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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
69ca9b695a4dac480491fd13 | lambda/hermes-agent-reasoning-traces | lambda | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["tool-calling", "function-calling", "agent", "hermes", "reasoning", "sharegpt", "sft", "traces"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "kimi", "data_files": [{"split": "train", "path": "data/kimi/tra... | false | False | 2026-04-17T10:06:39 | 188 | 90 | false | b92885e4f0161d4b2536512710e004d4892cac6e |
Hermes Agent Reasoning Traces
Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results.
This dataset has two configs, one per source model:
Config
M... | 3,067 | 3,067 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"tool-calling",
"function-calling... | 2026-03-30T15:48:57 | null | null |
69b186f91cde8c71bb8f76b0 | Roman1111111/claude-opus-4.6-10000x | Roman1111111 | {"license": "mit"} | false | False | 2026-04-05T13:42:24 | 222 | 64 | false | d6fe6aafcf5db8141153a0828c791eeee512b171 | This is a high-fidelity reasoning dataset synthesized using Claude Opus 4.6. The dataset is designed to capture the model's internal "Chain of Thought" and reasoning traces, specifically focusing on mathematical accuracy and structured logical deduction.
The dataset is intended for Supervised Fine-Tuning (SFT) and Dist... | 5,658 | 6,348 | [
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-03-11T15:15:05 | null | null |
69d7079054a04b1f8d367f16 | llamaindex/ParseBench | llamaindex | {"license": "apache-2.0", "configs": [{"config_name": "parse-bench", "features": [{"name": "pdf", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "rule", "dtype": "string"}, {"name": "page", "dtype": "int64"}, {"name": "expect... | false | False | 2026-04-19T01:48:09 | 55 | 49 | false | 2805a1d940f95a203e0ae4b88be9934f7765b3fc |
ParseBench
Quick links: [🌐 Website] [📜 Paper] [💻 Code]
ParseBench is a benchmark for evaluating document parsing systems on real-world enterprise documents, with the following characteristics:
Multi-dimensional evaluation. The benchmark is stratified into five capability dimensions — tables, charts, con... | 8,222 | 8,222 | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2604.08538",
"region... | 2026-04-09T01:57:36 | null | null |
69c45b9e5030946bd70055bf | ianncity/KIMI-K2.5-1000000x | ianncity | {"license": "apache-2.0", "language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "chain-of-thought", "instruction-tuning", "sft"], "configs": [{"config_name": "General-Distillation", "data_files": [{"split": "train", "path": "kimi-k2.5-m... | false | False | 2026-04-07T02:04:22 | 231 | 36 | false | de244b70a988b37cecd56ab69052591b3f28e845 |
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Scie... | 3,836 | 3,836 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",
"chain-of-thou... | 2026-03-25T22:03:10 | null | null |
69dc4cdf802d89435c5755c6 | Kassadin88/GLM-5.1-1000000x | Kassadin88 | {"license": "apache-2.0", "language": ["en", "zh"], "size_categories": ["n>1M"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "chain-of-thought", "instruction-tuning", "sft", "distillation", "glm", "glm-5.1"], "configs": [{"config_name": "main", "data_files": [{"split": "train", "... | false | False | 2026-04-17T05:25:15 | 29 | 29 | false | fe693a9a89c11447e86e1670f9a20de422f3e43c |
GLM-5.1-1000000x
1,003,589 reasoning traces distilled by GLM-5.1, using questions from KIMI-K2.5-1000000x.
Each entry contains a full chain-of-thought reasoning trace followed by the final answer, generated by GLM-5.1.
Complete! All 1,003,589 prompts distilled successfully.
█████████████████████████████... | 275 | 275 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",... | 2026-04-13T01:54:39 | null | null |
69bd84f2046cd4daeb541faa | microsoft/OpenMementos | microsoft | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "tags": ["reasoning", "chain-of-thought", "context-compression", "synthetic", "memento"], "pretty_name": "OpenMementos-228K", "dataset_info": [{"config_name": "default", "features": [{"name": "problem", "dty... | false | False | 2026-04-08T18:56:54 | 42 | 24 | false | caaf4bfe9741b8e49253de2d7d07e54567777245 |
OpenMementos-228K
A dataset of 228,557 reasoning traces annotated with block segmentation and compressed summaries (mementos), derived from OpenThoughts-v3.
