datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
code_contests
Dataset Card for CodeContests
Dataset Summary
CodeContests is a competitive programming dataset for machine-learning. This
dataset was used when training AlphaCode.
It consists of programming problems, from a variety of sources:
Site
URL
Source
Aizu
https://judge.u-aizu.ac.jp
CodeNet
AtCoder
https://atcoder.jp
CodeNet
CodeChef
https://www.codechef.com
description2code
Codeforces
https://codeforces.com
description2code and Codeforces
HackerEarth… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/code_contests.UltraData-Code
UltraData-Code
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series |
📖 Tech Report (Coming Soon) |
🤗 UltraData-Code-L2 Classifier
English | 中文
📚 Introduction
UltraData-Code is a complete implementation of the UltraData L0-L4 tiered data management framework. It covers four code data states from L0 through L3, with each level corresponding to a distinct construction stage. The pipeline starts from approximately 192 million public GitHub… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-Code.codeforces
Dataset Card for CodeForces
Dataset description
CodeForces is one of the most popular websites among competitive programmers, hosting regular contests where participants must solve challenging algorithmic optimization problems. The challenging nature of these problems makes them an interesting dataset to improve and test models’ code reasoning capabilities. This dataset includes more than 10k unique problems covering the very first contests all the way to 2025.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces.codeparrot-clean
CodeParrot 🦜 Dataset Cleaned
What is it?
A dataset of Python files from Github. This is the deduplicated version of the codeparrot.
Processing
The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:
Deduplication
Remove exact matches
Filtering
Average line length < 100
Maximum line length < 1000
Alpha numeric characters fraction > 0.25
Remove auto-generated files (keyword search)
For… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/codeparrot-clean.CodeTraceBenchCodeTraceBench
A Benchmark for Agent Trajectory Diagnosis
CodeTraceBench is a large-scale benchmark of 4,316 agent trajectories with human-verified step-level annotations for evaluating trajectory diagnosis systems. Each trajectory records the full action-observation sequence of a coding agent, annotated with incorrect and unuseful step labels.
Part of the CodeTracer project — a self-evolving agent trajectory diagnosis system.
Dataset Overview… See the full description on the dataset page: https://huggingface.co/datasets/NJU-LINK/CodeTraceBench.codeparrot-clean-valid
CodeParrot 🦜 Dataset Cleaned (valid)
Train split of CodeParrot 🦜 Dataset Cleaned.
Dataset structure
DatasetDict({
train: Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 61373
})
})
Code-Contests-Plus
CodeContests+: A Competitive Programming Dataset with High-Quality Test Cases
Introduction
CodeContests+ is a competitive programming problem dataset built upon CodeContests. It includes 11,690 competitive programming problems, along with corresponding high-quality test cases, test case generators, test case validators, output checkers, and more than 13 million correct and incorrect solutions.
Highlights
High… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance-Seed/Code-Contests-Plus.opc-fineweb-code-corpus
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing
opc-fineweb-code-corpus: the code-related page recalled from fineweb <-- you are here
opc-fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-fineweb-code-corpus.codeforces-cots
Dataset Card for CodeForces-CoTs
Dataset description
CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests.
The dataset consists of several subsets:
solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots.swallow-code-v2
SwallowCode-v2
Resources
📑 arXiv: Read our paper for detailed methodology and results at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowMath-v2, our companion dataset for mathematical reasoning.
💻 What is it?
SwallowCode-v1 was a high-quality Python code dataset generated through an LLM-based rewriting pipeline.
However, it had two significant limitations:
(1) it was distributed under the Llama 3.3 Community License, and
(2) its size was limited to… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-code-v2.CodeGraph
CodeGraph
An open-taxonomy, Wikidata-grounded semantic knowledge graph over 145M source files.
CodeGraph annotates 144,910,008 source files from Stack-Edu, across 14 programming languages, along four orthogonal semantic axes — application domains, algorithms (with category and asymptotic complexity), programming paradigms, and design patterns — and grounds the resulting concept vocabulary in Wikidata. The result is a typed property graph of 158.2M nodes and 1.02B edges… See the full description on the dataset page: https://huggingface.co/datasets/halykoss/CodeGraph.claude-fable-5-claude-code
claude-fable-5 Agent Traces
It's worth noting that our team was working with Glint-Research to collect as much fable data as possible.
These are just the anonymized raw traces of both of our teams combined. This means that Glint-Research/Fable-5-traces was created from formatting and splitting up this same dataset. If you use one for your tune, don't use the other (it's the same exact data).
For training on this dataset I recommend using the teich package to convert to openai… See the full description on the dataset page: https://huggingface.co/datasets/armand0e/claude-fable-5-claude-code.code-alchemy
CodeAlchemy
CodeAlchemy is a synthetic code dataset (~976.6B tokens, ~162M rows) designed for training and evaluating code language models. It consists of 5 training subsets covering a range of code-related tasks, and 2 evaluation subsets. All files are Parquet with zstd compression with on-disk size ~873 GB. Raw source files are not included due to ownership considerations and must be manually fetched as instructed below.
Dataset Statistics
Config… See the full description on the dataset page: https://huggingface.co/datasets/open-alchemy/code-alchemy.codeforces-submissions
Dataset Card for CodeForces-Submissions
Dataset description
CodeForces is one of the most popular websites among competitive programmers, hosting regular contests where participants must solve challenging algorithmic optimization problems. The challenging nature of these problems makes them an interesting dataset to improve and test models’ code reasoning capabilities.
