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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"], "pretty_name": "Coding Agent Conversations", "configs": [{"config_name": "default", "data_files": "conversations.jsonl"}]} | false | False | 2026-02-25T16:14:13 | 266 | 200 | 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 down. The dataset it produces is yours to share.
Exported with DataClaw.
Tag: dataclaw โ Browse all DataClaw datasets
Stats
Metric
Value
Sessions
549โฆ See the full description on the dataset page: https://huggingface.co/datasets/peteromallet/dataclaw-peteromallet. | 4,896 | 4,896 | [
"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 |
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": "license", "dtype": "string"}], "splits": [{"name": "SWE_Rebench", "num_bytes": 19392208677, "num_examples": 77169}, {"name": "SWE_Smith", "num_bytes": 33088967556, "num_examples": 148001}, {"name": "R2E_Gym", "num_bytes": 6869123922, "num_examples": 32964}, {"name": "filtered_reward1", "num_bytes": 33547502194, "num_examples": 155144}], "download_size": 22788997561, "dataset_size": 92897802349}, {"config_name": "trajectories-tokenized_qwencoder", "features": [{"name": "trajectory_id", "dtype": "string"}, {"name": "reward", "dtype": "float64"}, {"name": "chat_template_applied", "dtype": "string"}, {"name": "input_ids", "list": "int32"}, {"name": "labels", "list": "int64"}], "splits": [{"name": "SWE_Rebench", "num_bytes": 64238782798, "num_examples": 77169}, {"name": "SWE_Smith", "num_bytes": 107118447512, "num_examples": 148001}, {"name": "R2E_Gym", "num_bytes": 23869485518, "num_examples": 32964}, {"name": "filtered_reward1", "num_bytes": 108349044091, "num_examples": 155144}], "download_size": 49985669802, "dataset_size": 303575759919}], "configs": [{"config_name": "trajectories", "data_files": [{"split": "SWE_Rebench", "path": "trajectories/SWE_Rebench-*"}, {"split": "SWE_Smith", "path": "trajectories/SWE_Smith-*"}, {"split": "R2E_Gym", "path": "trajectories/R2E_Gym-*"}, {"split": "filtered_reward1", "path": "trajectories/filtered_reward1-*"}]}, {"config_name": "trajectories-tokenized_qwencoder", "data_files": [{"split": "SWE_Rebench", "path": "trajectories-tokenized_qwencoder/SWE_Rebench-*"}, {"split": "SWE_Smith", "path": "trajectories-tokenized_qwencoder/SWE_Smith-*"}, {"split": "R2E_Gym", "path": "trajectories-tokenized_qwencoder/R2E_Gym-*"}, {"split": "filtered_reward1", "path": "trajectories-tokenized_qwencoder/filtered_reward1-*"}]}]} | false | False | 2026-02-26T18:22:08 | 121 | 121 | 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 parameters.
Limitations
Adaptability to different scaffolds: We generated all trajectories using a single scaffold and fixed tool set (no permutations). Models trained viaโฆ See the full description on the dataset page: https://huggingface.co/datasets/togethercomputer/CoderForge-Preview. | 8,413 | 8,413 | [
"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 |
698b2c8b4c9e577aa3b1fa16 | nohurry/Opus-4.6-Reasoning-3000x-filtered | nohurry | {"license": "apache-2.0"} | false | False | 2026-02-10T13:06:40 | 233 | 96 | 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.
| 2,022 | 2,022 | [
"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 |
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": [{"split": "train", "path": "synthetic_tasks/skill_based/easy/*/data_filtered.parquet"}]}, {"config_name": "skill_based_medium", "data_files": [{"split": "train", "path": "synthetic_tasks/skill_based/medium/*/data_filtered.parquet"}]}, {"config_name": "skill_based_mixed", "data_files": [{"split": "train", "path": "synthetic_tasks/skill_based/mixed/*/data_filtered.parquet"}]}]} | false | False | 2026-02-27T22:37:57 | 58 | 52 | 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 combines dataset adaptation with synthetic task generation across diverse domains.
๐ Key Results & Performance
The high-quality trajectories in Terminal-Corpus enableโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Corpus. | 744 | 744 | [
"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 |
698e4ad0913c4d1f4a64479a | crownelius/Opus-4.6-Reasoning-3300x | crownelius | {"license": "apache-2.0"} | false | False | 2026-03-02T05:37:24 | 81 | 51 | 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
Cleaning Report
Original rows: 3,305
Clean rows: 2,160
Removed: 1,145 (34.6%)
Columns: ['id', 'problem', 'thinking', 'solution', 'difficulty', 'category', 'timestamp', 'hash']โฆ See the full description on the dataset page: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3300x. | 565 | 565 | [
"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 |
69a5b45a59ca5dda6cff15a9 | TuringEnterprises/Open-RL | TuringEnterprises | {"license": "mit", "language": ["en"], "tags": ["chemistry", "physics", "math", "biology", "science"], "pretty_name": "open-rl", "size_categories": ["n<1K"], "task_categories": ["question-answering"]} | false | False | 2026-03-04T11:24:40 | 47 | 47 | false | cef3b89150d73474ec6b9203897ce2d8d2dcd2bf |
Open-RL
Dataset Summary
This dataset contains self-contained, verifiable, and unambiguous STEM reasoning problems across Physics, Mathematics, Biology, and Chemistry.
Each problem:
Requires multi-step reasoning
Involves symbolic manipulation and/or numerical computation
Has a deterministic, objectively verifiable final answer
The problems were evaluated against contemporary large language models. Observed pass rates indicate that the tasks are non-trivial yetโฆ See the full description on the dataset page: https://huggingface.co/datasets/TuringEnterprises/Open-RL. | 4 | 4 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"chemistry",
"physics",
"math",
"biology",
"science"
] | 2026-03-02T16:01:30 | 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 | 109 | 43 | 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 code files from repositories across ~4,700 unique developers
80+ programming languages included (Python, JavaScript, TypeScript, Rust, Go, C/C++, Java, and more)
Source code only โโฆ See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/github-top-code. | 1,077 | 1,077 | [
"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 |
6982a8b7a22838536c4632ea | GD-ML/GenMRP | GD-ML | {"task_categories": ["tabular-classification", "graph-ml"]} | false | False | 2026-02-08T13:39:59 | 42 | 41 | 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 Features
Includes the road segment attributes
K * 2 * N
Link lengthLink Lane width
Frequency Features
Logs the user's travel history within the past three months
K * 2 * 10 * 7
Deltaโฆ See the full description on the dataset page: https://huggingface.co/datasets/GD-ML/GenMRP. | 610 | 610 | [
"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 |
6982a756e346e0f1b5010cc4 | GD-ML/CCN | GD-ML | {"task_categories": ["tabular-classification"]} | false | False | 2026-02-08T13:35:55 | 37 | 37 | 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 describe each route, including static features, dynamic features, and trajectory statistical features
N * 62
The estimated time of arrival for the routeThe total distance lengthโฆ See the full description on the dataset page: https://huggingface.co/datasets/GD-ML/CCN. | 581 | 581 | [
"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 |
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_info": {"features": [{"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_examples": 3632}]}} | false | False | 2026-02-28T12:33:39 | 51 | 37 | 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 (command + description).