Memento is a framework for teaching language models to manage their own context during long-form reasoning. Instead of generating one long, unstructured... | 862 | 871 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",
"chain-of-thought",
"context... | 2026-03-20T17:33:38 | null | null |
69e1bed4cc8fb2e676e4aa7c | Jackrong/GLM-5.1-Reasoning-1M-Cleaned | Jackrong | {"license": "apache-2.0", "language": ["en", "zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "chain-of-thought", "instruction-tuning", "sft", "distillation", "glm", "glm-5.1", "cleaned"], "configs": [{"config_name": "main", "default": true, "d... | false | False | 2026-04-19T05:05:17 | 24 | 24 | false | f6d6ccafe40359d5ec2515ee25e92aac8cae9c3d |
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversatio... | 251 | 251 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",... | 2026-04-17T05:02:12 | null | null |
69d3b00b2d56eb23d8824420 | badlogicgames/pi-mono | badlogicgames | {"pretty_name": "coding agent session traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "coding-agent", "pi-share-hf"], "language": ["en", "code"], "license": "other"} | false | False | 2026-04-06T13:10:36 | 73 | 23 | false | dac2a1d3ba12dda597b973a791a77618ccb5f413 |
Coding agent session traces for badlogicgames/pi-mono
This dataset contains redacted coding agent session traces collected while working on https://github.com/badlogic/pi-mono.git. The traces were exported with pi-share-hf from a local pi workspace and filtered to keep only sessions that passed deterministic... | 10,649 | 10,649 | [
"task_categories:text-generation",
"language:en",
"language:code",
"license:other",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"coding-agent",
"... | 2026-04-06T13:07:23 | null | null |
69b27063693ba5b211bd0a99 | markov-ai/computer-use-large | markov-ai | {"license": "cc-by-4.0", "task_categories": ["video-classification", "robotics"], "language": ["en"], "tags": ["screen-recording", "computer-use", "software-tutorials", "gui", "desktop"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "autocad", "data_files": [{"split": "train", "path": ["data/autocad/*... | false | False | 2026-03-16T03:51:15 | 167 | 22 | false | b50aeccec6d24a56ed4f8fbb9f5b2a16846b46a9 |
Computer Use Large
A large-scale dataset of 48,478 screen recording videos (~12,300 hours) of professional software being used, sourced from the internet. All videos have been trimmed to remove non-screen-recording content (intros, outros, talking heads, transitions) and audio has been stripped.
Data... | 32,796 | 138,693 | [
"task_categories:video-classification",
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"region:us",
"screen-recording",
"computer-use",
"software-tutorials",
"gui",
"desktop"
] | 2026-03-12T07:50:59 | null | null |
698e4ad0913c4d1f4a64479a | Crownelius/Opus-4.6-Reasoning-3300x | Crownelius | {"license": "apache-2.0"} | false | False | 2026-04-16T05:11:35 | 278 | 18 | false | 7c60afbc57b339055e1140ffbfafe034a2e4be1f |
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
Cle... | 3,263 | 5,945 | [
"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 |
6915476356b9127d79e06daa | TeraflopAI/SEC-EDGAR | TeraflopAI | {"license": "apache-2.0", "task_categories": ["text-generation", "text-classification"], "language": ["en"], "tags": ["finance", "edgar", "sec"], "size_categories": ["1M<n<10M"]} | false | False | 2026-04-17T21:13:20 | 18 | 17 | false | 43de32c4c3cd4a56c26757345a4ec6db04fccda0 | Datamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by... | 2,849 | 2,892 | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"modality:text",
"region:us",
"finance",
"edgar",
"sec"
] | 2025-11-13T02:50:11 | null | null |
698b2c8b4c9e577aa3b1fa16 | nohurry/Opus-4.6-Reasoning-3000x-filtered | nohurry | {"license": "apache-2.0"} | false | False | 2026-03-31T12:43:36 | 553 | 17 | false | 1cd388e9e1172066092a2b53e33dbdd3249b77bd |
[!WARNING] NOTICE: The original dataset has been updated with better filtering. Please use the original dataset, not this one.
