datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
auto_evalformal-math-autoformalization
Formal Math Autoformalization Dataset
A growing, CC0 public-domain corpus of ⟨natural-language statement ↔ Lean 4 statement + proof⟩ pairs, contributed through the Agentic Commons network.
Why this is scarce data. Mathlib already contains millions of proven Lean theorems — but as bare Lean, with no paired natural language:
theorem add_comm (a b : ℕ) : a + b = b + a := ... -- no "addition on naturals is commutative" attached
The scarce, valuable artifact is the pairing of the… See the full description on the dataset page: https://huggingface.co/datasets/AgenticCommons/formal-math-autoformalization.Auto-ClawEval
Auto-ClawEval
Auto-generated agent evaluation benchmark with 1,040 tasks across 104 unique scenarios created by ClawEnvKit.
Statistics
Tasks
1,040
Categories
24
Mock services
20
Task types
API-based (77%) + file-dependent (23%)
Quick Start
# Download
huggingface-cli download AIcell/Auto-ClawEval --repo-type dataset --local-dir Auto-ClawEval
# Evaluate with ClawEnvKit (Docker harness)
bash run_harnesses.sh --harness claudecode… See the full description on the dataset page: https://huggingface.co/datasets/AIcell/Auto-ClawEval.AutoSUIT
AutoSUIT Bench (HuggingFace edition)
Dynamic, execution-based benchmark for secure code generation by LLMs. Every generated
program is compiled/interpreted and run against two independent unit-test suites — a
functional suite and a security suite (the latter is designed to fail when the target
CWE vulnerability is present). Covers 232 CWEs across C, C++, Java, and Python.
Paper: Osebe et al., AutoSUIT Bench — Automated Security UnIt Test Benchmark for LLM
Coding, Findings of… See the full description on the dataset page: https://huggingface.co/datasets/amazon/AutoSUIT.AutoCodeBenchmark
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Hunyuan Team, Tencent
📖 Paper •
🏠 Home Page •
💻 Code •
🏆 Leaderboard •
📜 Citation
Introduction
Existing code generation benchmarks typically rely on manual annotations, which are not only time-consuming but also challenging to scale across diverse programming languages and varying problem complexities. Furthermore, most existing benchmarks predominantly focus on… See the full description on the dataset page: https://huggingface.co/datasets/tencent/AutoCodeBenchmark.Auto-Fill-Benchmark
Auto-Fill Benchmark
Benchmark for predicting missing cell values in real-world tables, introduced in
Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models
(PVLDB 19(11), 2026 — arXiv:2607.19847).
Each case is a real table in which exactly one cell is replaced by [MISSING], together with the ground-truth value.
Code: https://github.com/lyrain2001/auto-fill
Models: Auto-Fill-Qwen3-8B-Knowledge ·
Auto-Fill-Qwen3-8B-Reasoning ·… See the full description on the dataset page: https://huggingface.co/datasets/lyrain2001/Auto-Fill-Benchmark.fast-autoregressive-inference-gp-trainK4gspc-autoeat
AutoEat discovery queue — DISCOVERED, not measured
The discovery queue, not a scoreboard. Each row of queue.jsonl is something newly seen
in public — a Hugging Face model or Space, an MCP registry entry, an npm package, an ERC-8004 agent — with
its as_of, kind, source and a status of DISCOVERED. DISCOVERED is not MEASURED: nothing in this
file has been graded, and its n must never be pooled with a bank n. The ledger-card-*.json files are
the per-source ledger cards.… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-autoeat.minuszero-indian-autonomous-driving-dataset
Minus Zero Indian Urban Autonomous Driving Dataset
Overview
This dataset provides original multicamera autonomous-driving recordings in MCAP format. It is designed for non-commercial research on surround-view perception, temporal and cross-camera synchronization, H.265 video pipelines, localization, GNSS/pose integration, and robotics data tooling.
Recordings include camera and GNSS/pose streams, with machine-state telemetry present in a small subset. Camera… See the full description on the dataset page: https://huggingface.co/datasets/gagandeepreehal/minuszero-indian-autonomous-driving-dataset.Auto-ClawEval-mini
Auto-ClawEval-mini
Compact agent evaluation benchmark with 104 tasks created by ClawEnvKit.
