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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
699e0810251cac84be7d52ba | peteromallet/dataclaw-peteromallet | peteromallet | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["dataclaw", "claude-code", "codex-cli", "conversations", "coding-assistant", "tool-use", "agentic-coding", "claude-haiku-4-5-20251001", "claude-opus-4-5-20251101", "claude-opus-4-6", "claude-sonnet-4-5-20250929", "claude-sonnet-4-6"... | false | False | 2026-02-25T16:14:13 | 245 | 245 | false | b925056b0539a8bd28a06417dca464aac6ba7bdb |
Coding Agent Conversation Logs
This is a performance art project. Anthropic built their models on the world's freely shared information, then introduced increasingly dystopian data policies to stop anyone else from doing the same โ pulling up the ladder behind them. DataClaw lets you throw the ladder back d... | 4,010 | 4,010 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"dataclaw",
"claude-code",
"codex-cli",
"conversations",
"coding-assistan... | 2026-02-24T20:20:32 | null | null |
698b2c8b4c9e577aa3b1fa16 | nohurry/Opus-4.6-Reasoning-3000x-filtered | nohurry | {"license": "apache-2.0"} | false | False | 2026-02-10T13:06:40 | 208 | 115 | false | 80e9226ea6168634ee2d6c010c3da619af8ad542 | Filtered from: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3000x
The original dataset has 979 refusals, I removed these in this version.
| 1,843 | 1,843 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-10T13:03:07 | null | null |
6997f5d1260ef062721a6a13 | togethercomputer/CoderForge-Preview | togethercomputer | {"dataset_info": [{"config_name": "trajectories", "features": [{"name": "trajectory_id", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "messages", "dtype": "string"}, {"name": "reward", "dtype": "float64"}, {"name": "tools", "dtype": "string"}, {"name":... | false | False | 2026-02-26T18:22:08 | 102 | 102 | false | 060fca96cf723b2ebab3181e9e59fafd273df3cb |
CoderForge-Preview: SOTA Open Dataset for Training Efficient Agents
CoderForge-Preview is the largest open test-verified coding agent dataset.
Fine-tuning Qwen-3 32B on it, we boost SWE-Bench Verified performance 23.0% โ 59.4% pass@1 and rank #1 among open-data and #2 among open-weight models โค32B parameter... | 7,229 | 7,229 | [
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-20T05:49:05 | null | null |
698e4ad0913c4d1f4a64479a | crownelius/Opus-4.6-Reasoning-3300x | crownelius | {"license": "apache-2.0"} | false | False | 2026-03-02T05:37:24 | 65 | 59 | false | 2aaf2ade07cefc9fa733f4ce8d9abdd152e7ec91 |
Opus-4.6-Reasoning-3000x (Cleaned)
This dataset has been automatically cleaned to remove:
Empty or missing responses
Responses shorter than 10 characters
Refusal responses ("problem is incomplete", "cannot solve", etc.)
Responses with no substantive content
Responses that just echo the problem
Clea... | 468 | 468 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-12T21:49:04 | null | null |
6993ef463a18b487423bd218 | ronantakizawa/github-top-code | ronantakizawa | {"license": "mit", "task_categories": ["text-generation"], "language": ["code"], "tags": ["code", "github", "source-code", "trending-developers", "software-engineering"], "size_categories": ["1M<n<10M"]} | false | False | 2026-02-23T01:41:46 | 101 | 55 | false | 7e85cf433fa8aac7ba3d3ff2b24b0cfee91a3985 |
GitHub Top Developer Source Code
A curated dataset of 1.3M+ source code files from GitHub's top ranked developers (2015-2025).
This dataset is based on the top ranked developers from this dataset: https://huggingface.co/datasets/ronantakizawa/github-top-developers
Dataset Summary
1.3M+ source cod... | 1,027 | 1,027 | [
"task_categories:text-generation",
"language:code",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"github",
"source-code",
"trending-developers",
"software-... | 2026-02-17T04:32:06 | null | null |
699eda02bbb65cc3123d4df0 | LocoreMind/qwen3.5-27b-cli-reasoning-3632x | LocoreMind | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "cli", "terminal", "bash", "linux", "distillation", "synthetic", "thinking", "chain-of-thought"], "pretty_name": "Qwen3.5-27B CLI Reasoning 3632x", "dataset_i... | false | False | 2026-02-28T12:33:39 | 49 | 49 | false | a27b86980edee74a72a9abfd8c5097f5f95a8a48 |
Qwen3.5-27B CLI Reasoning 3632x
A synthetic reasoning dataset for CLI/terminal command assistance, distilled from Qwen3.5-27B with thinking mode enabled.
