datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
harbor-swesmith-rl-artifacts
Harbor SWE-Smith 强化学习数据产物
本数据集是 Harbor Qwen 工具调用代码智能体强化学习项目使用的冻结任务集，服务于 GRPO、原生价值模型/GAE PPO、训练过程诊断和统一协议评测。
项目已于 2026 年 8 月 30 日完成 P0 评测并进入阶段性归档。本数据集用于保留实验所依赖的数据切分、任务执行文件和审计信息，不代表新的通用代码能力基准。
数据概况
切分
任务数
训练集
187
验证集
42
测试集
38
合计
267
数据覆盖 89 个上游代码仓库。三个切分之间同时执行任务标识和仓库级隔离检查。
正式数据集名称：
swesmith-curated-grpo-267-v1
冻结切分的语义摘要：
ae5df9a3f4a3fc8af44fac420b36529e283839e1bd3de9daba65d5bcda51447d
该值来自 split-manifest.json 的 sha256 字段，用于标识切分语义，不等同于该文件本身的字节级… See the full description on the dataset page: https://huggingface.co/datasets/keryszhan/harbor-swesmith-rl-artifacts.tau2-uq-artifacts
tau2-bench UQ Artifacts
Interaction trajectories and token-level log-probability measurements from conversational customer service agent evaluations on tau2-bench, collected as part of the uncertainty quantification (UQ) pipeline. Used for analyses in the paper "Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities" under the agentuq codebase.
Dataset Overview
This dataset contains two types of artifacts:
Trajectories --… See the full description on the dataset page: https://huggingface.co/datasets/changdae/tau2-uq-artifacts.agent-code-rl-artifacts
Agent Code RL Artifacts
Recovered process data from a code-generation Agent project covering SFT,
Monte Carlo rollout, process reward modeling, and veRL GRPO. This repository
contains benchmark-derived records and AI-generated content; it is not a
human-authored-only dataset.
Related SFT adapter:
keryszhan/qwen2.5-coder-7b-code-plan-sft.
Data stages
Config
Purpose
Important boundary
splits
Canonical HumanEval/MBPP-derived task splits
grpo_evaluation is… See the full description on the dataset page: https://huggingface.co/datasets/keryszhan/agent-code-rl-artifacts.NPM-Artifact-Explanation-Benchmark
NPM-Artifact-Explanation-Benchmark
English
NPM-Artifact-Explanation-Benchmark is a cross-category multimodal corpus and benchmark resource for Chinese cultural artifact understanding and explanation.
This release contains 28,826 cleaned artifact records derived from National Palace Museum source records' opendata (https://digitalarchive.npm.gov.tw/opendata/). Each record includes structured artifact metadata, image URLs, source record URLs, and human-written… See the full description on the dataset page: https://huggingface.co/datasets/shunanhe/NPM-Artifact-Explanation-Benchmark.whowhen-regen-artifacts
Who&When Regeneration Artifacts (Thesis)
Training-free failure attribution via prefix-conditioned step regeneration (Ollama qwen2.5:14b, k=3).
Code: proposal-latex/experiments/ in the thesis Git repository.
Table B — Custom logs (primary contribution)
Path
Description
data/custom_logs/001.json … 030.json
Self-collected multi-agent failure traces (GPT-4o-mini collection)
ablation_outputs_custom/
Regeneration cache for all 30 cases… See the full description on the dataset page: https://huggingface.co/datasets/shreejan6/whowhen-regen-artifacts.best-of-attempts-summarization-artifacts
Artifacts for Testing Self-Correction in Generate-Critique-Refine Text Summarization
This repository contains artifact-safe research materials for an empirical study of best-of-attempts selection in a generate-critique-refine text summarization pipeline. The package is intended to make the reported paper results auditable: it includes evaluation metrics, prompt files, model/pipeline configuration summaries, paper drafts, provenance notes, and reviewer-facing completion evidence.… See the full description on the dataset page: https://huggingface.co/datasets/HugeTrunk/best-of-attempts-summarization-artifacts.Latin-OCR-Artifacts
Latin sentences sourced from The Latin Library, converted to images, were subsequently degraded via OCRODEG.
OCR (via Kraken and Tesseract) transcriptions were generated. The dataset was then augmented with several synthetic noise patterns in order to emulate the more severe corruption found in many older digitizations.
If you use this in your work, please cite:
@misc{mccarthy2025LACOROCR,
author = {McCarthy, A. M.},
title = {{Latin OCR Artifacts}},
year = {2025}… See the full description on the dataset page: https://huggingface.co/datasets/aimgo/Latin-OCR-Artifacts.