Dataset Summary
Source model
Qwen3.5-27B (DashScope API)
Samples
3,632
Thinking mode
Enabled (budget: 4096 tokens)
Sourceโฆ See the full description on the dataset page: https://huggingface.co/datasets/LocoreMind/qwen3.5-27b-cli-reasoning-3632x. | 594 | 594 | [
"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 |
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_gated_prompt": "## Terms of Use for The Stack v2\n\nThe Stack v2 dataset is a collection of source code in over 600 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. Downloading the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact [datasets@softwareheritage.org](mailto:datasets@softwareheritage.org?subject=TheStackV2%20request%20for%20dataset%20access%20information) for more information.\n2. If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n4. The Stack v2 is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack v2 to the most recent usable version.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}, "dataset_info": {"features": [{"name": "blob_id", "dtype": "string"}, {"name": "directory_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "content_id", "dtype": "string"}, {"name": "detected_licenses", "sequence": "string"}, {"name": "license_type", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "snapshot_id", "dtype": "string"}, {"name": "revision_id", "dtype": "string"}, {"name": "branch_name", "dtype": "string"}, {"name": "visit_date", "dtype": "timestamp[ns]"}, {"name": "revision_date", "dtype": "timestamp[ns]"}, {"name": "committer_date", "dtype": "timestamp[ns]"}, {"name": "github_id", "dtype": "int64"}, {"name": "star_events_count", "dtype": "int64"}, {"name": "fork_events_count", "dtype": "int64"}, {"name": "gha_license_id", "dtype": "string"}, {"name": "gha_event_created_at", "dtype": "timestamp[ns]"}, {"name": "gha_created_at", "dtype": "timestamp[ns]"}, {"name": "gha_language", "dtype": "string"}, {"name": "src_encoding", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "is_vendor", "dtype": "bool"}, {"name": "is_generated", "dtype": "bool"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "extension", "dtype": "string"}]}, "configs": [{"config_name": "default", "default": true, "data_files": [{"split": "train", "path": "data/*/*.parquet"}]}, {"config_name": "1C_Enterprise", "data_files": [{"split": "train", "path": "data/1C_Enterprise/*.parquet"}]}, {"config_name": "2-Dimensional_Array", "data_files": [{"split": "train", "path": "data/2-Dimensional_Array/*.parquet"}]}, {"config_name": "4D", "data_files": [{"split": "train", "path": "data/4D/*.parquet"}]}, {"config_name": "ABAP", "data_files": [{"split": "train", "path": "data/ABAP/*.parquet"}]}, {"config_name": "ABAP_CDS", "data_files": [{"split": "train", "path": "data/ABAP_CDS/*.parquet"}]}, {"config_name": "ABNF", "data_files": [{"split": "train", "path": "data/ABNF/*.parquet"}]}, {"config_name": "AGS_Script", "data_files": [{"split": "train", "path": "data/AGS_Script/*.parquet"}]}, {"config_name": "AIDL", "data_files": [{"split": "train", "path": "data/AIDL/*.parquet"}]}, {"config_name": "AL", "data_files": [{"split": "train", "path": "data/AL/*.parquet"}]}, {"config_name": "AMPL", "data_files": [{"split": "train", "path": "data/AMPL/*.parquet"}]}, {"config_name": "ANTLR", "data_files": [{"split": "train", "path": "data/ANTLR/*.parquet"}]}, {"config_name": "API_Blueprint", "data_files": [{"split": "train", "path": "data/API_Blueprint/*.parquet"}]}, {"config_name": "APL", "data_files": [{"split": "train", "path": "data/APL/*.parquet"}]}, {"config_name": "ASL", "data_files": [{"split": "train", "path": "data/ASL/*.parquet"}]}, {"config_name": "ASN.1", "data_files": [{"split": "train", "path": "data/ASN.1/*.parquet"}]}, {"config_name": "ASP.NET", "data_files": [{"split": "train", "path": "data/ASP.NET/*.parquet"}]}, {"config_name": "ATS", "data_files": [{"split": "train", "path": "data/ATS/*.parquet"}]}, {"config_name": "ActionScript", "data_files": [{"split": "train", "path": "data/ActionScript/*.parquet"}]}, {"config_name": "Ada", "data_files": [{"split": "train", "path": "data/Ada/*.parquet"}]}, {"config_name": "Adobe_Font_Metrics", "data_files": [{"split": "train", "path": "data/Adobe_Font_Metrics/*.parquet"}]}, {"config_name": "Agda", "data_files": [{"split": "train", "path": "data/Agda/*.parquet"}]}, {"config_name": "Alloy", "data_files": [{"split": "train", "path": "data/Alloy/*.parquet"}]}, {"config_name": "Alpine_Abuild", "data_files": [{"split": "train", "path": 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"data/X10/*.parquet"}]}, {"config_name": "XC", "data_files": [{"split": "train", "path": "data/XC/*.parquet"}]}, {"config_name": "XCompose", "data_files": [{"split": "train", "path": "data/XCompose/*.parquet"}]}, {"config_name": "XML", "data_files": [{"split": "train", "path": "data/XML/*.parquet"}]}, {"config_name": "XML_Property_List", "data_files": [{"split": "train", "path": "data/XML_Property_List/*.parquet"}]}, {"config_name": "XPages", "data_files": [{"split": "train", "path": "data/XPages/*.parquet"}]}, {"config_name": "XProc", "data_files": [{"split": "train", "path": "data/XProc/*.parquet"}]}, {"config_name": "XQuery", "data_files": [{"split": "train", "path": "data/XQuery/*.parquet"}]}, {"config_name": "XS", "data_files": [{"split": "train", "path": "data/XS/*.parquet"}]}, {"config_name": "XSLT", "data_files": [{"split": "train", "path": "data/XSLT/*.parquet"}]}, {"config_name": "X_BitMap", "data_files": [{"split": "train", "path": "data/X_BitMap/*.parquet"}]}, {"config_name": "X_Font_Directory_Index", "data_files": [{"split": "train", "path": "data/X_Font_Directory_Index/*.parquet"}]}, {"config_name": "X_PixMap", "data_files": [{"split": "train", "path": "data/X_PixMap/*.parquet"}]}, {"config_name": "Xojo", "data_files": [{"split": "train", "path": "data/Xojo/*.parquet"}]}, {"config_name": "Xonsh", "data_files": [{"split": "train", "path": "data/Xonsh/*.parquet"}]}, {"config_name": "Xtend", "data_files": [{"split": "train", "path": "data/Xtend/*.parquet"}]}, {"config_name": "YAML", "data_files": [{"split": "train", "path": "data/YAML/*.parquet"}]}, {"config_name": "YANG", "data_files": [{"split": "train", "path": "data/YANG/*.parquet"}]}, {"config_name