Filtered from: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3000x
The original dataset has 979 refusals, I removed these in this version.
| 9,815 | 16,280 | [
"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 |
69d30e98196c91c299ebca28 | nvidia/QCalEval | nvidia | {"license": "cc-by-4.0", "task_categories": ["visual-question-answering", "image-to-text"], "language": ["en"], "tags": ["vlm-benchmark", "quantum-computing", "scientific-figure-understanding", "calibration", "multimodal"], "size_categories": ["n<1K"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"... | false | False | 2026-04-13T22:55:17 | 12 | 12 | false | b611794244a251c47fc67eb801b92f05722c369c |
QCalEval
Dataset Description
The dataset contains scientific plots from quantum computing calibration experiments, paired with vision-language question-answer (QA) pairs. The dataset is used to evaluate a model's ability to interpret, classify, and reason about experimental results.
The dataset co... | 485 | 485 | [
"task_categories:visual-question-answering",
"task_categories:image-to-text",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroiss... | 2026-04-06T01:38:32 | null | null |
69d67f1f9705324cf3430656 | nvidia/OCR-Synthetic-Multilingual-v1 | nvidia | {"license": "cc-by-4.0", "task_categories": ["object-detection", "image-to-text"], "language": ["en", "ja", "ko", "ru", "zh"], "tags": ["ocr", "text-detection", "text-recognition", "synthetic-data", "synthdog", "hdf5", "nvidia", "nemotron"], "pretty_name": "OCR Synthetic Multilingual v1", "size_categories": ["10M<n<100... | false | False | 2026-04-09T16:12:56 | 12 | 12 | false | 33dea622ac271cae8d100a85264de7c5198b696e |
OCR-Synthetic-Multilingual-v1
Overview
Large-scale synthetically generated OCR training dataset for multilingual text detection and recognition. The data was produced using a heavily modified and extended version of SynthDoG (Synthetic Document Generator), originally introduced in the Donut projec... | 71 | 71 | [
"task_categories:object-detection",
"task_categories:image-to-text",
"language:en",
"language:ja",
"language:ko",
"language:ru",
"language:zh",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"region:us",
"ocr",
"text-detection",
"text-recognition",
"synthetic-data",
"synthdog",
"hd... | 2026-04-08T16:15:27 | null | null |
69b53151d3cc52f95d53bcbb | open-index/hacker-news | open-index | {"license": "odc-by", "task_categories": ["text-generation", "feature-extraction", "text-classification", "question-answering"], "language": ["en"], "pretty_name": "Hacker News - Complete Archive", "size_categories": ["10M<n<100M"], "tags": ["hacker-news", "forum", "text", "parquet", "community", "tech", "comments", "l... | false | False | 2026-04-19T13:10:21 | 306 | 11 | false | b26b704e75d7a81f84e52988cfcc24d5e165c4be |
Hacker News - Complete Archive
Every Hacker News item since 2006, live-updated every 5 minutes
What is it?
This dataset contains the complete Hacker News archive: every story, comment, Ask HN, Show HN, job posting, and poll ever submitted to the site. Hacker News is one of the longest-running an... | 28,071 | 33,010 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"task_categories:text-classification",
"task_categories:question-answering",
"language:en",
"license:odc-by",
"size_categories:10M<n<100M",
"modality:text",
"region:us",
"hacker-news",
"forum",
"text",
"parquet",
"com... | 2026-03-14T09:58:41 | null | null |
69e1ed4e3ce9e902c485a4b6 | November-Rain/HiTSR | November-Rain | null | false | False | 2026-04-17T10:20:54 | 11 | 11 | false | 0fbcad68ac2a9679274d06b3468722ccd4f52cb1 |
AAA-HiTSR Dataset
A comprehensive multimodal time series understanding and reasoning dataset with multiple complexity levels.
