datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ccrawl-recrawl-domains
Common Crawl Domain Recrawl
Live fetches of the home page of every ranked domain in Common Crawl's web graph, rendered to Markdown as they are fetched
What is it?
Common Crawl's web graph ranks domains by how central they are, but it does not tell you what those domains actually serve today. This dataset walks that ranking from the top and fetches each domain's home page now, storing the response as one Parquet row with the body, the headers, the timing and the… See the full description on the dataset page: https://huggingface.co/datasets/open-index/ccrawl-recrawl-domains.ReCITE
ReCITE: Real-world CausalIty from Textual Evidence Benchmark
ReCITE (Real-world CausalIty from Textual Evidence) is a benchmark for evaluating LLMs on causal graph extraction from real-world scientific text. It contains 292 annotated causal graphs from open-access MDPI and PLOS articles spanning diverse OpenAlex fields.
Paper: Can Large Language Models Infer Causal Relationships from Real-World Text?
GitHub: ReCITE Repository
Dataset Configurations
This dataset has… See the full description on the dataset page: https://huggingface.co/datasets/RyanSaklad/ReCITE.ccrawl-recrawl-urls
Common Crawl URL Recrawl
Live refetches of pages from Common Crawl's URL index, with the body inline and the text already extracted
What is it?
Common Crawl publishes which URLs it saw and when, but the page bodies live in WARC archives that are awkward to query and are as old as the crawl that made them. This dataset takes the URL index for a single monthly crawl and fetches the pages again now, storing each response as one Parquet row with the body, the… See the full description on the dataset page: https://huggingface.co/datasets/open-index/ccrawl-recrawl-urls.recube-data
Data
This directory contains all benchmark data for the Re2Code repository-level code reconstruction benchmark.
Download
All data files are hosted on Hugging Face and can be downloaded using:
# Install huggingface_hub if not already installed
pip install huggingface_hub
# Download the entire dataset
huggingface-cli download wlqmfl1999/recube-data --repo-type=dataset --local-dir data/
# Or download in Python
from huggingface_hub import snapshot_download… See the full description on the dataset page: https://huggingface.co/datasets/wlqmfl1999/recube-data.recursive-task-synthesis-glm-5.3-rollouts
GLM 5.3 agentic rollouts on Recursive-Task-Synthesis
This dataset catalogs the full collection made from the pinned
Recursive-Task-Synthesis dataset revision
be44f96808d5a9b599d5cb024341ff00091adeb7. The repository includes approximately 260.5 GiB of trajectory payload tar shards.
Contents at a glance
Item
Count
Source tasks considered
37,284
Source candidates inspected
19,368
Converted tasks after source filters
18,600
Tasks passing gold… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/recursive-task-synthesis-glm-5.3-rollouts.harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-30m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-30m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 5.0000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-30m-historical-20t-think.harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-3m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-3m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 1.3000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-3m-historical-20t-think.harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-10m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-10m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 4.0000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-10m-historical-20t-think.recube-dataThis dataset serves as the official data repository for the ReCUBE benchmark; the corresponding evaluation codebase and execution instructions can be found at https://anonymous.4open.science/r/ReCUBE-E0FB/README.md.
Data
This directory contains all benchmark data for ReCUBE.
Download
All data files are hosted on Hugging Face and can be downloaded using:
# Install huggingface_hub if not already installed
pip install huggingface_hub
# Download the entire dataset… See the full description on the dataset page: https://huggingface.co/datasets/recube-anon-2026/recube-data.recon-eval
ReconEval — Financial Reconciliation Benchmark
Reading results from this benchmark. Four properties of ReconEval shape what
a score on it means. Anyone comparing models here should know them.
One class can dominate a margin. PARTIAL_MATCH is the highest-variance class
between models, and its 32 evaluation items are generated from 9 abbreviation
pairs — all of which also appear in the training split, overlap fraction 1.0. On
this set, "learned the concept" and "memorised nine… See the full description on the dataset page: https://huggingface.co/datasets/caiotheodoro/recon-eval.EduBench
EduBench 📚
EduBench é um benchmark em português brasileiro para avaliação de Large Language Models (LLMs) em tarefas educacionais, composto por 3,149 questões discursivas extraídas de vestibulares de alta competitividade.
