datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Code-Generation-Quality-Estimation
Code Generation Quality Estimation
This repository contains model-ready task context, generated code, and complete-case execution-resource targets for five public LLM code-generation cohorts. It provides deterministic 70/10/20 group-aware split versions using seeds 42, 1234, and 2026.
Configurations
There are 15 configurations: one for each dataset and split seed. Each configuration has train, validation, and test splits.
Dataset
Complete rows
Groups
Models… See the full description on the dataset page: https://huggingface.co/datasets/Samsoup/Code-Generation-Quality-Estimation.code-quality-corpus
CatQualia code-quality corpus — semantic smell classes with before/after fixes
39,383 rows · 25,541,792 bytes · JSON Lines, one object per line.
What this is
Real code smells paired with the fix: a smell_class that names the semantic problem (not just the syntax), the original lines, the corrected lines, the file and line it came from, and a rationale explaining why the original was wrong. Useful for code-review or repair training where the label has to say what… See the full description on the dataset page: https://huggingface.co/datasets/CatQualia/code-quality-corpus.code_qualitycode-quality-poor
Low Quality Code Dataset
Overview
This dataset contains 444 Python code samples with poor quality scores (62-74 out of 100).
These samples can be used for:
Training models to recognize bad code patterns
Contrastive learning (good vs bad code)
Code quality classification tasks
Statistics
Metric
Value
Total samples
444
Quality range
62-74
Average quality
~70
Source Distribution
Source
Count
BigOBench
425… See the full description on the dataset page: https://huggingface.co/datasets/happylife365/code-quality-poor.