license: cc-by-sa-4.0
dataset_info:
features:
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dtype: string
- name: max_stars_owner
dtype: string
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dtype: string
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- name: max_issues_repo_id
dtype: float64
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dtype: int64
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dtype: string
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dtype: string
- name: min_stars_name
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dtype: string
- name: max_forks_owner
dtype: string
- name: min_issues_path
dtype: string
- name: min_len
dtype: 'null'
- name: max_forks_languages_distribution
dtype: string
- name: max_issues
dtype: float64
- name: min_forks_main_language
dtype: string
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- name: min_stars_path
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- name: min_forks_languages_distribution
dtype: string
- name: name
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splits:
- name: train
num_bytes: 174601557.889927
num_examples: 25000
download_size: 69469316
dataset_size: 174601557.889927
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Dataset Summary
The dataset contains 25000 Kotlin code samples selected from 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 32016).
Dataset Collection Procedure
The filtering 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 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