": "YARA", "data_files": [{"split": "train", "path": "data/YARA/*.parquet"}]}, {"config_name": "YASnippet", "data_files": [{"split": "train", "path": "data/YASnippet/*.parquet"}]}, {"config_name": "Yacc", "data_files": [{"split": "train", "path": "data/Yacc/*.parquet"}]}, {"config_name": "Yul", "data_files": [{"split": "train", "path": "data/Yul/*.parquet"}]}, {"config_name": "ZAP", "data_files": [{"split": "train", "path": "data/ZAP/*.parquet"}]}, {"config_name": "ZIL", "data_files": [{"split": "train", "path": "data/ZIL/*.parquet"}]}, {"config_name": "Zeek", "data_files": [{"split": "train", "path": "data/Zeek/*.parquet"}]}, {"config_name": "ZenScript", "data_files": [{"split": "train", "path": "data/ZenScript/*.parquet"}]}, {"config_name": "Zephir", "data_files": [{"split": "train", "path": "data/Zephir/*.parquet"}]}, {"config_name": "Zig", "data_files": [{"split": "train", "path": "data/Zig/*.parquet"}]}, {"config_name": "Zimpl", "data_files": [{"split": "train", "path": "data/Zimpl/*.parquet"}]}, {"config_name": "cURL_Config", "data_files": [{"split": "train", "path": "data/cURL_Config/*.parquet"}]}, {"config_name": "desktop", "data_files": [{"split": "train", "path": "data/desktop/*.parquet"}]}, {"config_name": "dircolors", "data_files": [{"split": "train", "path": "data/dircolors/*.parquet"}]}, {"config_name": "eC", "data_files": [{"split": "train", "path": "data/eC/*.parquet"}]}, {"config_name": "edn", "data_files": [{"split": "train", "path": "data/edn/*.parquet"}]}, {"config_name": "fish", "data_files": [{"split": "train", "path": "data/fish/*.parquet"}]}, {"config_name": "hoon", "data_files": [{"split": "train", "path": "data/hoon/*.parquet"}]}, {"config_name": "jq", "data_files": [{"split": "train", "path": "data/jq/*.parquet"}]}, {"config_name": "kvlang", "data_files": [{"split": "train", "path": "data/kvlang/*.parquet"}]}, {"config_name": "mIRC_Script", "data_files": [{"split": "train", "path": "data/mIRC_Script/*.parquet"}]}, {"config_name": "mcfunction", "data_files": [{"split": "train", "path": "data/mcfunction/*.parquet"}]}, {"config_name": "mupad", "data_files": [{"split": "train", "path": "data/mupad/*.parquet"}]}, {"config_name": "nanorc", "data_files": [{"split": "train", "path": "data/nanorc/*.parquet"}]}, {"config_name": "nesC", "data_files": [{"split": "train", "path": "data/nesC/*.parquet"}]}, {"config_name": "ooc", "data_files": [{"split": "train", "path": "data/ooc/*.parquet"}]}, {"config_name": "q", "data_files": [{"split": "train", "path": "data/q/*.parquet"}]}, {"config_name": "reStructuredText", "data_files": [{"split": "train", "path": "data/reStructuredText/*.parquet"}]}, {"config_name": "robots.txt", "data_files": [{"split": "train", "path": "data/robots.txt/*.parquet"}]}, {"config_name": "sed", "data_files": [{"split": "train", "path": "data/sed/*.parquet"}]}, {"config_name": "wdl", "data_files": [{"split": "train", "path": "data/wdl/*.parquet"}]}, {"config_name": "wisp", "data_files": [{"split": "train", "path": "data/wisp/*.parquet"}]}, {"config_name": "xBase", "data_files": [{"split": "train", "path": "data/xBase/*.parquet"}]}]} | false | auto | 2024-04-23T15:52:32 | 510 | 36 | 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 further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories.bigcode/the-stack-v2-train-smol-ids: based on theโฆ See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-v2. | 8,948 | 254,461 | [
"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 |
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 Big Video Reasoning Training Data", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "generator", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "sample_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "metadata_json", "dtype": "string"}, {"name": "first_frame_path", "dtype": "string"}, {"name": "final_frame_path", "dtype": "string"}, {"name": "ground_truth_video_path", "dtype": "string"}, {"name": "tar_file", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 1000000}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/metadata.parquet"}]}]} | false | False | 2026-02-27T20:46:01 | 40 | 33 | 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 split: 100 curated reasoning task generators with 1,000,000 video clips (10,000 samples per generator), with each sample consisting of a video, start/end frames, a textualโฆ See the full description on the dataset page: https://huggingface.co/datasets/Video-Reason/VBVR-Dataset. | 1,433 | 1,433 | [
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"language:en",
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"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroi... | 2026-02-24T05:02:57 | null | null |
698a9b89700a694a5b97db6f | AudioVisual-Caption/ASID-1M | AudioVisual-Caption | {"license": "cc-by-2.0", "language": ["en"], "pretty_name": "ASID-1M", "tags": ["caption", "audiovisual", "instruction-tuning", "attribute-structured", "quality-verified", "video-understanding"], "task_categories": ["image-text-to-text"], "configs": [{"config_name": "all_attributes", "data_files": [{"split": "train", "path": ["annotations/0_30_s_youtube_v0_1/train/all_attributes_0_30_s_youtube_v0_1.jsonl", "annotations/30_60_s_youtube_v0_1/train/all_attributes_30_60_s_youtube_v0_1.jsonl", "annotations/1_2_m_youtube_v0_1/train/all_attributes_1_2_m_youtube_v0_1.jsonl", "annotations/finevideo/train/all_attributes_finevideo.jsonl"]}]}, {"config_name": "single_attribute", "data_files": [{"split": "train", "path": ["annotations/0_30_s_youtube_v0_1/train/single_attribute_0_30_s_youtube_v0_1.jsonl", "annotations/30_60_s_youtube_v0_1/train/single_attribute_30_60_s_youtube_v0_1.jsonl", "annotations/1_2_m_youtube_v0_1/train/single_attribute_1_2_m_youtube_v0_1.jsonl", "annotations/finevideo/train/single_attribute_finevideo.jsonl"]}]}]} | false | False | 2026-03-04T05:45:00 | 35 | 32 | false | 39f63879c7f8b6492b412a417e5647d3277d70e1 |
ASID-1M: Attribute-Structured and Quality-Verified Audiovisual Instructions
[๐ Homepage] [๐ Arxiv Paper] [๐ค Models & Datasets] [๐ป Code]
Introduction
We introduce ASID-1M, a large-scale audiovisual instruction dataset built to support universal video understanding with fine-grained, controllable supervision.
