README.md

---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- code
- ru
- en
license: other
multilinguality:
- multilingual
pretty_name: GitFlic Code Dataset
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
tags:
- code
- russian
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/*.parquet"
  default: true
dataset_info:
  features:
  - name: file_text
    dtype: string
  - name: language
    dtype: string
  - name: file_name
    dtype: string
---

# GitFlic Code Dataset

A comprehensive code dataset compiled from [GitFlic](https://gitflic.ru), Russia's first native service for storing and working with source code. This dataset is specifically designed to support training code models with strong Russian language understanding and authentic Russian coding practices.

---

## Overview

The GitFlic Code Dataset represents a significant code corpus from Russia's leading Git-based code hosting platform, capturing diverse projects across 690 programming languages. It serves as a valuable resource for developing multilingual code understanding models reflecting Russian development practices and standards.

### Key Statistics

| Metric | Value |
|--------|-------|
| Total Files | 5,975,978 |
| Total Repositories | 12,527 |
| Compressed Size | 6.44 GB (Parquet with Zstd) |
| Programming Languages | 690 |
| File Format | 6 Parquet files |

---

## Dataset Characteristics

### Scope and Coverage

This dataset captures code from over 12,500 repositories hosted on GitFlic, including:

- **Russian code community**: Extensive coverage of code written by Russian developers and enterprises, featuring Russian comments, documentation, and variable naming conventions
- **Diverse language ecosystem**: Support for 690 distinct programming languages
- **Developer and enterprise projects**: A comprehensive mix of individual developer projects and enterprise-grade codebases
- **Quality-assured**: Deduplicated and filtered to remove binary files and non-code content

### Programming Languages

The dataset encompasses 690 languages. The 30 most represented languages by file count are:

| Rank | Language | File Count |
|------|----------|------------|
| 1 | C | 739,012 |
| 2 | Java | 634,899 |
| 3 | C++ | 587,528 |
| 4 | JavaScript | 422,832 |
| 5 | PHP | 365,105 |
| 6 | XML | 291,920 |
| 7 | Markdown | 211,574 |
| 8 | Shell | 207,178 |
| 9 | Python | 206,443 |
| 10 | Unity3D Asset | 150,654 |
| 11 | SVG | 150,136 |
| 12 | TypeScript | 141,886 |
| 13 | Text | 139,406 |
| 14 | JSON | 126,214 |
| 15 | HTML | 122,341 |
| 16 | Go | 109,740 |
| 17 | YAML | 89,416 |
| 18 | Roff | 82,609 |
| 19 | C# | 77,520 |
| 20 | Makefile | 63,594 |
| 21 | LLVM | 55,680 |
| 22 | Scala | 53,395 |
| 23 | Unix Assembly | 49,909 |
| 24 | Rust | 35,553 |
| 25 | reStructuredText | 35,023 |
| 26 | Objective-C | 34,151 |
| 27 | Ruby | 33,366 |
| 28 | CMake | 33,030 |
| 29 | CSS | 31,664 |
| 30 | TSX | 31,397 |

---

## Dataset Structure

### Data Fields

Each record contains three fields providing content and metadata:

| Field | Type | Description |
|-------|------|-------------|
| `file_text` | string | Complete file content in UTF-8 encoding |
| `language` | string | Programming language identified using [github-linguist](https://github.com/github-linguist/linguist) |
| `file_name` | string | Unique file identifier within the dataset |

### Sample Record

```json
{
    "file_text": "package com.example.demo;\n\nimport org.springframework.boot.SpringApplication;\n...",
    "language": "Java",
    "file_name": "Application.java"
}
```

### File Format

- **Format**: Apache Parquet with Zstd compression
- **Structure**: 6 consolidated files (`gitflic-00000.parquet` to `gitflic-00005.parquet`)
- **Encoding**: UTF-8
- **Split**: All examples are included in a single training split (no validation or test splits)

---

## Data Creation Process

### Language Detection Methodology

Programming languages are identified using [github-linguist](https://github.com/github-linguist/linguist), GitHub's robust library for language detection. This ensures consistent and reliable classification across all files in the dataset.

### Source Data

All data originates from public repositories hosted on [GitFlic](https://gitflic.ru), Russia's native code hosting platform.

### Quality Filtering

The dataset has undergone systematic filtering to ensure quality and usability:

#### Deduplication
- Files have been deduplicated to ensure each code file appears only once in the dataset

#### Binary File Removal
- Binary files have been systematically excluded from the dataset
- Only text-based source code files are retained

#### Content Quality
- Files filtered to ensure data quality and remove non-code content
- UTF-8 encoding validation applied to all text files

---

## Usage Considerations

### Data Privacy and Security

The dataset may contain sensitive information that requires careful handling:

- **Email Addresses**: Present in code comments, documentation, or configuration files
- **Credentials**: Accidentally committed API keys or authentication tokens
- **Personal Information**: Names, phone numbers, and other identifiable data in comments or documentation

Users should implement appropriate filtering and anonymization when preparing data for model training.

### Licensing and Attribution

This dataset aggregates source code from repositories with diverse licenses. Any use of code or data derived from this dataset must comply with the original repository licenses, including attribution requirements where applicable.

Users are responsible for:
- Reviewing applicable license terms for code they utilize
- Providing proper attribution when required
- Ensuring compliance with license restrictions
- Respecting the rights of original authors and using the data responsibly

---

## Technical Details

**Source**: Public repositories hosted on [GitFlic](https://gitflic.ru)

**Annotations**: Machine-generated (language detection)

**Multilingual Support**: Includes multilingual code and documentation with emphasis on Russian content

**Task Categories**: Text generation, code modeling, language understanding

**Tags**: Code, Russian language, multilingual, open-source development

---