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---
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](https://huggingface.co/datasets/JetBrains/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](https://huggingface.co/codellama/CodeLlama-7b-hf) 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](https://huggingface.co/datasets/JetBrains/KStack) is performed using zero-shot quality estimation based on [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/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](https://huggingface.co/Salesforce/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> |