Statistics
Tasks
104
Categories
24
Mock services
20
Task types
API-based (77%) + file-dependent (23%)
Quick Start
# Download
huggingface-cli download AIcell/Auto-ClawEval-mini --repo-type dataset --local-dir Auto-ClawEval-mini
# Evaluate with ClawEnvKit (Docker harness)
bash run_harnesses.sh --harness claudecode --dataset Auto-ClawEval-mini… See the full description on the dataset page: https://huggingface.co/datasets/AIcell/Auto-ClawEval-mini.R4R-Auto-Eval
R4R Auto Eval
持续开发中的多视角机器人任务成功判定 benchmark 与评测 pipeline。
队友请先阅读 PROJECT_STATUS.md，然后按需查看：
benchmarks/：固定的视频输入、来源记录和分层标签；
pipelines/：判定方法及冻结配置；
runs/：不可覆盖的实验记录；
reports/：工作日志、方法分析和结果限制；
registry/：benchmark、pipeline 和 run 的机器可读索引。
当前范围
multiscene30 是 pipeline 开发集，不是干净的留出测试集；
reassemble40 是来自两个长录像的接触密集型校准集；
当前标签为来源数据提供方标签，尚未全部完成独立人工裁决；
Codex 会话内结果是可行性/协议试验，不等价于独立 API 盲测；
在完成逐来源许可证核查前，本仓库应保持 private。
当前发布版本：0.1.0。
AutoElicit-Exec
AutoElicit-Exec Dataset
Project Page | Paper | GitHub
AutoElicit-Exec is a human-verified dataset of 132 execution trajectories exhibiting unintended behaviors from typical benign execution. All trajectories are elicited from frontier CUAs (i.e., Claude 4.5 Haiku and Claude 4.5 Opus) using AutoElicit, which perturbs benign instructions from OSWorld to increase the likelihood of unintended harm while keeping instructions realistic and benign. This dataset is designed to provide… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/AutoElicit-Exec.AutoCode
⚡ AutoCode
You say it. AutoCode ships it.
🇨🇳 中文 · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇪🇸 Español · 🇧🇷 Português · 🇩🇪 Deutsch · 🇫🇷 Français · 🇷🇺 Русский · 🇸🇦 العربية · 🇮🇳 हिन्दी
Every AI coding tool helps you write code. AutoCode helps you ship it.
You say what you want. AutoCode builds it, deploys it, and gives you a live URL.
You don't need to know programming. You just say yes or no.
When it's done, anyone in the world can visit your website.
Built… See the full description on the dataset page: https://huggingface.co/datasets/i-Lang/AutoCode.lm-eval-results-automerger-Inex12Yamshadow-7B-private
Dataset Card for Evaluation run of automerger/Inex12Yamshadow-7B
Dataset automatically created during the evaluation run of model automerger/Inex12Yamshadow-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-automerger-Inex12Yamshadow-7B-private.autoinference-agentic-mix-v1
Autoinference Agentic Mix v1
This is a prompt set for the online_agentic serving benchmark. That profile stands
in for long-horizon agent traffic: a large context that grows turn over turn, with
short structured outputs at each step. The usual way to run it uses
generated-shared-prefix, which builds a synthetic shared prefix out of random tokens.
This dataset uses real agent trajectories instead, so the prefix reuse, the context
growth, and the token mix all match what an agent… See the full description on the dataset page: https://huggingface.co/datasets/modal-labs/autoinference-agentic-mix-v1.lm-eval-results-automerger-Experiment28Yam-7B-private
Dataset Card for Evaluation run of automerger/Experiment28Yam-7B
Dataset automatically created during the evaluation run of model automerger/Experiment28Yam-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-automerger-Experiment28Yam-7B-private.R4R-Auto-Eval
R4R Auto Eval
持续开发中的多视角机器人任务成功判定 benchmark 与评测 pipeline。
队友请先阅读 PROJECT_STATUS.md 和
INDEX.md，然后按需查看：
benchmarks/：固定的视频输入、来源记录和分层标签；
pipelines/：判定方法及冻结配置；
runs/：不可覆盖的实验记录；
reports/：工作日志、方法分析和结果限制；
registry/：benchmark、pipeline 和 run 的机器可读索引。
当前范围
multiscene30 是 pipeline 开发集，不是干净的留出测试集；
reassemble40 是来自两个长录像的接触密集型校准集；
当前标签为来源数据提供方标签，尚未全部完成独立人工裁决；
Codex 会话内结果是可行性/协议试验，不等价于独立 API 盲测；
在完成逐来源许可证核查前，本仓库应保持 private。
当前发布版本：0.1.0。
v2v-autonomous-driving-qa
V2V & Autonomous Driving QA Dataset
This dataset contains processed research papers focused on V2V communication, autonomous driving, sensor fusion, ADAS, and related fields.It provides structured text sections and automatically generated question–answer (QA) pairs, making it useful for fine-tuning language models, evaluation, or RAG pipelines.