Most existing video-instruction data represents complex audiovisual content as a single, monolithic caption. This often leads to incomplete coverage (missingโฆ See the full description on the dataset page: https://huggingface.co/datasets/AudioVisual-Caption/ASID-1M. | 237 | 237 | [
"task_categories:image-text-to-text",
"language:en",
"license:cc-by-2.0",
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"library:mlcroissant",
"arxiv:2602.13013",
"region:us",
"caption",
"audiovisual",
"instruction-tu... | 2026-02-10T02:44:25 | null | null |
69a0ac7cc1f01f9b6b9031de | BytedTsinghua-SIA/CUDA-Agent-Ops-6K | BytedTsinghua-SIA | {"license": "cc-by-4.0", "pretty_name": "CUDA-Agent-Ops-6K", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2026-02-27T19:56:56 | 30 | 30 | false | 44a734c78c947bfcba5189cbfd13f57a6d29a698 |
CUDA-Agent-Ops-6K
CUDA-Agent-Ops-6K is a curated training dataset for CUDA kernel generation and optimization.
It is released as part of the CUDA-Agent project:
Project Page: https://CUDA-Agent.github.io/
Github Repo: https://github.com/BytedTsinghua-SIA/CUDA-Agent
Dataset Summary
CUDA-Agent-Ops-6K contains 6,000 synthesized operator-level training tasks designed for large-scale agentic RL training. It is intended to provide diverse and executable CUDA-oriented trainingโฆ See the full description on the dataset page: https://huggingface.co/datasets/BytedTsinghua-SIA/CUDA-Agent-Ops-6K. | 62 | 62 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-26T20:26:36 | 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 | 30 | 29 | 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 each route, including static features, dynamic features, and trajectory statistical features
N * 62
The estimated time of arrival for the routeThe total distance length of theโฆ See the full description on the dataset page: https://huggingface.co/datasets/GD-ML/SCASRec. | 1,358 | 1,358 | [
"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": "apache-2.0", "tags": ["theorem-proving", "lean4", "code"], "size_categories": "1M<n<10M", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | False | 2026-03-02T19:27:28 | 28 | 28 | false | da71b450f771880ea314a8860532ee26bf494753 |
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, Ziran and Chung, Jui-Hui and Zhao, Haoyu and Jiang, Lai and Geng, Yihan and Ge, Jiawei and Sun, Jingruo and others},
journal={arXiv preprint arXiv:2508.03613},
year={2025}
}
| 395 | 395 | [
"language:en",
"license:apache-2.0",
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"library:polars",
"library:mlcroissant",
"arxiv:2508.03613",
"region:us",
"theorem-proving",
"lean4",
"code"
] | 2026-02-25T20:36:18 | 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"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "math-77k.parquet"}]}]} | false | False | 2026-02-26T15:46:28 | 30 | 26 | 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 general multimodal benchmarks.
Training on DeepVision-103K elicits more efficient reasoning.
Benchmark
Qwen3-VL-8B-Instruct (Acc / Tokens)
Qwen3-VL-8B-DeepVision (Acc /โฆ See the full description on the dataset page: https://huggingface.co/datasets/skylenage/DeepVision-103K. | 1,847 | 1,847 | [
"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 |
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 | 27 | 21 | 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, 8b(note: better to fine tune newest versions(2507 4b qwen3 , or qwen 3 vl:8b)) for maximum improvement.
This dataset is a high-complexity synthetic reasoning corpus containingโฆ See the full description on the dataset page: https://huggingface.co/datasets/Roman1111111/gemini-3-pro-10000x-hard-high-reasoning. | 432 | 432 | [
"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 |
69a0413735be92a8b511584c | AweAI-Team/Scale-SWE | AweAI-Team | nan | false | False | 2026-02-27T05:39:32 | 21 | 21 | false | cdccb2de14bfbe01ec03b01d93de637fd4e13bc4 |
Immersion in the GitHub Universe: Scaling Coding Agents to Mastery
๐ฅ Highlights
Source from 6M+ pull requests and 23000+ repositories.
Cover 5200 Repositories.
100k high-quality instances.
71k trajectories from DeepSeek v3.2 with 3.5B token.
Strong performance: 64% in SWE-bench-Verified trained from Qwen3-30A3B-Instruct.
๐ฃ News
2026-02-26 ๐ We released a portion of our data on Hugging Face. This release includes 20,000 SWE taskโฆ See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/Scale-SWE. | 273 | 273 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.09892",
"region:us"
] | 2026-02-26T12:48:55 | 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", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 284205983, "num_examples": 2500}], "download_size": 274276147, "dataset_size": 284205983}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | auto | 2026-01-20T22:42:17 | 735 | 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 human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,500 questions across dozens ofโฆ See the full description on the dataset page: https://huggingface.co/datasets/cais/hle. | 42,919 | 193,504 | [
"benchmark:official",
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"size_categories:1K<n<10K",
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"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
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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 topic (ุงูู
ูุถูุน). The data is captured in its original Arabic text alongside metadata provided by the platform.