Each sample contains a realistic user scenario describing a terminal task, paired with the model's reasoning chain (<think>) and a structured JSON answer (... | 554 | 554 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
... | 2026-02-25T11:16:18 | null | null |
6996711477c275fd9adb7137 | nvidia/Nemotron-Terminal-Corpus | nvidia | {"license": "cc-by-4.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["code"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "dataset_adapters", "data_files": [{"split": "train", "path": "dataset_adapters/*.parquet"}]}, {"config_name": "skill_based_easy", "data_files": [{"spli... | false | False | 2026-02-27T22:37:57 | 48 | 48 | false | a1667c4ffdadea02a89bffe4f1bb7ca2ff19f8d9 |
Terminal-Corpus: Large-Scale SFT Dataset for Terminal Agents
Terminal-Corpus is a large-scale Supervised Fine-Tuning (SFT) dataset designed to scale the terminal interaction capabilities of Large Language Models (LLMs). Developed by NVIDIA, this dataset was built using the Terminal-Task-Gen pipeline, which c... | 601 | 601 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2602.21193",
"region:us",
"code"
] | 2026-02-19T02:10:28 | null | null |
6993443f3002cd10f9b949e7 | VLR-CVC/DocVQA-2026 | VLR-CVC | {"task_categories": ["visual-question-answering", "document-question-answering", "image-text-to-text", "question-answering"], "language": ["en"], "tags": ["multimodal", "benchmark", "document-understanding"], "configs": [{"config_name": "default", "data_files": [{"split": "val", "path": "val.parquet"}]}]} | false | False | 2026-02-23T08:34:00 | 57 | 42 | false | 3cf7b9ab362ae4daa9d083d400a33e44d5232cb3 |
DocVQA 2026 | ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
Building upon previous DocVQA benchmarks, this evaluation dataset introduces challenging reasoning questions over a diverse collection of documents spanning eight domains, including... | 5,246 | 5,246 | [
"task_categories:visual-question-answering",
"task_categories:document-question-answering",
"task_categories:image-text-to-text",
"task_categories:question-answering",
"language:en",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library... | 2026-02-16T16:22:23 | null | null |
6982a8b7a22838536c4632ea | GD-ML/GenMRP | GD-ML | {"task_categories": ["tabular-classification", "graph-ml"]} | false | False | 2026-02-08T13:39:59 | 37 | 37 | false | 537862bad5afc6d9414afb70a229834aaadde8ca |
GenMRP: A Generative Multi-Route Planning Framework for Efficient and Personalized Real-Time Industrial Navigation
This is the dataset for our paper.
The following table contains the feature dimensions and key features of our dataset.
Feature Type
Interpretation
Shape
Some Key Features
Link Featur... | 511 | 511 | [
"task_categories:tabular-classification",
"task_categories:graph-ml",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-04T02:02:31 | null | null |
65dc13085ca10be41fdd8b27 | bigcode/the-stack-v2 | bigcode | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack-v2", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_ga... | false | auto | 2024-04-23T15:52:32 | 507 | 35 | false | 7408bfbcfd48e5833d62fd3dba48afd20d109473 |
The Stack v2
The dataset consists of 4 versions:
bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here
bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated
bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but... | 8,180 | 253,168 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
... | 2024-02-26T04:26:48 | null | null |
6982a756e346e0f1b5010cc4 | GD-ML/CCN | GD-ML | {"task_categories": ["tabular-classification"]} | false | False | 2026-02-08T13:35:55 | 35 | 35 | false | 25907bb31a4fe43e8a2b51b3b9e8939e3e408dd8 |
Towards Full Candidate Interaction: A Comprehensive Comparison Network for Better Route Recommendation
This is the dataset for our paper.
The following table contains the feature dimensions and key features of our dataset.