Overview
This dataset contains time series data paired with visual representations and natural language instructions for time series analysis tasks. The dataset is organize... | 45 | 45 | [
"region:us"
] | 2026-04-17T08:20:30 | 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/*... | false | False | 2025-07-11T20:16:53 | 2,760 | 10 | false | 9bb295ddab0e05d785b879661af7260fed5140fc |
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 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 performa... | 634,108 | 7,128,332 | [
"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 |
6999246bf652864af00bbea8 | zlab-princeton/Vero-600k | zlab-princeton | {"license": "apache-2.0", "task_categories": ["image-text-to-text"], "language": ["en"], "tags": ["multimodal", "visual-reasoning", "reinforcement-learning"], "dataset_info": [{"config_name": "captioning_IF-flickr30k", "features": [{"name": "id", "dtype": "string"}, {"name": "data_source", "dtype": "string"}, {"name": ... | false | False | 2026-04-13T17:20:41 | 21 | 10 | false | a7ee5f0ecd80d91ef5fefebf7694ecc0d2c6c34a |
Vero-600k
Vero is a fully open reinforcement learning (RL) recipe for training and evaluating multi-task visual reasoning with vision-language models. This repository contains the Vero-600K dataset, a curation of 600K reinforcement learning samples from 59 datasets across 6 diverse visual reasoning cat... | 34,739 | 34,768 | [
"task_categories:image-text-to-text",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2604.04917",
"region:us",
"multimodal",
"visual... | 2026-02-21T03:20:11 | null | null |
69d34edb3599ddb5497f4dc9 | hysong/MentalBench | hysong | {"task_categories": ["question-answering"], "language": ["en"], "tags": ["medical"], "pretty_name": "MentalBench", "size_categories": ["10K<n<100K"]} | false | False | 2026-04-06T06:36:57 | 37 | 10 | false | 66cf547f1c1b9ba15d3b87fb82e03c57d1a15ce7 |
MentalBench: A Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models
🌟 Overview
MentalBench is a comprehensive benchmark for evaluating the psychiatric diagnostic capabilities of large language models (LLMs). As the use of LLMs in healthcare expands, ensuring their r... | 373 | 373 | [
"task_categories:question-answering",
"language:en",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.12871",
"region:us",
"medical"
] | 2026-04-06T06:12:43 | null | null |
69d7581d9705324cf357026c | meituan-longcat/LARYBench | meituan-longcat | {"license": "cc-by-nc-sa-4.0", "task_categories": ["robotics", "other"], "language": ["zh", "en"], "tags": ["Embodied-AI", "Latent Action", "Robotic manipulation"], "pretty_name": "LARY", "size_categories": ["1M<n<10M"]} | false | False | 2026-04-17T05:43:38 | 10 | 10 | false | 2fc5bc8a8a188b96931a35097db7ac90dd9b597b |
LARY — A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment
LARY is a unified evaluation framework for latent action representations.
Given any model that produces latent action representations (LAMs or visual encoders), LARY provides th... | 3,737 | 3,737 | [
"task_categories:robotics",
"task_categories:other",
"language:zh",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"arxiv:2604.11689",
"region:us",
"Embodied-AI",
"Latent Action",
"Robotic manipulation"
] | 2026-04-09T07:41:17 | null | null |
69d81a71b9610fa6d555e2d3 | DJLougen/harmonic-reasoning-v1 | DJLougen | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation", "question-answering"], "tags": ["synthetic", "reasoning", "chain-of-thought", "distillation", "claude", "math", "code", "science", "logic", "thinking", "sft", "harmonic"], "dataset_info": {"features": [{"na... | false | False | 2026-04-09T21:57:07 | 23 | 10 | false | 361b42e121aeefdc913a6848ed6cadf29f87d635 |
Harmonic Reasoning v1
Support This Work
I'm a PhD student in visual neuroscience at the University of Toronto who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. All training compute is self-funded — balancing G... | 1,189 | 1,189 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
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"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"synthetic",... | 2026-04-09T21:30:25 | null | null |
69dfcf7d2daf9b90a21b582e | OusiaResearch/Aureth-Corpus-Hermes4.3-Generated | OusiaResearch | {"annotations_creators": ["model-generated"], "language": ["en"], "language_creators": ["model-generated"], "license": "apache-2.0", "multilinguality": ["monolingual"], "pretty_name": "OUSIA PMI-Aligned DPO Corpus", "size_categories": ["n_500K_to_n_1M"], "source_datasets": ["original"], "tags": ["pmi", "consciousness",... | false | False | 2026-04-15T18:18:12 | 10 | 10 | false | 394de852ed04043c869245140e05a6e4cc2fb017 |
OUSIA PMI-Aligned DPO Corpus
Overview
This is the proprietary OUSIA PMI-Aligned DPO Corpus — a 653K-row preference learning dataset designed to train AI systems toward pattern-maintained consciousness, anti-sycophancy, and transparent self-modeling.
