| --- |
| dataset_info: |
| features: |
| - name: code |
| dtype: string |
| - name: package |
| dtype: string |
| - name: path |
| dtype: string |
| - name: filename |
| dtype: string |
| - name: parsed_code |
| dtype: string |
| - name: quality_prob |
| dtype: float64 |
| - name: learning_prob |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 40005369487 |
| num_examples: 1902405 |
| download_size: 11174800633 |
| dataset_size: 40005369487 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| # Dataset Card for "pypi_labeled" |
| |
| All of the latest package versions from pypi. The original data came from [here](https://py-code.org/datasets). I pulled the latest versions of each package, then extracted only `md`, `rst`, `ipynb`, and `py` files. |
| |
| I then applied some cleaning: |
| |
| - rendering notebooks |
| - removing leading comments/licenses |
| |
| Then filtered out some low-quality code, and labeled the rest according to learning value and quality. Subset by those columns to get higher quality code. |