Feature Type
Interpretation
Shape
Some Key Features
Route Features
Used to ... | 418 | 418 | [
"task_categories:tabular-classification",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-04T01:56:38 | null | null |
699d3101b96a940ec78fab3c | Video-Reason/VBVR-Dataset | Video-Reason | {"license": "apache-2.0", "task_categories": ["video-classification", "visual-question-answering", "video-text-to-text"], "language": ["en"], "tags": ["video-reasoning", "video-generation", "visual-reasoning", "benchmark", "spatiotemporal", "VBVR"], "size_categories": ["1M<n<10M"], "pretty_name": "VBVR-Dataset: Very Bi... | false | False | 2026-02-27T20:46:01 | 34 | 34 | false | a5085d4cd93d0d1964036d83b86f0092c66214cd |
VBVR-Dataset: Very Big Video Reasoning Training Data
๐ Website โข
๐ VBVR-Bench โข
๐ป GitHub โข
๐ Leaderboard
Overview
VBVR-Dataset is an unprecedentedly large-scale video reasoning training resource, part of the Very Big Video Reasoning (VBVR) Suite. This release contains the training s... | 1,365 | 1,365 | [
"task_categories:video-classification",
"task_categories:visual-question-answering",
"task_categories:video-text-to-text",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroi... | 2026-02-24T05:02:57 | null | null |
698c22cfff4308d71cbe4e5d | GD-ML/IntTravel_dataset | GD-ML | {"task_categories": ["other"], "tags": ["recommendation-system", "poi-recommendation", "mobility", "travel"], "size_categories": ["1B<n<10B"], "configs": [{"config_name": "default", "data_files": [{"split": "interaction", "path": "interaction_1.csv"}, {"split": "user_info", "path": "user_info.csv"}, {"split": "poi_info... | false | False | 2026-02-26T02:35:51 | 90 | 31 | false | 1585298e474bebbcd66c766be1f7e38f06367c1b | All data has been uploaded. Please note that the current POI dataset does not include coordinate information. We will be updating it as soon as possible, so please stay tuned. If you do not need the coordinate information for the POIs, please disregard this message.
IntTravel: A Real-World Dataset and Generativ... | 3,207 | 3,207 | [
"task_categories:other",
"size_categories:1B<n<10B",
"arxiv:2602.11664",
"region:us",
"recommendation-system",
"poi-recommendation",
"mobility",
"travel"
] | 2026-02-11T06:33:51 | null | null |
68873481d4a41fe542ba35b7 | uv-scripts/ocr | uv-scripts | {"viewer": false, "tags": ["uv-script", "ocr", "vision-language-model", "document-processing", "hf-jobs"]} | false | False | 2026-02-28T13:51:55 | 120 | 29 | false | 9c9016c2ef49f3d352b92a1f82427284778a67a7 |
OCR UV Scripts
Part of uv-scripts - ready-to-run ML tools powered by UV and HuggingFace Jobs.
14 OCR models from 0.9B to 8B parameters. Pick a model, point at your dataset, get markdown โ no setup required.
๐ Quick Start
Run OCR on any dataset without needing your own GPU:
# Quick test with 10 ... | 914 | 3,534 | [
"region:us",
"uv-script",
"ocr",
"vision-language-model",
"document-processing",
"hf-jobs"
] | 2025-07-28T08:27:45 | null | null |
6982a83da22838536c463080 | GD-ML/SCASRec | GD-ML | {"task_categories": ["tabular-classification"], "tags": ["recommendation", "route", "generative list recommendation"]} | false | False | 2026-02-25T12:27:08 | 28 | 27 | false | 551f6e7ec67c31176291b3bce54449e7b316f044 |
SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation
This is the dataset for our paper.
The following table contains the feature dimensions and key features of our dataset.