GitHub
Paper
Dataset Description
Fontes
USP: Universidade de São Paulo
UNICAMP: Universidade Estadual de Campinas
UNESP: Universidade Estadual Paulista
Período
2015-2025 (11 anos de provas)
Áreas do… See the full description on the dataset page: https://huggingface.co/datasets/recogna-nlp/EduBench.DriftSQL-Recovery
DriftSQL-Recovery
DriftSQL-Recovery is an execution-verified dataset for training and
evaluating SQL agents under schema, business-knowledge, and interaction drift.
It contains database-isolated recovery tasks, canonical seven-tool
trajectories, real on-policy failures, Recovery SFT examples, hard replay, and
full-episode GRPO records.
The accompanying implementation, data factory, sandbox, reward, and evaluation
code are available in DriftSQL.
Release scope… See the full description on the dataset page: https://huggingface.co/datasets/lxSYSU/DriftSQL-Recovery.VeriLoop-Governed-Recurrence-Verified
VLR-Recurrence-Verified
VLR-Recurrence-Verified is a synthetic-data construction release for studying
evidence-convergent program repair. It operationalizes a protected partial order:
a candidate is positive only when it preserves every already-satisfied
obligation and strictly improves at least one unresolved obligation.
Scale
Split
Tasks
Families
Transitions
Balanced pairs
Certified finals
Train
3,500
28
12,250
49,000
3,500
Validation
750
10
2,623… See the full description on the dataset page: https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-Governed-Recurrence-Verified.RecurrReason
RecurrReason: Recurrent Reasoning on Symbolic Puzzles
A difficulty-controlled benchmark for evaluating multi-step reasoning in language models
📋 Table of Contents
Overview
Dataset Structure
Puzzles
Quick Start
Citation
License
🎯 Overview
RecurrReason is a benchmark of four recurrent logic puzzles with optimal trajectories and controlled difficulty scaling (N=1 to 10). It tests whether language models can:
Find optimal (minimal-length)… See the full description on the dataset page: https://huggingface.co/datasets/gmannem/RecurrReason.pubmedqa-recursive-llm-degradation-qwen2.5-0.5b
PubMedQA Recursive LLM Degradation — Qwen2.5-3B
This repository contains synthetic biomedical question-answering data
and model predictions generated as part of a study of recursive
fine-tuning and model degradation.
Base Model
Qwen/Qwen2.5-3B
Source Dataset
The experiments use the PubMedQA dataset:
qiaoxin/PubMedQA
This repository contains generated/derived research artifacts and does
not redistribute the original PubMedQA dataset in its entirety.… See the full description on the dataset page: https://huggingface.co/datasets/chrislimbe/pubmedqa-recursive-llm-degradation-qwen2.5-0.5b.gigaverbo-v2-rec-sft
GigaVerbo-v2 REC SFT
A model should not merely know how to reason; it should learn when reasoning is worth the cost.
Dataset repository: OliveiraJLT/gigaverbo-v2-rec-sftBase dataset: Polygl0t/gigaverbo-v2-sftAnswer-generation model: openai/gpt-oss-20bQuality classifier: Polygl0t/portuguese-qwen3-4b-instruct-quality-classifierReasoning translation model and token accounting tokenizer: Qwen/Qwen3.5-9B
Dataset Summary
GigaVerbo-v2 REC SFT — short for GigaVerbo-v2… See the full description on the dataset page: https://huggingface.co/datasets/OliveiraJLT/gigaverbo-v2-rec-sft.pubmedqa-recursive-llm-degradation-qwen2.5-3b
PubMedQA Recursive LLM Degradation — Qwen2.5-3B
This repository contains synthetic biomedical question-answering data
and model predictions generated as part of a study of recursive
fine-tuning and model degradation.
Base Model
Qwen/Qwen2.5-3B
Source Dataset
The experiments use the PubMedQA dataset:
qiaoxin/PubMedQA
This repository contains generated/derived research artifacts and does
not redistribute the original PubMedQA dataset in its entirety.… See the full description on the dataset page: https://huggingface.co/datasets/chrislimbe/pubmedqa-recursive-llm-degradation-qwen2.5-3b.tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified
ToolACE - Tool-Use Agent Data Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of the Team-ACE/ToolACE dataset. ToolACE is a high-quality conversational tool-use dataset containing 11,300+ examples of natural language interactions requiring function calling across diverse domains. This version converts the original OpenAI function-call format into a standardized multi-turn tool-use… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified.Turkish-Recipe-Corpus
Turkish Recipe Corpus (TRC-30K)
Türkçe'nin en kapsamlı açık kaynaklı tarif veri seti.74,768 Türkçe tariften oluşan, 3 farklı NLP görevine hazır yapılandırılmış corpus.