Statistics & Topics
Topic (Arabic)
Topic (English)โฆ See the full description on the dataset page: https://huggingface.co/datasets/abdeljalilELmajjodi/Lhadith_dataset. | 49 | 49 | [
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] | 2026-02-26T10:17:15 | null | null |
69839652036f5289e473e94a | nebius/SWE-rebench-V2 | nebius | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "software-engineering", "swe-bench", "nebius"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "base_commit", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "image_name", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "interface", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "pr_description", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "FAIL_TO_PASS", "sequence": "string"}, {"name": "PASS_TO_PASS", "sequence": "string"}, {"name": "install_config", "struct": [{"name": "base_image_name", "dtype": "string"}, {"name": "docker_specs", "struct": [{"name": "_variant", "dtype": "string"}, {"name": "bazel_version", "dtype": "string"}, {"name": "bun_version", "dtype": "string"}, {"name": "cargo_version", "dtype": "string"}, {"name": "deno_version", "dtype": "string"}, {"name": "docker_version", "dtype": "string"}, {"name": "erlang_version", "dtype": "string"}, {"name": "gcc_version", "dtype": "string"}, {"name": "go_version", "dtype": "string"}, {"name": "helm_version", "dtype": "string"}, {"name": "java_version", "dtype": "string"}, {"name": "jdk_version", "dtype": "string"}, {"name": "llvm_version", "dtype": "string"}, {"name": "lua_version", "dtype": "string"}, {"name": "luajit_version", "dtype": "string"}, {"name": "neovim_version", "dtype": "string"}, {"name": "node_version", "dtype": "string"}, {"name": "npm_version", "dtype": "string"}, {"name": "nvim_version", "dtype": "string"}, {"name": "pnpm_version", "dtype": "string"}, {"name": "python_image", "dtype": "string"}, {"name": "python_version", "dtype": "string"}, {"name": "redis_version", "dtype": "string"}, {"name": "ruby_version", "dtype": "string"}, {"name": "rust_version", "dtype": "string"}, {"name": "rustc_version", "dtype": "string"}, {"name": "solana_version", "dtype": "string"}, {"name": "sqlite_version", "dtype": "string"}]}, {"name": "install", "sequence": "string"}, {"name": "log_parser", "dtype": "string"}, {"name": "test_cmd", "dtype": "string"}]}, {"name": "meta", "struct": [{"name": "llm_metadata", "struct": [{"name": "code", "dtype": "string"}, {"name": "confidence", "dtype": "float64"}, {"name": "detected_issues", "struct": [{"name": "B1", "dtype": "bool"}, {"name": "B2", "dtype": "bool"}, {"name": "B3", "dtype": "bool"}, {"name": "B4", "dtype": "bool"}, {"name": "B5", "dtype": "bool"}, {"name": "B6", "dtype": "bool"}]}, {"name": "difficulty", "dtype": "string"}, {"name": "external_urls", "sequence": "string"}, {"name": "intent_completeness", "dtype": "string"}, {"name": "pr_categories", "sequence": "string"}, {"name": "reasoning", "dtype": "string"}, {"name": "test_alignment_issues", "sequence": "string"}]}, {"name": "num_modified_files", "dtype": "int64"}, {"name": "num_modified_lines", "dtype": "int64"}, {"name": "pr_author", "dtype": "string"}, {"name": "pr_labels", "sequence": "string"}, {"name": "pr_url", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2556623715, "num_examples": 32079}], "download_size": 510547989, "dataset_size": 2556623715}} | false | False | 2026-03-03T09:41:19 | 18 | 18 | false | 90879320f9d2b6a0cf0bbd9e3f07a2032608e769 |
SWE-rebench-V2
Dataset Summary
SWE-rebench-V2 is a curated dataset of software-engineering tasks derived from real GitHub issues and pull requests. The dataset contains 32,079 samples covering Python, Go, TypeScript, JavaScript, Rust, Java, PHP, Kotlin, Julia, Elixir, Scala, Swift, Dart, C, C++, C#, R, Clojure, OCaml, and Lua.
For log parser functions, base Dockerfiles, and the prompts used, please see https://github.com/SWE-rebench/SWE-rebench-V2The detailed technicalโฆ See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-rebench-V2. | 28 | 28 | [
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"arxiv:2602.23866",
"region:us",
"code",
"software-engineering",
"swe-ben... | 2026-02-04T18:56:18 | 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 | 58 | 17 | 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 business reports, scientific papers, slides, posters, maps, comics, infographics, and engineering drawings.By expanding coverage to new document domains and introducing richerโฆ See the full description on the dataset page: https://huggingface.co/datasets/VLR-CVC/DocVQA-2026. | 5,689 | 5,689 | [
"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 |
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 | 17 | 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 ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptationโฆ See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/JavaScript-Code-Large. | 19,577 | 19,577 | [
"task_categories:text-generation",
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"license:mit",
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"format:json",
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"library:datasets",
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6928ac839f54f92be8b78d70 | TeichAI/claude-4.5-opus-high-reasoning-250x | TeichAI | nan | false | False | 2025-11-28T03:02:41 | 305 | 16 | 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 (USD)
Total tokens (input + output): 2.13 M
| 5,779 | 16,194 | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
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] | 2025-11-27T19:54:43 | null | null |
6966f68558a9216ec1e0e909 | nvidia/Nemotron-Research-GooseReason-0.7M | nvidia | {"license": "cc-by-nc-4.0", "language": ["en"], "tags": ["reasoning", "rlvr", "math", "code", "stem", "nvidia"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "math", "path": "data/math-train.jsonl"}, {"split": "code", "path": "data/code-train.jsonl"}, {"split": "stem", "path": "data/stem-train.jsonl"}]}]} | false | False | 2026-03-01T13:58:54 | 15 | 15 | false | 043e538672eaf11b2195ddee7549e68ad3a1099e |
GooseReason-0.7M
Synthesized with Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text
GooseReason-0.7M is a large-scale RLVR dataset with over 0.7 million tasks across mathematics, programming, and general scientific domains, synthesized by the Golden Goose pipeline. It is used to train GooseReason-4B-Instruct, which achieves new state-of-the-art results among 4B-Instruct models across 15 diverse benchmarks, spanning mathematicsโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Research-GooseReason-0.7M. | 66 | 66 | [
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"reasoning",
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"nvi... | 2026-01-14T01:51:01 | null | null |
6993497cb265036892229930 | OmniLottie/MMLottie-2M | OmniLottie | {"license": "cc-by-nc-sa-4.0", "language": ["en"], "tags": ["lottie", "animation", "vector-graphics", "motion-graphics", "multi-modal"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "Lottie", "data_files": "data/Lottie/*.parquet"}, {"config_name": "Lottie_SVG", "data_files": "data/Lottie_SVG/*.parquet"}]} | false | False | 2026-03-03T05:56:44 | 15 | 15 | false | f128648b38164ba0a94dfa815a293f4a041fb14e |
MMLottie-2M Dataset
The first large-scale Lottie animation dataset for multi-modal vector animation generation, containing ~2M samples with diverse motion patterns and visual styles.