Feature Type
Interpretation
Shape
Some Key Features
Route Features
Used to describe ea... | 967 | 967 | [
"task_categories:tabular-classification",
"size_categories:100K<n<1M",
"format:csv",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"recommendation",
"route",
"generative list recommendation"
] | 2026-02-04T02:00:29 | null | null |
699f5d42ad3359a3a1f7d220 | Goedel-LM/SFT_dataset_v2 | Goedel-LM | {"pretty_name": "Goedel-Prover-V2 SFT Dataset", "language": "en", "license": "other", "tags": ["theorem-proving", "lean4", "code"], "size_categories": "1M<n<10M", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | False | 2026-02-26T00:50:08 | 26 | 26 | false | 69553efc797ff17eaa18e3c60f8a9557a5275fdd |
Goedel-Prover-V2 SFT Dataset
SFT data. Total: 1,745,010 samples.
See Goedel-Prover-V2 paper.
Citation
@article{lin2025goedel,
title={Goedel-prover-v2: Scaling formal theorem proving with scaffolded data synthesis and self-correction},
author={Lin, Yong and Tang, Shange and Lyu, Bohan and Yang,... | 357 | 357 | [
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2508.03613",
"region:us",
"theorem-proving",
"lean4",
"code"
] | 2026-02-25T20:36:18 | null | null |
6996f9d7c633a08f9cec0674 | Solenopsisbot/real-slop | Solenopsisbot | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Real Slop"} | false | False | 2026-02-21T02:02:48 | 38 | 25 | false | 868e146adc54a76862b936a9774697fa0cd79e50 |
REAL SLOP
About
This is a dataset containing 155k entries of real llm interactions across a variety of models.
Privacy and Filtering
This dataset was created with users knowing that all requests they sent would be monitored and logged. This dataset has undergone pretty heaving filterin... | 193 | 210 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-19T11:53:59 | null | null |
69046ac0bdcb40370ae08f99 | google/MapTrace | google | {"license": "cc-by-4.0", "task_categories": ["image-to-text"], "language": ["en"], "tags": ["map"], "size_categories": ["1M<n<10M"]} | false | False | 2026-01-03T06:15:16 | 108 | 24 | false | 00bae0d2d917fd12548a089285d633dadf1bc81c |
MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps
Dataset Format
The dataset contains 2M annotated paths designed to train models on route-tracing tasks.
Splits:
maptrace_parquet: Contains paths on more complex, stylized maps such as those found in brochures, park directories or s... | 3,151 | 18,927 | [
"task_categories:image-to-text",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.19609",
"region:us",
"map"
] | 2025-10-31T07:52:32 | null | null |
69904ca73883cdc4e0d843b0 | skylenage/DeepVision-103K | skylenage | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-text-to-text"], "pretty_name": "DeepVision-103K", "tags": ["math", "multimodal", "reasoning", "rl"], "configs": [{"config_name": "visual_logic", "data_files": [{"split": "train", "path": "visual_logic-26k.parquet"}]}, {"... | false | False | 2026-02-26T15:46:28 | 23 | 23 | false | d90619a39b1d2db7815ec958f2d728c78daa80cb |
๐ญ DeepVision-103K
A Visually Diverse, Broad-Coverage, and Verifiable Mathematical Dataset for Multimodal Reasoning
Training on DeepVision-103K yields top performance on both multimodal mathematical reasoning and general multimodal benchmarks:
Average Performance on multimodal math and gen... | 1,524 | 1,524 | [
"task_categories:image-text-to-text",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.16742",
"arxiv:2507.1... | 2026-02-14T10:21:27 | null | null |
6995eab429908127acb95001 | ajibawa-2023/JavaScript-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "javascript"], "size_categories": ["1M<n<10M"]} | false | False | 2026-02-18T20:04:18 | 32 | 23 | false | d78dea8c5dd737b71c7024e0e4b2ed82bee4b436 | JavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript e... | 16,506 | 16,506 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"javascript"
] | 2026-02-18T16:37:08 | null | null |
699976f2d9b39c5c7980eb37 | FINAL-Bench/Metacognitive | FINAL-Bench | {"language": ["en"], "license": "apache-2.0", "pretty_name": "FINAL Bench \u2014 Functional Metacognitive Reasoning Benchmark", "size_categories": ["n<1K"], "task_categories": ["text-generation", "question-answering"], "tags": ["functional-metacognition", "self-correction", "reasoning", "benchmark", "error-recovery", "... | false | False | 2026-02-27T05:45:49 | 62 | 23 | false | 4ef5c7c3e7b559638685101611b62d1f704599f6 |
FINAL Bench: Functional Metacognitive Reasoning Benchmark
"Not how much AI knows โ but whether it knows what it doesn't know, and can fix it."
