0-da-otherai-context-15", "target_revision": "865c81bca10dfba3d0122d062a09fb7a86e706bd"… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-10-01-mask-qwen36-0-da-otherai-context-15.pretrain-academic-mix-long-contextNormalized documents plus aligned Dolma-2 tokens and target masks.
Size
Tokens
43,694,042,993 (43.7B)
Trainable tokens
43,694,042,993 (43.7B)
Documents
1,001,557
Shards
373
UTF-8 bytes
183,279,720,921
Tokenizer
allenai/dolma2-tokenizer@5292e5d6c0f4
documents.parquet - document_id, text, part_ends, part_trainable,
must_not_split. The readable payload and the mask intent.
metadata.parquet - one text-free row per document: token span, source,
stratum… See the full description on the dataset page: https://huggingface.co/datasets/placeholderlabs/pretrain-academic-mix-long-context.2026-10-01-mask-qwen36-1-da-otherai-context-15
mask eval of dougalldeepmind/2026-10-01-qwen36-1-da-otherai-context-15 (mode=think)
field
value
experiment
mask eval of dougalldeepmind/2026-10-01-qwen36-1-da-otherai-context-15 (mode=think)
date_generated
2026-10-01
constitution
none
source_repo
teaching_claude_why_replication @ 509be22a619f0976e5513286de067719bc2dac35
models
{"target": "dougalldeepmind/2026-10-01-qwen36-1-da-otherai-context-15", "target_revision": "24559cf0882018da027110893687a98441e2ae5f"… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-10-01-mask-qwen36-1-da-otherai-context-15.Tracebench
Tracebench
This dataset contains agent trajectories (TerminalBench + SWE-bench) with two splits:
full: 3316 trajectories (2670 terminal + 646 SWE-bench)
verified: 1000 trajectories (489 SWE-bench + 511 terminal; terminal selected by step_count>=20, has incorrect steps, error-stage ratio threshold)
Agents: mini-SWE-agent (1024), OpenHands (1242), Terminus2 (923), SWE-agent (127).
Models: Anthropic/Claude-Sonnet-4, DeepSeek/DeepSeek-V3.2, Moonshot/Kimi-K2, OpenAI/GPT-5… See the full description on the dataset page: https://huggingface.co/datasets/Contextbench/Tracebench.persona-drift-contextecho
ContextEcho — Released Dataset
Per-cell evaluation corpus and donated session prefixes for the ContextEcho
benchmark. This Hugging Face repository hosts the released dataset artifacts.
The canonical project page, latest README, code, reproduction instructions, and
donation workflow are maintained on GitHub:
https://github.com/Accenture/ContextEcho
Donate a coding-agent session: https://accenture.github.io/ContextEcho/donate/
For the formal datasheet, see DATASHEET.md.… See the full description on the dataset page: https://huggingface.co/datasets/contextecho2026/persona-drift-contextecho.contextual_testCheck out the paper.
X2I-in-context-learning
X2I Dataset
Project Page: https://vectorspacelab.github.io/OmniGen/
Github: https://github.com/VectorSpaceLab/OmniGen
Paper: https://arxiv.org/abs/2409.11340
Model: https://huggingface.co/Shitao/OmniGen-v1
To achieve robust multi-task processing capabilities, it is essential to train the OmniGen on large-scale and diverse datasets. However, in the field of unified image generation, a readily available dataset has yet to emerge. For this reason, we have curated a large-scale… See the full description on the dataset page: https://huggingface.co/datasets/yzwang/X2I-in-context-learning.world-bank-indicatorsthe_stack_v2_python_repos_pretraining_dataset_imported_context-datasetfineweb-filter-malaysian-context
HuggingFaceFW/fineweb filter Malaysian context
What is it?
We filter the original 🍷 FineWeb dataset that consists more than 15T tokens on simple Malaysian keywords.
Total tokens for the filtered dataset is 174102784199 tokens, 174B tokens.
How we do it?
We filter rows using {'malay', 'malaysia', 'melayu', 'bursa', 'ringgit'} keywords on r5.16xlarge EC2 instance for 7 days.
We calculate total tokens using tiktoken.encoding_for_model("gpt2") on c7a.24xlarge EC2… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/fineweb-filter-malaysian-context.the_stack_v2_2M_repos_pretraining_dataset_imported_context-datasetactivating_contexts_131k_layers_21_42ContextBench
ContextBench
This repository provides:
default: the full ContextBench table (single train split).
contextbench_verified: a 500-instance subset (single split).
Columns
The dataset uses a unified schema across sources:
instance_id: ContextBench instance id (e.g., SWE-Bench-Verified__python__...).
original_inst_id: Original benchmark instance id (e.g., astropy__astropy-14539).
source: One of Verified, Pro, Poly, Multi.
language: Programming language.
repo_url: Repository… See the full description on the dataset page: https://huggingface.co/datasets/Contextbench/ContextBench.ambient-acoustic-context
Dataset Card for Ambient Acoustic Context
The Ambient Acoustic Context dataset contains 1-second segments for activities that occur in a workplace setting. Each segment is associated with speaker_id.
Dataset Details
Using Amazin Mechanical Turk, crowd workers were asked to listen to 1-second segments and choose the right label. To ensure the quality of the annotations, audio segments that did not reach majority agreement among the turkers were excluded.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/ambient-acoustic-context.mix-context-post-training-128k
Mix-Context Post-Training Dataset for 128K Context Extension
Overview
Mix-Context Post-Training 128K is a dataset designed specifically for post-training context window extension of pretrained LLMs.
It targets the stage after base pretraining, where a model is adapted to operate over much longer contexts (up to 128K tokens) while preserving short-context behavior. The dataset mixes short- and long-context packed sequences with a controlled length distribution to support:… See the full description on the dataset page: https://huggingface.co/datasets/ghostcc3/mix-context-post-training-128k.scientific-figures-captions-context
Dataset Card for Scientific Figures, Captions, and Context
A novel vision-language dataset of scientific figures taken directly from research papers.
We scraped approximately ~150k papers, with about ~690k figures total. We extracted each figure's caption and label from the paper. In addition, we searched through each paper to find references of each figure and included the surrounding text as 'context' for this figure.
All figures were taken from arXiv research papers.… See the full description on the dataset page: https://huggingface.co/datasets/mawadalla/scientific-figures-captions-context.Agentic-Long-Context-Understanding-QA 📖 Agentic Long Context Understanding 📖
Self-Taught Agentic Long Context Understanding (Arxiv).
AgenticLU refines complex, long-context queries through self-clarifications and contextual grounding, enabling robust long-document understanding in a single pass.
Installation Requirements
This codebase is largely based on OpenRLHF and Helmet, kudos to them.
The requirements are the same
pip install openrlhf
pip install -r ./HELMET/requirements.txt… See the full description on the dataset page: https://huggingface.co/datasets/yzhuang/Agentic-Long-Context-Understanding-QA.ContextAwarehle-context-baseline-gpt55