Dataset Overview
MMLottie-2M consists of two complementary subsets designed to support comprehensive training for Lottie animation generation:
1. Lottie Subset
Native Lottie animations collected from major online platforms including LottieFiles, IconScout, Flaticon, Iconfont, andโฆ See the full description on the dataset page: https://huggingface.co/datasets/OmniLottie/MMLottie-2M. | 47 | 47 | [
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"lottie",
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"vector-graphics",
"motion-gra... | 2026-02-16T16:44:44 | 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 | 15 | 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 providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream codeโฆ See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/PHP-Code-Large. | 117 | 117 | [
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"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 |
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", "declarative-procedural-gap", "cognitive-bias", "TICOS", "AGI-evaluation", "LLM-evaluation", "metacognition"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "FINAL_Bench_100.jsonl"}]}]} | false | False | 2026-02-27T05:45:49 | 64 | 14 | 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 functional metacognition in Large Language Models (LLMs).
Unlike existing benchmarks (MMLU, HumanEval, GPQA) that measure only final-answer accuracy, FINAL Bench evaluates theโฆ See the full description on the dataset page: https://huggingface.co/datasets/FINAL-Bench/Metacognitive. | 9,609 | 9,609 | [
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"format:json",
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"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/7873",
... | 2026-02-21T09:12:18 | null | null |
6969acb43e3da85000a87abf | OmniLottie/MMLottieBench | OmniLottie | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "video", "dtype": "video"}, {"name": "task_type", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "real", "num_bytes": 8287690, "num_examples": 450}, {"name": "synthetic", "num_bytes": 276071888, "num_examples": 450}], "download_size": 284211476, "dataset_size": 284359578}, "configs": [{"config_name": "default", "data_files": [{"split": "real", "path": "data/real-*"}, {"split": "synthetic", "path": "data/synthetic-*"}]}], "license": "cc-by-nc-sa-4.0", "language": ["en"], "tags": ["Lottie", "animation", "vector-graphics", "multimodal"]} | false | False | 2026-03-03T05:57:53 | 13 | 13 | false | dddaeac454c0f112feffe2393038ab58507ef429 |
Dataset Description
MMLottieBench is a comprehensive evaluation protocol for multi-modal vector animation generation. The lack of mature and standardized benchmarks and metrics for vector animation generation poses significant challenges in evaluating (1) the quality of generated vector animations and (2) the extent to which generators faithfully follow multi-modal instructions.
Our benchmark addresses these challenges by providing:
Real Subset: 450 samples curated fromโฆ See the full description on the dataset page: https://huggingface.co/datasets/OmniLottie/MMLottieBench. | 55 | 55 | [
"language:en",
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"modality:image",
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"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2603.02138",
"region:us",
"Lottie",
"animation",
"vector-graphics",
"multimodal"
... | 2026-01-16T03:12:52 | 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 | 40 | 13 | 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 filtering of PII, maybe too much but oh well better safe than sorry.
Tools
Yeah so funny story... I kind of wasn't logging tool defenitions until partway into collecting thisโฆ See the full description on the dataset page: https://huggingface.co/datasets/Solenopsisbot/real-slop. | 202 | 219 | [
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"language:en",
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"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 |
66212f29fb07c3e05ad0432e | HuggingFaceFW/fineweb | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2025-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-05/*"}]}, {"config_name": "CC-MAIN-2025-08", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-08/*"}]}, {"config_name": "CC-MAIN-2025-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-13/*"}]}, {"config_name": "CC-MAIN-2025-18", "data_files": [{"split": "train", "path": 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"CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]} | false | False | 2025-07-11T20:16:53 | 2,684 | 12 | false | 9bb295ddab0e05d785b879661af7260fed5140fc |
๐ท FineWeb
15 trillion tokens of the finest data the ๐ web has to offer
What is it?
The ๐ท FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the ๐ญ datatrove library, our large scale data processing library.
๐ท FineWeb was originally meant to be a fully open replication of ๐ฆ
RefinedWeb, with a releaseโฆ See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 165,563 | 6,368,327 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10B<n<100B",
"modality:tabular",
"modality:text",
"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | 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"]}, {"split": "multi_column", "path": ["bench_data/multi_column.jsonl"]}, {"split": "old_scans", "path": ["bench_data/old_scans.jsonl"]}, {"split": "old_scans_math", "path": ["bench_data/old_scans_math.jsonl"]}, {"split": "table_tests", "path": ["bench_data/table_tests.jsonl"]}]}], "language": ["en"], "pretty_name": "olmOCR-bench", "size_categories": ["1K<n<10K"]} | false | False | 2026-02-19T17:28:38 | 113 | 12 | 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 structural information.
Quick links:
๐ Paper
๐ ๏ธ Code
๐ฎ Demo
Table 1. Distribution of Test Classes by Document Source
Document Source
Text Present
Textโฆ See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-bench. | 2,884 | 32,202 | [
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"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 |
69046ac0bdcb40370ae08f99 | google/MapTrace | google | {"license": "cc-by-4.0", "task_categories": ["image-to-text", "visual-question-answering"], "language": ["en"], "tags": ["map", "navigation"], "size_categories": ["1M<n<10M"]} | false | False | 2026-03-03T11:35:20 | 109 | 12 | false | 8dd60adfde2f189768f27204c78ec44af07a67bf |
MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps
Welcome to the MapTrace dataset! If you use this dataset in your work, please cite our paper below.