---
Overview
FINAL Bench (Frontier Intelligence Nexus for AGI-Level Verification) is the first comprehensive benchmark for evaluating ... | 9,399 | 9,399 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:document",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/7873",
... | 2026-02-21T09:12:18 | null | null |
67d45c3d35fc7f6d2ab224c8 | allenai/olmOCR-bench | allenai | {"license": "odc-by", "tags": ["text"], "configs": [{"config_name": "olmocr-bench", "data_files": [{"split": "arxiv_math", "path": ["bench_data/arxiv_math.jsonl"]}, {"split": "headers_footers", "path": ["bench_data/headers_footers.jsonl"]}, {"split": "long_tiny_text", "path": ["bench_data/long_tiny_text.jsonl"]}, {"spl... | false | False | 2026-02-19T17:28:38 | 109 | 21 | false | 54a96a6fb6a2bd3b297e59869491db4d3625b711 |
olmOCR-bench
olmOCR-bench is a dataset of 1,403 PDF files, plus 7,010 unit test cases that capture properties of the output that a good OCR system should have.
This benchmark evaluates the ability of OCR systems to accurately convert PDF documents to markdown format while preserving critical textual and str... | 2,839 | 32,080 | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:odc-by",
"size_categories:1K<n<10K",
"modality:document",
"modality:text",
"arxiv:2502.18443",
"region:us",
"text"
] | 2025-03-14T16:41:33 | null | null |
6791fcbb49c4df6d798ca7c9 | cais/hle | cais | {"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtyp... | false | auto | 2026-01-20T22:42:17 | 733 | 20 | false | 5a81a4c7271a2a2a312b9a690f0c2fde837e4c29 |
[!NOTE]
IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
Humanity's Last Exam
๐ Website | ๐ Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of huma... | 41,731 | 190,886 | [
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"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
6982ffd3cbaa03fa2bd18900 | lm-provers/FineProofs-SFT | lm-provers | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["math", "reasoning", "olympiad", "proof-generation", "chain-of-thought"], "pretty_name": "FineProofs SFT Dataset", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "all", "features": [{"... | false | False | 2026-02-14T14:01:35 | 36 | 20 | false | 73661e62811cf2940a0d3f82788a4f4332204c2f |
FineProofs SFT
Dataset Description
FineProofs SFT is a high-quality supervised fine-tuning dataset containing mathematical Olympiad problems paired with chain-of-thought reasoning and formal proofs distilled from DeepSeek-Math-V2. The dataset comprises 7,777 samples (4,300 unique problems) sourced... | 330 | 330 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
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"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroi... | 2026-02-04T08:14:11 | null | null |
699b3c7f3ccabf2d24343ff6 | ajibawa-2023/PHP-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "PHP"], "size_categories": ["10M<n<100M"]} | false | False | 2026-02-23T09:30:50 | 20 | 19 | false | a254d639f75f1c99b90af1ec13c799597a1f460e | PHP-Code-Large
PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.
By pro... | 111 | 111 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"PHP"
] | 2026-02-22T17:27:27 | null | null |
69a01dab10196ad126da8e66 | abdeljalilELmajjodi/Lhadith_dataset | abdeljalilELmajjodi | {"dataset_info": [{"config_name": "\u0627\u0644\u062a\u0641\u0633\u064a\u0631", "features": [{"name": "text", "dtype": "string"}, {"name": "source_\u0627\u0644\u0645\u0635\u062f\u0631", "dtype": "string"}, {"name": "narrator_\u0627\u0644\u0631\u0627\u0648\u064a", "dtype": "string"}, {"name": "ruling_\u0627\u0644\u062d\... | false | False | 2026-02-26T10:37:36 | 19 | 19 | false | f15d39855b005cd037b4a48cd9b923a6e7c369d6 |
Lhadith : Moroccan Hadith Dataset
A comprehensive collection of Hadiths. This dataset organizes prophetic traditions into various thematic topics, including beliefs, rulings, etiquette, and more.
