license:mit size_categories:1K<n<10K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
dataset_info:
  features:
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 882141
    num_examples: 1045
  download_size: 340574
  dataset_size: 882141
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Python Linting Dataset
Python Static Analysis Dataset created by running pylint on stack-v2 and manually labelling other issues not found by pylint.
Data Augmentation is used on the manually labelled dataset using python's ast module to parse the code and modify name nodes.
## Scripts to download more data or modify this dataset further:
[Github Repository](https://github.com/ahmedhus22/llm4lint)
- use augmentation.py to augment your manually labelled data.
- run stackv2.py to download stackv2 python files with linenos < 200, then run genlabels.py to generate the lables from pylint.
- append augmented dataset to this to get the combined dataset for training.
- use train.py to fine-tune using unsloth.
## [Model Trained on this dataset](https://huggingface.co/ahmedhus22/llm4lint-7B-Qwen2.5Coder)
To use this Linter in CLI:
- run ollama create llm4lint7b -f <Modelfile-Path>
- `pip install llm4lint`
To use it as a vscode extension:
https://github.com/ahmedhus22/llm4lint-vsc-extension