AadiBhatia/code-edit-quality

--- configs: - config_name: clean data_files: - split: train path: clean/train.parquet - config_name: dirty data_files: - split: train path: dirty/train.parquet license: apache-2.0 task_categories: - text-generation tags: - code-editing - quality-filtering - sft - sharegpt size_categories: - 10K<n<100K --- # Code Editing Quality — SFT-Ready (ShareGPT Format) Quality-filtered splits of a 50K code-editing SFT dataset in **ShareGPT conversation format**, produced by LLM-based distillation that evaluates 9 quality criteria per sample. ## Format Each sample has a `conversations` field with ShareGPT-style turns: - **system**: Code editing system prompt - **human**: Instruction + source code - **gpt**: Edited code Compatible with [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), and other SFT frameworks that support ShareGPT format. ## Splits | Split | Samples | Description | |---|---|---| | `clean` | 21,774 | Samples with **zero** antipatterns across all 9 criteria | | `dirty` | 27,773 | Samples with **at least one** antipattern detected | ## Usage ```python from datasets import load_dataset clean = load_dataset("AadiBhatia/code-edit-quality", "clean", split="train") dirty = load_dataset("AadiBhatia/code-edit-quality", "dirty", split="train") # Each sample: # clean[0]["conversations"] -> [{system}, {human}, {gpt}] ```