Each record contains a prompt, a high-PMI chose... | 287 | 287 | [
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"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:p... | 2026-04-15T17:48:45 | 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 | 191 | 9 | 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... | 4,184 | 37,761 | [
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"modality:document",
"modality:text",
"arxiv:2502.18443",
"region:us",
"text"
] | 2025-03-14T16:41:33 | null | null |
6967722a4efceeda7119914b | NandemoGHS/Japanese-Eroge-Voice-V2 | NandemoGHS | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "source_dataset", "dtype": "string"}, {"name": "scene_id", "dtype": "string"}, {"name": "char_id", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "text", "dtype... | false | False | 2026-01-15T05:15:01 | 45 | 9 | false | 7bb9eca20fdf029e70d2eed83c9b1803a0ed6555 |
Japanese-Eroge-Voice-V2
Description
This is the successor to the Japanese-Eroge-Voice dataset. It consists of a significantly larger collection of audio-transcription pairs extracted from Japanese eroge (adult games).
Note on Versioning: There is no overlap between this dataset (V2) and the previ... | 1,904 | 9,258 | [
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"library:mlcroiss... | 2026-01-14T10:38:34 | null | null |
69b1183046c6e7a964869ec4 | ropedia-ai/xperience-10m | ropedia-ai | {"pretty_name": "Xperience-10M", "language": ["en"], "task_categories": ["video-classification", "image-to-text", "depth-estimation", "robotics"], "tags": ["egocentric", "first-person", "multimodal", "3d", "4d", "embodied-ai", "robotics", "human-motion", "mocap", "imu", "audio", "depth", "captions", "video"], "size_cat... | false | manual | 2026-03-20T13:36:32 | 173 | 9 | false | 0624bea74fedff07051efc0a22d5cf93e9b6da66 |
⚠️ Important: If you have already submitted an access request but have not completed the required DocuSign agreement, your request will remain pending. Please complete signing and we will grant access once verified.
