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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: code |
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dtype: string |
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- name: label |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 3851220 |
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num_examples: 2493 |
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download_size: 1555926 |
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dataset_size: 3851220 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Python Linting Dataset |
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Python Static Analysis Dataset created by running pylint on stack-v2 and manually labelling other issues not found by pylint. |
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Data Augmentation is used on the manually labelled dataset using python's ast module to parse the code and modify name nodes. |
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## Scripts to download more data or modify this dataset further: |
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[Github Repository](https://github.com/ahmedhus22/llm4lint) |
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- use augmentation.py to augment your manually labelled data. |
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- run stackv2.py to download stackv2 python files with linenos < 200, then run genlabels.py to generate the lables from pylint. |
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- append augmented dataset to this to get the combined dataset for training. |
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- use train.py to fine-tune using unsloth. |
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## [Model Trained on this dataset](https://huggingface.co/ahmedhus22/llm4lint-7B-Qwen2.5Coder) |
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To use this Linter in CLI: |
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- run ollama create llm4lint7b -f <Modelfile-Path> |
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- `pip install llm4lint` |
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To use it as a vscode extension: |
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https://github.com/ahmedhus22/llm4lint-vsc-extension |