Dataset Overview
The dataset consists of configs, each corresponding to a specific religious to... | 39 | 39 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-26T10:17:15 | null | null |
67335bb8f014ee49558ef3fe | PleIAs/common_corpus | PleIAs | {"language": ["en", "fr", "de", "zh", "it", "es", "ja", "pl", "la", "nl", "ru", "ar", "ko"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "common_corpus_1/subset_100_1.parquet"}]}]} | false | False | 2026-02-19T13:13:01 | 382 | 18 | false | b78a5c1dfe9a7f2ed3062f27e95a0af510d62ee6 |
Common Corpus
Full paper - ICLR 2026 oral
Common Corpus is the largest open and permissible licensed text dataset, comprising 2.27 trillion tokens (2,267,302,720,836 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Co... | 86,579 | 961,361 | [
"language:en",
"language:fr",
"language:de",
"language:zh",
"language:it",
"language:es",
"language:ja",
"language:pl",
"language:la",
"language:nl",
"language:ru",
"language:ar",
"language:ko",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"l... | 2024-11-12T13:44:24 | null | null |
69811d0763152b4ea6afd82b | OpenResearcher/OpenResearcher-Dataset | OpenResearcher | {"dataset_info": [{"config_name": "seed_42", "features": [{"name": "qid", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "messages", "list": [{"name": "channel", "dtype": "string"}, {"name": "content", "list": [{"name": "channel_config", "struct": [{"name": "... | false | False | 2026-02-12T22:23:38 | 106 | 18 | false | 447be0c730619d46e5eab75233a2fdab5eef5316 |
๐ค HuggingFace ๏ฝ
Blog ๏ฝ Slack | WeChat
Overview
OpenResearcher is a fully open agentic large language model (30B-A3B) designed for long-horizon deep research scenarios. It achieves an impressive 54.8% accuracy on BrowseComp-Plus, surpassing performance of GPT... | 15,520 | 15,520 | [
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-02T21:54:15 | null | null |
6928ac839f54f92be8b78d70 | TeichAI/claude-4.5-opus-high-reasoning-250x | TeichAI | nan | false | False | 2025-11-28T03:02:41 | 297 | 17 | false | 742c86f88b66bf53cb5961a25e4360f5582f4a6e | This is a reasoning dataset created using Claude Opus 4.5 with a reasoning depth set to high. Some of these questions are from reedmayhew and the rest were generated.
The dataset is meant for creating distilled versions of Claude Opus 4.5 by fine-tuning already existing open-source LLMs.
Stats
Costs: $ 52.3... | 5,708 | 16,001 | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-11-27T19:54:43 | null | null |
698a63a3700a694a5b946224 | allenai/prescience | allenai | {"license": "odc-by", "task_categories": ["text-generation", "question-answering"], "tags": ["scientific-papers", "arxiv", "citation-prediction", "author-prediction", "collaboration-prediction", "research-forecasting"], "size_categories": ["100K<n<1M"], "language": ["en"]} | false | False | 2026-02-25T02:42:04 | 17 | 17 | false | a6cbe3237468567f0dca6fc2834f4684c9463858 |
PreScience: A Benchmark for Forecasting Scientific Contributions
Dataset Summary
Can AI systems trained on the scientific record up to a fixed point in time forecast the scientific advances that follow? Such a capability could help researchers identify collaborators and impactful research directio... | 72 | 72 | [
"task_categories:text-generation",
"task_categories:question-answering",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.20459",
"region:us",
"scienti... | 2026-02-09T22:45:55 | null | null |
698dd2570db46090757245bc | markov-ai/computer-use | markov-ai | {"license": "apache-2.0", "task_categories": ["robotics", "image-to-text"], "tags": ["computer-use", "gui-agent", "osworld", "trajectories", "reinforcement-learning"], "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}]}]} | false | False | 2026-02-13T15:11:21 | 50 | 16 | false | de58c88b4b33dd03fa4d5d0f490748f576bd37b3 |
Computer Use Trajectories
Successful computer-use agent trajectories collected on OSWorld tasks.