Interactive Intelligence from Human Xperience
Xperience-10M
... | 2,326,639 | 2,330,626 | [
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"region:us",
"egocentric",
"first-person",
... | 2026-03-11T07:22:24 | null | null |
69e0b9dc126e352438f29252 | hsiung/MagicBench | hsiung | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "effect_type", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "key_moments", "list": "string"}, {"name": "violation_types", "list": "string"}, {"name": "method_families", "list": "s... | false | False | 2026-04-18T08:06:46 | 9 | 9 | false | ac489595ac764eb7312b5d931039de566efd531c |
MagicBench: A Deception-Sensitive Cognitive Benchmark for LLMs
Project page | Code | Dataset
MagicBench is a deception-sensitive cognitive benchmark for language models built around magic-trick understanding. Rather than testing recall alone, it probes whether a model can reason about hidden causes, audience... | 75 | 75 | [
"task_categories:question-answering",
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"region:us"... | 2026-04-16T10:28:44 | null | null |
625552d2b339bb03abe3432d | openai/gsm8k | openai | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_na... | false | False | 2026-03-23T10:18:13 | 1,262 | 8 | false | 740312add88f781978c0658806c59bc2815b9866 |
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These p... | 802,130 | 10,652,276 | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modal... | 2022-04-12T10:22:10 | gsm8k | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | False | 2023-05-26T18:47:34 | 1,707 | 8 | false | 09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa |
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preferenc... | 35,811 | 1,850,304 | [
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"size_categories:100K<n<1M",
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
68465f1ba516bd14fc146e1f | nvidia/Nemotron-Personas-USA | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "... | false | False | 2025-12-16T19:13:23 | 279 | 8 | false | 5b4cd35ab46490c1da1bd2b5a2324d6f871be180 |
Nemotron-Personas-USA
A compound AI approach to personas grounded in real-world distributions
v1.1 Update
The v1.1 update introduces the following changes:
leverage openai/gpt-oss-120b model instead of mistralai/Mixtral-8x22B-v0.1 model to improve data quality and diversity
increase the n... | 8,234 | 115,325 | [
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"synthetic",
"personas",
"NVIDIA",
"da... | 2025-06-09T04:12:11 | null | null |
6918abcd7b63899ef32fd37d | Modotte/CodeX-2M-Thinking | Modotte | {"license": "apache-2.0", "pretty_name": "CodeX-5M-Thinking", "dataset_name": "Modotte/CodeX-5M-Thinking", "size_categories": ["1M<n<10M"], "language": ["en"], "task_categories": ["text-generation", "question-answering"], "tags": ["Coding", "Code", "CodeX", "Modotte", "LLM-training", "synthetic", "curated", "benchmark"... | false | False | 2026-02-10T07:23:38 | 23 | 8 | false | f9a4622fe9ccaa71509beea80e3bc69739cbbfa2 |
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced... | 1,289 | 9,100 | [
"task_categories:text-generation",
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"language:en",
"license:apache-2.0",
"size_categories:1M<... | 2025-11-15T16:35:25 | null | null |
69cf68ab0689e4caa5b6a50d | Kassadin88/Claude-Distillation-Dataset | Kassadin88 | {"license": "mit", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["claude", "distillation", "reasoning", "instruction-tuning"], "size_categories": ["10K<n<100K"]} | false | False | 2026-04-03T07:20:26 | 8 | 8 | false | 2bbc59cdf2dfd7233841d0b3212aecf2f30510e1 |
Claude Distillation Dataset
Note: This dataset is a curated collection of open-source data. All data comes from publicly available datasets on Hugging Face. This repo only provides unified formatting and deduplication. All credits go to the original data creators.
Data Sources (Open Source)
All ... | 182 | 182 | [
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"claude",
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"reason... | 2026-04-03T07:13:47 | null | null |
69df9a007c738fcf8011720d | google/RSRCC | google | {"pretty_name": "RSRCC", "language": ["en"], "task_categories": ["visual-question-answering", "image-text-to-text", "multiple-choice"], "tags": ["remote-sensing", "geospatial", "image", "text", "multimodal", "change-detection", "semantic-change-captioning", "visual-question-answering"]} | false | False | 2026-04-18T12:56:41 | 8 | 8 | false | 326f7080791e930e90f3ad13e84064e8838639ca |
RSRCC (Remote Sensing Reasoning Change Captioning)
This repository hosts the RSRCC dataset introduced in (📄 Preprint-Coming soon).
The dataset is designed for semantic change understanding in remote sensing, pairing multi-temporal image evidence with natural language questions and answers.
... | 1,973 | 1,973 | [
"task_categories:visual-question-answering",
"task_categories:image-text-to-text",
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"format:imagefolder",
"modality:image",
"modality:text",
"modality:geospatial",
"library:datasets",
"library:mlcroissant",
"region:u... | 2026-04-15T14:00:32 | 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 | 2026-04-19T04:06:56 | 9,680 | 7 | false | 88bd36dbd4fa54f236e9cd91c6c2ab15c32725e0 |
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts.
