README.md

metadata

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. 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.