Ethosoft Research · huggingface.co/Ethosoft · ethosoft.org
Dataset Özeti
Değer
Toplam tarif
74,768
Dil
Türkçe (tr)
Lisans
CC BY 4.0
Konfigürasyonlar
3 (structured, instruction, ingredient2recipe)
Split
Train / Validation / Test (80 / 10 / 10)
Ortalama adım sayısı
5.23 /… See the full description on the dataset page: https://huggingface.co/datasets/Ethosoft/Turkish-Recipe-Corpus.harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-1m-historical-20t-think
harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-1m-historical-20t-think
1,000 historical evaluation attempts (250 tasks, four samples per task), newly
graded with gpt-5.6-sol using Harvey's original per-criterion rubric prompt
and all-criteria-pass rule. Mean all-pass rate: 2.2000%.
The train split contains evaluation records, not training examples.
Generation and grading protocols
Generation is unchanged: historical 20-turn thinking-enabled
glob/grep/read agent… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-gpt56sol-qwen35-9b-notes70-recall30-1m-historical-20t-think.openclaw-recursive-study-data
OpenClaw Recursive Repository Study Data
Synthetic repository-study data generated against
openclaw/openclaw at commit
da228660306b55a9cce3b973946f3aacfc515848. The source repository is MIT licensed.
This release contains exploration questions, tool-using study trajectories,
recursive notes, full recall-rewritten trajectories, and recall-to-action
training examples. Nested chat/tool objects are stored as JSON strings to keep
the schema stable and can be decoded with json.loads.… See the full description on the dataset page: https://huggingface.co/datasets/aviralku/openclaw-recursive-study-data.indonesian-recipes
Resep Masakan Indonesia 🍛
Kumpulan resep masakan Indonesia autentik — dari rendang sampai es cendol, lengkap dengan bahan, langkah, tingkat kesulitan, waktu, dan daerah asal.
Kenapa dataset ini ada?
Resep adalah salah satu konten paling dicari untuk LLM (assistant masak) — tapi dataset resep Indonesia di HF nyaris kosong (cuma 1 yang 34 likes). Gw isi gap itu dengan resep-resep yang benar-benar asli Indonesia, bukan versi western yang diterjemahkan.… See the full description on the dataset page: https://huggingface.co/datasets/LorthGyu/indonesian-recipes.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p05-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p05-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 100M notes + note-conditioned trajectory mixture, and KL coefficient 0.05.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p05-think.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p01-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p01-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 100M notes + note-conditioned trajectory mixture, and KL coefficient 0.01.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p01-think.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p1-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p1-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 100M notes + note-conditioned trajectory mixture, and KL coefficient 0.1.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-kl-0p1-think.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-10m-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-10m-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 10M notes + note-conditioned trajectory mixture, and no KL regularization.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-10m-think.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-1m-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-1m-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 1M notes + note-conditioned trajectory mixture, and no KL regularization.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-1m-think.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-30m-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-30m-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 30M notes + note-conditioned trajectory mixture, and no KL regularization.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-30m-think.harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-think
harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-think
Complete closed-book recall evaluation: 7,933 probes. One dataset repository for Qwen3.5-9B, the 100M notes + note-conditioned trajectory mixture, and no KL regularization.
The train split contains evaluation records. Each row is one scored probe; this split name follows the existing evaluation dataset layout.
Model, data, and KL condition
Evaluated model:… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/harvey-eval-recall-qwen35-9b-notes70-notecondtraj30-100m-think.tool-reasoning-sft-RESEARCH-dr-tulu-sft-deep-research-agent-data-cleaned-rectified
Deep Research - Tulu SFT Data Cleaned Rectified
👥 Follow the Author
Supriti Vijay
Overview
This dataset is a cleaned and restructured version of the DR-TULU SFT dataset released by AllenAI's RL Research team. The original DR-TULU dataset represents significant work in creating high-quality training data for reasoning-enhanced language models with tool use capabilities. This version addresses structural issues in the original release while preserving… See the full description on the dataset page: https://huggingface.co/datasets/SupritiVijay/tool-reasoning-sft-RESEARCH-dr-tulu-sft-deep-research-agent-data-cleaned-rectified.