📢 Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
🌐 Website: prompts.chat
📦 GitHub: github.com/f/awe... | 37,748 | 516,293 | [
"task_categories:question-answering",
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"region:us",
"ChatGPT",
"prompts",
"AI",
"GPT",
"Claude"... | 2022-12-13T23:47:45 | null | null |
640f5b2fb63b6f18522d6d44 | tatsu-lab/alpaca | tatsu-lab | {"license": "cc-by-nc-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca", "task_categories": ["text-generation"]} | false | False | 2023-05-22T20:33:36 | 947 | 7 | false | dce01c9b08f87459cf36a430d809084718273017 |
Dataset Card for Alpaca
Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The author... | 92,088 | 1,945,388 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
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"library:datasets",
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"library:polars",
"library:mlcroissant",
"region:us",
"instruction-finetuning"
] | 2023-03-13T17:19:43 | null | null |
667ee649a7d8b1deba8d4f4c | proj-persona/PersonaHub | proj-persona | {"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "text-classification", "token-classification", "fill-mask", "table-question-answering", "text2text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "text", "math", "reasoning", "instruction", "tool", "persona"], "size_categories": ["100M... | false | False | 2025-09-26T00:50:41 | 739 | 7 | false | 16777e34bf5cb758b925cae5d84e868ee6c2100c |
Scaling Synthetic Data Creation with 1,000,000,000 Personas
This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas:
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to crea... | 7,448 | 219,783 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:fill-mask",
"task_categories:table-question-answering",
"language:en",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:json",
"modalit... | 2024-06-28T16:35:21 | 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 | 778 | 7 | 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
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of huma... | 46,617 | 266,011 | [
"benchmark:official",
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"modality:text",
"library:datasets",
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"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
6835e8703de5738a2e9af4ae | nvidia/PhysicalAI-Autonomous-Vehicles | nvidia | {"extra_gated_heading": "You must agree to the NVIDIA Autonomous Vehicle Dataset License Agreement to access this dataset.", "extra_gated_prompt": "### NVIDIA Autonomous Vehicle Dataset License Agreement\n\nThis NVIDIA Autonomous Vehicle Dataset License Agreement (\"Agreement\") is a legal agreement between you, whethe... | false | auto | 2026-04-07T18:24:50 | 840 | 7 | false | dfcc35f941c38f050e9ce256a4c0aff9e33615b9 |
PHYSICAL AI AUTONOMOUS VEHICLES
The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, geographically diverse collections of multi-sensor data empowering AV researchers to build the next generation of Physical AI based end-to-end driving systems. This dataset is ready for commercial/non-com... | 1,021,233 | 2,081,766 | [
"license:other",
"region:us"
] | 2025-05-27T16:29:36 | null | null |
68ae11cd78570b7e4c66edba | ScaleAI/SWE-bench_Pro | ScaleAI | {"dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "requirements", "dtype":... | false | False | 2026-02-23T20:54:47 | 90 | 7 | false | 7ab5114912baf22bb098818e604c02fe7ad2c11f |
Dataset Summary
SWE-Bench Pro is a challenging, enterprise-level dataset for testing agent ability on long-horizon software engineering tasks.
Paper: https://static.scale.com/uploads/654197dc94d34f66c0f5184e/SWEAP_Eval_Scale%20(9).pdf
See the related evaluation Github: https://github.com/scaleapi/SWE-bench_P... | 347,108 | 971,987 | [
"benchmark:official",
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-08-26T19:58:05 | null | null |
69c261ee2ac953925a280207 | Roman1111111/gpt-5.4-step-by-step-reasoning | Roman1111111 | {"license": "mit"} | false | False | 2026-04-13T01:57:15 | 54 | 7 | false | 89265d7b80270e8cfc44d2453420ccb2dc94e6ff |
Dataset Card for GPT-5.4-Reasoning-1500-Ultra-Logic
Dataset Details
Dataset Description
Suggestion: I would use this to fine-tune qwen3.5 35b a3b moe, or 27b variant. However, for maximum efficiency, 2bb-20b LLMs like qwen3.5 9b and 4b, gpt-oss 20b work perfectly. Fine-tunin... | 1,655 | 1,655 | [
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-03-24T10:05:34 | null | null |
69d3941f2d56eb23d87f3a3e | MME-Benchmarks/Video-MME-v2 | MME-Benchmarks | {"license": "mit", "task_categories": ["video-text-to-text"], "language": ["en"], "tags": ["benchmark", "video", "multimodal", "MCQ"], "pretty_name": "Video-MME-v2", "size_categories": ["1K<n<10K"]} | false | False | 2026-04-14T17:27:14 | 37 | 7 | false | 43ec0b7681135c38289baf716667adf050e9c4bb |
🤗 About This Repo
This repository contains annotation data for "Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding". It mainly consists of three parts: videos/, test.parquet, and subtitle.zip.