Dataset Details
Rows: 160 (one per task trajectory)
Steps: 1,378 total across all trajectories (avg ~8.6 steps/task)
Agent: Gemini 3 Flash Preview with linearized accessibility-tree grounding
Score fi... | 724 | 724 | [
"task_categories:robotics",
"task_categories:image-to-text",
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"size_categories:n<1K",
"format:parquet",
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"modality:image",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"librar... | 2026-02-12T13:15:03 | null | null |
69932857f73a8ccc1e28ae4a | ajibawa-2023/Java-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "java"], "size_categories": ["10M<n<100M"]} | false | False | 2026-02-16T17:49:08 | 30 | 16 | false | be47bf9c12862c51ea21b272596fca301ebe4712 | Java-Code-Large
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-... | 3,899 | 3,899 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"java"
] | 2026-02-16T14:23:19 | null | null |
6996a0f665f352f44ec11a37 | Roman1111111/gemini-3-pro-10000x-hard-high-reasoning | Roman1111111 | {"license": "mit", "task_categories": ["question-answering", "text-generation", "reasoning"], "tags": ["code", "finance", "legal", "agent", "chemistry", "art", "synthetic", "gemini-3-pro", "hard-reasoning", "mathematics", "physics"], "size_categories": ["10K<n<100K"], "language": ["en"]} | false | False | 2026-02-20T03:49:27 | 20 | 16 | false | 5feedf31aaa6ff0ae0ee1bc8a169bc6bfaccbd5a |
Dataset Card for Gemini-3-Pro-Reasoning-10000x-high-reasoning
Dataset Details
Dataset Description
Suggestion: I would use it to fine tune glm- 4.7-flash, or other 30b moe models, but 2-20b llms work perfectly, you can fine tune Nanbeige 4.1 - 3b, gpt-oss:20b, or qwen3: 4b, 8... | 408 | 408 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"finance",
"legal",
"... | 2026-02-19T05:34:46 | null | null |
6716146cfc14a25260d39431 | openfoodfacts/product-database | openfoodfacts | {"language": ["en", "fr", "de", "es", "it", "nl", "pl", "pt", "sv", "bg", "ro", "fi", "ru", "nb", "cs", "th", "da", "hr", "hu", "ar", "el", "ja", "ca", "sr", "sl", "sk", "tr", "lt", "zh", "et", "lv", "xx", "uk", "id", "he", "vi", "is", "la", "in", "ko", "sq", "iw", "ka", "ms", "bs", "fa", "bn", "gl", "kk", "mk", "nn", ... | false | False | 2026-03-01T16:01:28 | 115 | 15 | false | c1949cfbbf6e66a92162c04a6459f22d60369841 |
Open Food Facts Database
What is ๐ Open Food Facts?
A food products database
Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.
Made by everyone
Open Food Facts is a no... | 4,984 | 72,890 | [
"language:en",
"language:fr",
"language:de",
"language:es",
"language:it",
"language:nl",
"language:pl",
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"language:bg",
"language:ro",
"language:fi",
"language:ru",
"language:nb",
"language:cs",
"language:th",
"language:da",
"language:hr",
"language:... | 2024-10-21T08:44:28 | null | null |
698b62cfacace680e3a87508 | microsoft/webgym_tasks | microsoft | {"license": "cdla-permissive-2.0", "task_categories": ["reinforcement-learning"], "language": ["en"], "tags": ["web-navigation", "web-agents", "task-planning"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "train.jsonl"}, {"split": "test", "path": "t... | false | False | 2026-02-10T18:37:17 | 15 | 15 | false | a61330203480bea9b90b8e954ecf0b084a114cca |
WebGym Tasks Dataset
Dataset Description
This dataset contains web navigation tasks for training and evaluating autonomous web agents. Each task consists of a natural language instruction that describes an action to be performed on a specific website, along with evaluation criteria and metadata.
... | 55 | 55 | [
"task_categories:reinforcement-learning",
"language:en",
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"size_categories:100K<n<1M",
"format:json",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.02439",
"region:us",
"web-navigation",
"web-agents... | 2026-02-10T16:54:39 | null | null |
End of preview. Expand in Data Studio
Changelog
NEW Changes July 25th
- added
baseModelsfield to models which shows the models that the user tagged as base models for that model
Example:
{
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
}
NEW Changes July 9th
- Fixed issue with
ggufcolumn with integer overflow causing import pipeline to be broken over a few weeks โ
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
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