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.CodeChat
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.fluent_speech_commands_synth
Dataset Card for "fluent_speech_commands_synth"
More Information needed
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-tfrecordsCodecFake
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.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.librispeech_synth
Dataset Card for "librispeech_synth"
More Information needed
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)
codecomplex
CodeComplex Dataset
Dataset Description
CodeComplex consists of 4,200 Java codes submitted to programming competitions by human programmers and their complexity labels annotated by a group of algorithm experts.
How to use it
You can load and iterate through the dataset with the following two lines of code:
from datasets import load_dataset
ds = load_dataset("codeparrot/codecomplex", split="train")
print(next(iter(ds)))
Data Structure… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/codecomplex.codecpilot-compression-decision-dataset
CodecPilot Compression-Decision Dataset
中文简介：这是用于训练和评估“图像压缩参数决策器”的多源数据集。模型的目标不是改变输入格式，而是在保持输入格式不变的前提下，根据图像内容和用户偏好（均衡压缩、极致压缩、尽量原图）预测合适的编码参数，减少反复试编码的成本。
The repository contains immutable image inputs, compression-decision labels, candidate records, manifests, integrity data, and dataset-preparation reports. Images are packaged without re-encoding so the original compressed bytes are preserved.
数据集组件 / Dataset components
The repository intentionally keeps two… See the full description on the dataset page: https://huggingface.co/datasets/winrisef/codecpilot-compression-decision-dataset.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.voxceleb1_synthvocal_imitation_synth
Dataset Card for "vocal_imitation_synth"
More Information needed
maestro_synth
Dataset Card for "maestro_synth"
More Information needed
crema_d_synth
Dataset Card for "crema_d_synth"
More Information needed
CodeChat-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_2 is a large-scale dataset comprising 587,568 real-world developer–LLM conversations, derived from the WildChat dataset.
Dataset Overview
Field
👉V1.0
V2.0
Records
82,845 conversations
587,568 conversations
Code
368,506 code snippets
2,252,399 code snippets
Languages
20+… See the full description on the dataset page: https://huggingface.co/datasets/Suzhen/CodeChat-V2.0.vocalset_synth
Dataset Card for "vocalset_synth"
More Information needed
librispeech_asr_test_48k_synthaudioset_unitvocalset_synthvox_lingua_top10_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.torgo_synthspeech_accent_archive_synthcode-code-translation-java-csharp
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/code-to-code-trans in Semeru
CodeXGLUE -- Code2Code Translation
Task Definition
Code translation aims to migrate legacy software from one programming language in a platform toanother.
In CodeXGLUE, given a piece of Java (C#) code, the task is to translate the code into C#… See the full description on the dataset page: https://huggingface.co/datasets/semeru/code-code-translation-java-csharp.opensinger_synth