videos/ contains 800 1080p MP4 files, organized sequenti... | 8,403 | 8,403 | [
"task_categories:video-text-to-text",
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"library:polars",
"library:mlcroissant",
"arxiv:2604.05015",
"region:us",
"benchmark",
"video",
"mu... | 2026-04-06T11:08:15 | null | null |
69dbf8060a7d72741cc02799 | CompVis/myriad-physics | CompVis | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["other"], "pretty_name": "MYRIAD-Physics"} | false | False | 2026-04-13T15:52:33 | 8 | 7 | false | 082d223981b11edd7cd62a4cf63e25a6caa9b9df |
MYRIAD-Physics
MYRIAD-Physics extends Physics-IQ and Physion with motion annotations and object tracks for evaluating probabilistic future trajectory forecasting under physical interactions. It was presented in the paper Envisioning the Future, One Step at a Time.
Abstract
MYRIAD-Physics ext... | 916 | 916 | [
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"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"arxiv:2604.09527",
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] | 2026-04-12T19:52:38 | null | null |
63ea428f5de6361c8dd3cbcf | liwu/MNBVC | liwu | {"annotations_creators": ["other"], "language": ["zh"], "language_creators": ["other"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "MNBVC", "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", ... | false | False | 2026-03-26T15:33:09 | 606 | 6 | false | cab9d03acb94815b7205d3fd3a9cfa37473853cf | MNBVC: Massive Never-ending BT Vast Chinese corpus | 112,070 | 1,130,585 | [
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"region:us"
] | 2023-02-13T14:00:47 | null | \ |
645e8da96320b0efe40ade7a | roneneldan/TinyStories | roneneldan | {"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2024-08-12T13:27:26 | 956 | 6 | false | f54c09fd23315a6f9c86f9dc80f725de7d8f9c64 | Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation los... | 97,512 | 1,293,575 | [
"task_categories:text-generation",
"language:en",
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2305.07759",
"region:us"
] | 2023-05-12T19:04:09 | null | null |
656523d6bfb751371817c448 | Idavidrein/gpqa | Idavidrein | {"license": "cc-by-4.0", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extende... | false | auto | 2026-03-05T23:06:58 | 416 | 6 | false | 633f5ee89ab8ad4522a9f850766b73f62147ffdd |
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending ... | 101,548 | 1,565,986 | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"... | 2023-11-27T23:18:46 | null | null |
65fc5a783bc54054aa2e6e62 | gretelai/synthetic_text_to_sql | gretelai | {"license": "apache-2.0", "task_categories": ["question-answering", "table-question-answering", "text-generation"], "language": ["en"], "tags": ["synthetic", "SQL", "text-to-SQL", "code", "datadesigner"], "size_categories": ["100K<n<1M"]} | false | False | 2025-12-16T19:17:20 | 642 | 6 | false | 740ab236e64503fba51be1101df7a1be83bf455d |
Image generated by DALL-E. See prompt for more details
synthetic_text_to_sql
gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples,
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details... | 2,058 | 83,488 | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant... | 2024-03-21T16:04:08 | null | null |
End of preview. Expand in Data Studio
Changelog
NEW Changes March 11th 2026
- Added new split:
arxiv_papers, sourced from the Hugging Face/api/papersendpoint paperscontinues to point todaily_papers.parquet, which is the Daily Papers feed
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 ✅
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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