For more details about our methodology and findings, please visit our project page or read the official white paper.
This work was also recently featured on the Google Research Blog.
Code & Scripts
Official training and data loading scripts are available in our GitHub repository:โฆ See the full description on the dataset page: https://huggingface.co/datasets/google/MapTrace. | 15,484 | 31,362 | [
"task_categories:image-to-text",
"task_categories:visual-question-answering",
"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",
"... | 2025-10-31T07:52:32 | null | null |
69253bac1213f45122661c76 | pulsia/fr-bench-pdf2md | pulsia | {"license": "mit", "arxiv": 2602.1196, "github": "ld-lab-pulsia/vlmparse", "library_name": "vlmparse", "tags": ["vision", "ocr", "vlm", "document-parsing", "image-to-text"], "space": "pulsia/fr-bench-pdf2md", "language": ["fr"], "size_categories": ["1K<n<10K"]} | false | False | 2026-02-26T08:09:08 | 16 | 12 | false | 6ee20957e8dadf8523a4b9033498dee03c0b0368 |
fr-bench-pdf2md Benchmark
[๐ arXiv] | [Dataset (๐คHugging Face)] | [pypi] | [vlmparse] | [Benchmark] | [Leaderboard]
fr-bench-pdf2md is a benchmark and dataset for evaluating PDF-to-Markdown conversion with visionโlanguage models on challenging French documents. It is designed for practitioners who need reliable document parsing as a front-end to RAG and other LLM pipelines, where the quality of the Markdown (structure + content) matters more than exact character-levelโฆ See the full description on the dataset page: https://huggingface.co/datasets/pulsia/fr-bench-pdf2md. | 3,489 | 5,900 | [
"language:fr",
"license:mit",
"size_categories:1K<n<10K",
"arxiv:2602.11960",
"region:us",
"vision",
"ocr",
"vlm",
"document-parsing",
"image-to-text"
] | 2025-11-25T05:16:28 | null | null |
699e4eef01ea68738e116111 | peteromallet/my-personal-codex-data | peteromallet | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["dataclaw", "claude-code", "codex-cli", "conversations", "coding-assistant", "tool-use", "agentic-coding", "gpt-5.3-codex"], "pretty_name": "Coding Agent Conversations"} | false | False | 2026-02-25T01:23:00 | 17 | 12 | false | 772c95e0d1b4a0238e1a68371493313efc466d42 |
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 down. The dataset it produces may or may not be useful for training, but the point is that it's yours to share.
Exported with DataClaw.
Tag: dataclaw โ Browse all DataClawโฆ See the full description on the dataset page: https://huggingface.co/datasets/peteromallet/my-personal-codex-data. | 280 | 280 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"region:us",
"dataclaw",
"claude-code",
"codex-cli",
"conversations",
"coding-assistant",
"tool-use",
"agentic-coding",
"gpt-5.3-codex"
] | 2026-02-25T01:22:55 | null | null |
69a0500920cb585a88327108 | AweAI-Team/Scale-SWE-Distilled | AweAI-Team | nan | false | False | 2026-02-28T08:26:06 | 12 | 12 | false | c14ce2130f494812fef907f4afc81d8e33990805 |
Immersion in the GitHub Universe: Scaling Coding Agents to Mastery
๐ฅ Highlights
Source from 6M+ pull requests and 23000+ repositories.
Cover 5200 Repositories.
100k high-quality instances.
71k trajectories from DeepSeek v3.2 with 3.5B token.
Strong performance: 64% in SWE-bench-Verified trained from Qwen3-30A3B-Instruct.
๐ฃ News
2026-02-26 ๐ We released a portion of our data on Hugging Face. This release includes 20,000 SWE taskโฆ See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/Scale-SWE-Distilled. | 288 | 288 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2602.09892",
"region:us"
] | 2026-02-26T13:52:09 | null | null |
69a06a6e6946e3aa6a37296e | ronantakizawa/webui | ronantakizawa | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-to-text", "text-generation", "object-detection"], "tags": ["code-generation", "ui", "screenshot", "html", "css", "web-development", "design-systems", "frontend", "bounding-boxes", "multi-viewport", "responsive-design"], "pretty_name": "WebUI", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}, {"split": "validation", "path": "data/validation-*.parquet"}, {"split": "test", "path": "data/test-*.parquet"}]}], "dataset_info": {"config_name": "default", "features": [{"name": "sample_id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "html", "dtype": "string"}, {"name": "css", "dtype": "string"}, {"name": "js", "dtype": "string"}, {"name": "viewport", "dtype": "string"}, {"name": "source_name", "dtype": "string"}, {"name": "source_url", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "framework", "dtype": "string"}, {"name": "css_framework", "dtype": "string"}, {"name": "component_type", "dtype": "string"}, {"name": "element_count", "dtype": "int32"}, {"name": "has_animations", "dtype": "bool"}, {"name": "bboxes", "sequence": [{"name": "tag", "dtype": "string"}, {"name": "x", "dtype": "int32"}, {"name": "y", "dtype": "int32"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "class", "dtype": "string"}, {"name": "parent_index", "dtype": "int32"}]}], "splits": [{"name": "train", "num_examples": 29409}, {"name": "validation", "num_examples": 3702}, {"name": "test", "num_examples": 3696}]}} | false | False | 2026-02-28T07:25:18 | 12 | 12 | false | 71982976513a2d0faa88930dc57169b0d59b878b |
WebUI
A large-scale dataset pairing real-world UI screenshots with their original HTML, CSS, and JavaScript source code, per-viewport bounding boxes for every visible DOM element, and GPT-4.1 vision descriptions. Every sample is rendered at three responsive breakpoints. Built from public design systems, component libraries, open-source projects, and community code โ not synthetically generated.
Overview
Stat
Value
Total rows
36,807
Unique UI samples
12โฆ See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/webui. | 39 | 39 | [
"task_categories:image-to-text",
"task_categories:text-generation",
"task_categories:object-detection",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:po... | 2026-02-26T15:44:46 | null | null |
63990f21cc50af73d29ecfa3 | fka/prompts.chat | fka | {"license": "cc0-1.0", "tags": ["ChatGPT", "prompts", "AI", "GPT", "Claude", "Gemini", "Llama", "Mistral", "LLM", "prompt-engineering", "conversational-ai", "text-generation", "chatbot", "awesome-list"], "task_categories": ["question-answering", "text-generation"], "size_categories": ["100K<n<1M"]} | false | False | 2026-03-04T03:49:27 | 9,608 | 11 | false | e1f517879ec1d03b1958ab50d444a002143f7a0b |
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat โ a social platform for AI prompts.