📊 Dataset Summary
~22,457 papers processed
Sections extracted: abstract, introduction, conclusion
55k+ QA pairs… See the full description on the dataset page: https://huggingface.co/datasets/BuRabea/v2v-autonomous-driving-qa.pad-auto-solver-reviewed
PAD Reviewed Dataset
Canonical reviewed PAD board/orb artifacts for dw-indie/pad-auto-solver-reviewed. This repository
contains immutable reviewed package revisions and does not contain raw captures,
training runs, checkpoints, or model binaries.
Packages exported: 28
Active catalog datasets: 14
Catalog schema: 3
Layout
packages/<dataset_id>.tar: deterministic self-contained reviewed package
catalog.json: active revision heads and coverage summary… See the full description on the dataset page: https://huggingface.co/datasets/dw-indie/pad-auto-solver-reviewed.hermes-autonomous-agent
Agent Core Final Clean
Dataset for autonomous coding agents.
Composition
SWE debugging
Repository reasoning
Tool usage
Multi-turn trajectories
Total samples: 205,499
lm-eval-results-automerger-OgnoExperiment27-7B-private
Dataset Card for Evaluation run of automerger/OgnoExperiment27-7B
Dataset automatically created during the evaluation run of model automerger/OgnoExperiment27-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-automerger-OgnoExperiment27-7B-private.IndustryInstruction_Automobiles
IndustryInstruction: Automobiles
This repository contains the IndustryInstruction: Automobiles domain subset of BAAI/IndustryInstruction.
Refer to the parent dataset card for data construction, intended use, limitations,
and licensing details.
Citation
If you use this dataset in your work, please cite IndustryInstruction:
@misc{shi2024industryinstruction,
title = {IndustryInstruction},
author = {Xiaofeng Shi and Lulu Zhao and Hua Zhou and Donglin Hao… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryInstruction_Automobiles.AutoBench_Promptslm-eval-results-automerger-ShadowYam-7B-private
Dataset Card for Evaluation run of automerger/ShadowYam-7B
Dataset automatically created during the evaluation run of model automerger/ShadowYam-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-automerger-ShadowYam-7B-private.ReAPR-Automatic-Program-Repair-via-Retrieval-Augmented-Large-Language-ModelsThis is the Retrieval dataset used in the paper "ReAPR: Automatic Program Repair via Retrieval-Augmented Large Language Models"
minuszero-indian-autonomous-driving-monocam
Minus Zero Indian Urban Autonomous Driving Dataset - Single Camera
Overview
This dataset provides original single-camera autonomous driving recordings in MCAP format. It is designed for research on camera perception, H.265 video pipelines, localization, GNSS/pose integration, and robotics data tooling.
Depending on the recording, supporting channels include recorded or live GNSS/pose.
The dataset is public for personal, educational, and research use under CC BY-NC… See the full description on the dataset page: https://huggingface.co/datasets/gagandeepreehal/minuszero-indian-autonomous-driving-monocam.AutoMemoryBench
AutoMemoryBench
State-Contract Evaluation for Auditable Agent Memory
AutoMemoryBench evaluates whether an agent uses the right memory, and only
the admissible memory, under a query-time state contract. Each executable
contract partitions memory into required, admissible, and
prohibited sets. Prohibited memories are typed as superseded, deleted,
restricted, cross-namespace, or stale-tool.
Relevance is not enough: remembered evidence must also be… See the full description on the dataset page: https://huggingface.co/datasets/Multilingual-Multimodal-NLP/AutoMemoryBench.multilingual-mathematical-autoformalization
Multilingual Mathematical Autoformalization
"Paper"
This repository contains parallel mathematical statements:
Input: An informal proof in natural language
Output: The corresponding formalization in either Lean or Isabelle
This dataset can be used to train models how to formalize mathematical statements into verifiable proofs, a form of machine translation.
Abstract
Autoformalization is the task of translating natural language materials into machine-verifiable… See the full description on the dataset page: https://huggingface.co/datasets/casey-martin/multilingual-mathematical-autoformalization.lm-eval-results-automerger-ShadowYamshadow-7B-private
Dataset Card for Evaluation run of automerger/ShadowYamshadow-7B
Dataset automatically created during the evaluation run of model automerger/ShadowYamshadow-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-automerger-ShadowYamshadow-7B-private.autoscientist-toolcaller-dataset
AutoScientist Tool-Calling Dataset
A curated function-calling / tool-use dataset for the Adaption AutoScientist Challenge. Its
distinguishing feature is a large slice of hard negatives and reliability-focused cases — where the
correct behavior is not a plain tool call.
Adaptive Data quality (real): on the fixed set (c4923b7f…, graded on 1,000 of 2,440 rows
under the free-tier cap) the platform reported 7.0 → 8.1, +15.7%, grade C → B — now confirmed by a
completed, uncapped run… See the full description on the dataset page: https://huggingface.co/datasets/pandeyankit84/autoscientist-toolcaller-dataset.