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.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.code_clippy_githubThe Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totalling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BiqQuery.CodeContests-O
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation
Overview
CodeContests-O is a high-quality competitive programming dataset with iteratively refined test cases, designed to provide reliable verification signals for training and evaluating reasoning-centric Large Language Models (LLMs). Built upon the CodeContests dataset, CodeContests-O employs a novel Feedback-Driven Iterative Framework to systematically synthesize, validate, and… See the full description on the dataset page: https://huggingface.co/datasets/caijanfeng/CodeContests-O.fluent_speech_commands_synth
Dataset Card for "fluent_speech_commands_synth"
More Information needed
CodecFake
CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems
Paper,
Code,
Project Page
Interspeech 2024
TL;DR: We show that better detection of deepfake speech from codec-based TTS systems can be achieved by training models on speech re-synthesized with neural audio codecs.
This dataset is released for this purpose.
See our paper and Github for more details on using our dataset.
Acknowledgement… See the full description on the dataset page: https://huggingface.co/datasets/rogertseng/CodecFake.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.code-clippy-tfrecordsCodeChat
CodeChat: Developer–LLM Conversations Dataset
Paper: https://arxiv.org/abs/2509.10402
GitHub: https://github.com/Software-Evolution-Analytics-Lab-SEAL/CodeChat
CodeChat is a large-scale dataset comprising 82,845 real-world developer–LLM conversations, containing 368,506 code snippets generated across more than 20 programming languages, derived from the WildChat (i.e., general Human-LLMs conversations dataset). The dataset enables empirical analysis of how developers… See the full description on the dataset page: https://huggingface.co/datasets/Suzhen/CodeChat.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)
code-comments-small
Comment Dataset
Opening comments extracted from code datasets with CommentMiner and ML4SE-toolkit.
Files are grouped as <dataset>/<language>/part-*.parquet.
The Hugging Face dataset card declares one config per source dataset and one split-safe language name per language.
Each row contains dataset, record_id, opening_comment, language, path, repo, extracted_at, and metadata.
For Parquet exports, metadata is stored as a JSON string so every source dataset shares one stable… See the full description on the dataset page: https://huggingface.co/datasets/Jkatzy/code-comments-small.codecpilot-compression-decision-dataset
CodecPilot Compression-Decision Dataset
用于同格式图片压缩参数预测的原图和编码候选实测结果。给定图像，在所选质量门槛达标的候选中选择体积较小的参数。
This dataset contains input images and measured same-format compression candidates. It contains no trained models and does not store every candidate's encoded output image.
当前正式标注：五个完整数据库
布局版本 v0.7，完整合并发布日期 2026-10-01。原标注与正式增量已按格式合并。
格式
图片文件数
候选组数／张
实测结果数
状态
PNG
8,832
42
370,944
完整
JPEG
18,742
48
899,616
完整
WebP
15,000
45
675,000
完整
JXL
15,000
47
705,000… See the full description on the dataset page: https://huggingface.co/datasets/winrisef/codecpilot-compression-decision-dataset.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 Quality Test… See the full description on the dataset page: https://huggingface.co/datasets/HexQuant/Code-Contests-Plus.code-code-CodeCompletion-TokenLevel-Python
Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru
CodeXGLUE -- Code Completion (token level)
Update 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.
Here is the introduction and pipeline for token level code completion task.… See the full description on the dataset page: https://huggingface.co/datasets/semeru/code-code-CodeCompletion-TokenLevel-Python.crema_d_synth
Dataset Card for "crema_d_synth"
More Information needed
maestro_synth
Dataset Card for "maestro_synth"
More Information needed
codecfake-audio
Codecfake Dataset
Overview
The Codecfake dataset is a large-scale dataset designed for the detection of Audio Language Model (ALM)-based deepfake audio. This dataset includes millions of audio samples across two languages and various test conditions, tailored specifically for ALM-based audio detection.
Conversion
The original dataset was downloaded from Zenodo and converted to FLAC format to maintain audio quality while reducing file size. The dataset has been… See the full description on the dataset page: https://huggingface.co/datasets/ajaykarthick/codecfake-audio.vocal_imitation_synth
Dataset Card for "vocal_imitation_synth"
More Information needed
opensinger_synthsmash-battlefield-fox-codec-20fps
Smash Battlefield Fox — codec-ready videos
Lossless 20 FPS preprocessing of DhruvBhatia0/smash-battlefield-fox at revision 2c94351b82c2c65a31fb39fe52a34ff905b6abcf. This dataset contains 512 complete replays selected deterministically with seed 28.
Every third decoded frame is resized to 252×208 with PyAV's training-time resize, then stored losslessly as RGB FFV1 in a streaming NUT container. Decoding the processed files reproduces the preprocessed RGB tensors bit-for-bit.… See the full description on the dataset page: https://huggingface.co/datasets/DhruvBhatia0/smash-battlefield-fox-codec-20fps.torgo_synthspeech_accent_archive_synthvoxceleb1_synthlibrispeech_asr_test_48k_synthagent-sft-stitch-zh-tts-taste-codec-chat-sample
Gemma 4 E2B Taste-S multi-turn codec SFT
This dataset contains 37,362 complete Traditional Chinese agent
dialogues selected from voidful/agent-sft-stitch-zh-tts. It covers
229,434 synthesized speech segments, approximately
520.5 hours of audio before codec extraction.
Every assistant speech segment is represented without Gemma native audio tags:
<SAY> text_token <a_code> <b_code> ... <p_code> ... </SAY>
The first assistant output starts immediately with <SAY>.
[SOPR]...[EOPR]… See the full description on the dataset page: https://huggingface.co/datasets/voidful/agent-sft-stitch-zh-tts-taste-codec-chat-sample.vocalset_synthvox_lingua_top10_synthlibrispeech_asr_test_synthsample_100CodeChat-V2.0
CodeChat: Developer–LLM Conversations Dataset
Paper: https://arxiv.org/abs/2509.10402
GitHub: https://github.com/Software-Evolution-Analytics-Lab-SEAL/CodeChat
CodeChat V2.0 is a large-scale dataset comprising 587,568 real-world developer–LLM conversations, derived from the WildChat dataset.
The V2.0 statistics below match Table I of the CASCON 2026 camera-ready paper, Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality.… See the full description on the dataset page: https://huggingface.co/datasets/Suzhen/CodeChat-V2.0.