๐ข Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
๐ Website: prompts.chat
๐ฆ GitHub: github.com/f/awesome-chatgpt-prompts
About
prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community. The project can beโฆ See the full description on the dataset page: https://huggingface.co/datasets/fka/prompts.chat. | 20,388 | 461,341 | [
"task_categories:question-answering",
"task_categories:text-generation",
"license:cc0-1.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"ChatGPT",
"prompts",
"AI",
"GPT",
"Claude"... | 2022-12-13T23:47:45 | null | null |
68873481d4a41fe542ba35b7 | uv-scripts/ocr | uv-scripts | {"viewer": false, "tags": ["uv-script", "ocr", "vision-language-model", "document-processing", "hf-jobs"]} | false | False | 2026-03-02T13:35:24 | 120 | 11 | false | bb7928b28e13bb1a175a321baa3faa158659d262 |
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 samples
hf jobs uv run --flavor l4x1 \
--secrets HF_TOKEN \
https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
your-input-datasetโฆ See the full description on the dataset page: https://huggingface.co/datasets/uv-scripts/ocr. | 977 | 3,643 | [
"region:us",
"uv-script",
"ocr",
"vision-language-model",
"document-processing",
"hf-jobs"
] | 2025-07-28T08:27:45 | null | null |
6980519f5b8376816ec5618f | togethercomputer/CoderForge-Preview-32B-SWE-Bench-Verified-Evaluation-trajectories | togethercomputer | {"dataset_info": {"config_name": "trajectory", "features": [{"name": "trajectory_id", "dtype": "string"}, {"name": "run_id", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "reward", "dtype": "float64"}, {"name": "num_steps", "dtype": "int64"}, {"name": "test_output", "dtype": "string"}, {"name": "reward_calc_time", "dtype": "string"}, {"name": "exp_name", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "output_patch", "dtype": "string"}, {"name": "messages", "dtype": "string"}, {"name": "agent_log", "dtype": "string"}, {"name": "ds", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 600649325, "num_examples": 500}], "download_size": 158992877, "dataset_size": 600649325}, "configs": [{"config_name": "trajectory", "data_files": [{"split": "train", "path": "trajectory/train-*"}]}]} | false | False | 2026-02-02T07:26:27 | 11 | 11 | false | 753f050488d4ec332491dbebedaafca79baac3aa | null | 97 | 97 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-02T07:26:23 | 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": [{"name": "problem", "dtype": "string"}, {"name": "reasoning_content", "dtype": "string"}, {"name": "proof", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "competition", "dtype": "string"}, {"name": "gemini-3-pro-grade", "dtype": "int64"}, {"name": "qwen3-4b-thinking-reward@128", "dtype": "float64"}, {"name": "source", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1261177070, "num_examples": 7777}], "download_size": 559037559, "dataset_size": 1261177070}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "reasoning_content", "dtype": "string"}, {"name": "proof", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "competition", "dtype": "string"}, {"name": "gemini-3-pro-grade", "dtype": "int64"}, {"name": "qwen3-4b-thinking-reward@128", "dtype": "float64"}, {"name": "source", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 694239300, "num_examples": 4281}], "download_size": 346092746, "dataset_size": 694239300}], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | False | 2026-02-14T14:01:35 | 36 | 11 | 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 from international Olympiad competitions and Art of Problem Solving (AoPS), each annotated with:
Detailed reasoning traces (thinking content) generated byโฆ See the full description on the dataset page: https://huggingface.co/datasets/lm-provers/FineProofs-SFT. | 359 | 359 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroi... | 2026-02-04T08:14:11 | 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": "test.jsonl"}]}]} | false | False | 2026-02-10T18:37:17 | 17 | 11 | 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.
Dataset Summary
Total Training Tasks: 292,092
Total Test Tasks: 1,167
Domains: Multiple domains including Lifestyle & Leisure, Sports & Fitness, and more
Sourceโฆ See the full description on the dataset page: https://huggingface.co/datasets/microsoft/webgym_tasks. | 79 | 79 | [
"task_categories:reinforcement-learning",
"language:en",
"license:cdla-permissive-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.02439",
"region:us",
"web-navigation",
"web-agents... | 2026-02-10T16:54:39 | null | null |
69a3c2fb500cc88f18795f51 | dddraxxx/ref-adv-s | dddraxxx | {"license": "cc-by-4.0", "task_categories": ["visual-question-answering", "object-detection"], "language": ["en"], "tags": ["referring-expression-comprehension", "visual-grounding", "mllm", "benchmark"], "size_categories": ["1K<n<10K"]} | false | False | 2026-03-02T03:47:02 | 11 | 11 | false | e7a53e352b5885b8228fc6afa8645ab78e76d5f1 |
Ref-Adv-s
๐ Website | ๐ฅ๏ธCode | ๐Results | ๐Paper
Ref-Adv-s is the publicly released subset of the Ref-Adv benchmark from our paper "Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks" (ICLR 2026).
Overview
Referring Expression Comprehension (REC) links natural language to region-level visual perception โ given an image and a text expression, the task is to localize the described object. Standard benchmarks such as RefCOCO, RefCOCO+, and RefCOCOgโฆ See the full description on the dataset page: https://huggingface.co/datasets/dddraxxx/ref-adv-s. | 20 | 20 | [
"task_categories:visual-question-answering",
"task_categories:object-detection",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:m... | 2026-03-01T04:39:23 | 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 | 18 | 10 | 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 directions, and anticipate which problems and methods will become central next. We introduce PreScience, a scientific forecasting benchmark that decomposes the research process into fourโฆ See the full description on the dataset page: https://huggingface.co/datasets/allenai/prescience. | 81 | 81 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.20459",
"region:us",
"scienti... | 2026-02-09T22:45:55 | 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
Updated Daily
- Downloads last month
- 7,152
Size of downloaded dataset files:
1.74 GB
Size of the auto-converted Parquet files:
1.74 GB
Number of rows:
4,717,862