README.md

metadata

dataset_info:
  features:
    - name: code
      dtype: string
    - name: repo_path
      dtype: string
    - name: parsed_code
      dtype: string
    - name: quality_prob
      dtype: float64
    - name: learning_prob
      dtype: float64
  splits:
    - name: train
      num_bytes: 852705076967
      num_examples: 65509810
  download_size: 0
  dataset_size: 852705076967
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Card for "starcoder_labeled"

Starcoder data, with several popular languages selected, short sequences filtered out, then labeled based on learning quality (educational value) and code quality.

A good heuristic is to take anything with >.5 code quality and >.3 learning quality. But you may want to vary the thresholds by language, depending on your target task.