This dataset includes millions of real user (human) code submissions to the CodeForces website.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-submissions.style-dpo
gijl style dataset (multi-type)
Generated by meta-models/Muse-Glimmer-30B through a tool-using scouting loop over real sources (Stack Exchange, GitHub, OSV, Hacker News, arXiv, Wikipedia, web). Splits are a deterministic hash of the record id (90/5/5); derived records inherit their parent's split. Synthetic, model-written, not human-verified. Every rejected response is intentionally poor and must never be used as an example of good behavior.
config
folder
train
validation… See the full description on the dataset page: https://huggingface.co/datasets/codex-automatus/style-dpo.codeparrot-clean-train
CodeParrot 🦜 Dataset Cleaned (train)
Train split of CodeParrot 🦜 Dataset Cleaned.
Dataset structure
DatasetDict({
train: Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5300000
})
})
codeparrot-train-more-filter-3.3b-cleanedarxiv_cplusplus_research_code
Dataset card for ArtifactAI/arxiv_cplusplus_research_code
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_cplusplus_research_code
Dataset Summary
ArtifactAI/arxiv_python_research_code contains over 10.6GB of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
How to use it
from datasets import load_dataset
# full dataset (10.6GB of data)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_cplusplus_research_code.chroma_promptsA collection of prompts captioned using Gemma 2b captioning model. These prompts are intended to be used with FLUX Chroma model.
Download .parquet files to your Google Drive and
run them using the .ipynb notebook in this repo
mimo-claude-code-traces-1k
MIMO Claude Code Traces
MIMO Claude Code Traces is a collection of coding-agent trajectories in a Claude Code-style environment. Each record contains a user coding task, the full multi-turn message trace, available tool schemas, assistant reasoning fields, tool calls, tool outputs, and metadata such as model name, category, duration, cost, token usage, and whether the trace used tools.
The traces were generated with mimo-v2.5-pro, MiMo's most capable model at the time of… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/mimo-claude-code-traces-1k.LiteCoder-Terminal-RL-preview
LiteCoder-Terminal-RL-preview
Paper | Code | Blog Post
This dataset contains 602 standardized Harbor terminal environments and was released as part of the paper LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents.
Unlike static text-only instructions, these environments are fully executable and are designed to support the training of terminal-based agents.
Environment Generation Pipeline
The lack of high-quality, executable… See the full description on the dataset page: https://huggingface.co/datasets/Lite-Coder/LiteCoder-Terminal-RL-preview.codeparrot-train-more-filtering
CodeParrot 🦜 Dataset Cleaned and filtered (train)
Dataset Description
A dataset of Python files from Github. It is a more filtered version of the train split codeparrot-clean-train of codeparrot-clean. The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:
files with a mention of "test file" or "configuration file" or… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/codeparrot-train-more-filtering.GPT-5.5-CodexThis dataset was generated using teich by TeichAI
GPT-5.5 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 317
Model metadata: gpt-5.5
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GPT-5.5-Codex.code_contests_instruct
Dataset Card for "code_contests_instruct"
The deepmind/code_contests dataset formatted as markdown-instruct for text generation training.
There are several different configs. Look at them. Comments:
flesch_reading_ease is computed on the description col via textstat
hq means that python2 (aka PYTHON in language column) is dropped, and keeps only rows with flesch_reading_ease 75 or greater
min-cols drops all cols except language and text
possible values for language are {'CPP'… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/code_contests_instruct.tsla-historic-pricesKlear-CodeTestvoice-code-bench
VoiceCodeBench
VoiceCodeBench is a test-only benchmark for evaluating whether automatic
speech recognition (ASR) systems preserve exact structured values in English
workplace speech.
Paper: VoiceCodeBench: Evaluating Exact Structured-Token Recovery in Automatic Speech Recognition
The benchmark targets cases where a transcript is software input: callback
numbers, email addresses, command-line flags, file paths, URLs, account
identifiers, dates, measurements, and similar values… See the full description on the dataset page: https://huggingface.co/datasets/besimple-ai/voice-code-bench.RefineCode-code-corpus-metaThis dataset consists of meta information (including the repository name and file path) of the raw code data from RefineCode. You can collect those files referring to this metadata and reproduce RefineCode!
Note: Currently, we have uploaded the meta data covered by The Stack V2 (About 50% file volume). Due to complex legal considerations, we are unable to provide the complete source code currently. We are working hard to make the remaining part available.
RefineCode is a high-quality… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/RefineCode-code-corpus-meta.codeparrot-valid-near-deduplication
CodeParrot 🦜 Dataset after near deduplication (validation)
Dataset Description
A dataset of Python files from Github. We performed near deduplication of this dataset split codeparrot-clean-train from codeparrot-clean. Exact deduplication can miss a fair amount of nearly identical files. We used MinHash with a Jaccard threshold (default=0.85) to create duplicate clusters. Then these clusters are reduced to unique files based on the exact Jaccard similarity. Fore more… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/codeparrot-valid-near-deduplication.code_contestsHF-datasets version of Deepmind's code_contests dataset, notably used for AlphaGo. 1 row per solution, no test data or incorrect solutions included (only name/source/description/solution/language/difficulty)