README.md

metadata

license: other
dataset_info:
  features:
    - name: path
      dtype: string
    - name: owner
      dtype: string
    - name: repo_id
      dtype: int64
    - name: is_fork
      dtype: bool
    - name: languages_distribution
      dtype: string
    - name: content
      dtype: string
    - name: issues
      dtype: float64
    - name: main_language
      dtype: string
    - name: forks
      dtype: int64
    - name: stars
      dtype: int64
    - name: commit_sha
      dtype: string
    - name: size
      dtype: int64
    - name: name
      dtype: string
    - name: license
      dtype: string
  splits:
    - name: train
      num_bytes: 75063445
      num_examples: 25000
  download_size: 29298620
  dataset_size: 75063445
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Summary

The dataset contains 25,000 Kotlin code samples selected from the KStack dataset. The selection is performed based on the value of the code for learning algorithmic concepts in Kotlin. In total, the dataset contains about 23M CodeLlama-7b tokens (vocab size 32,016).

Column description

The dataset contains the following columns:

• size — size of the file in bytes
• content — text (content) of the file after removing personal identifiable information
• repo_id — GitHub ID of the repository
• path — path to a file
• owner — repo owner on GitHub
• name — repo name on GitHub
• commit_sha — hash of the commit, from which the revision of the file is taken
• stars — number of stars in the repo at the moment of collection
• forks — number of forks in the repo at the moment of collection
• issues — number of issues in the repo at the moment of collection
• is_fork — true if the repo is a fork or not as defined by GitHub
• main_language — main language of the repo as defined by GitHub
• languages_distribution — JSON with the distribution of languages by size in bytes in the repo
• license — permissive license of the repository

Dataset Collection

The filtering from KStack is performed using zero-shot quality estimation based on Mistral-7B-Instruct-v0.2. The model is prompted to determine which of two files has higher "educational value for learning algorithms in Kotlin". The results of the comparisons are averaged and used to train a binary classifier based on CodeT5p-220m. The binary classifier is then applied to the entire KStack to obtain scores for each sample in the dataset. The log-probability of the classifier prediction is used as a criterion of the selection.

Opt-out

If you want your data to be removed from dataset, or have any other questions, please reach out to Sergey Titov: sergey.titov@jetbrains.com