README.md

---
license: cc-by-4.0
task_categories:
  - text-generation
  - summarization
language:
  - code
tags:
  - code
  - documentation
  - docstring
  - code-to-text
  - python
  - java
  - javascript
  - typescript
  - cpp
size_categories:
  - 10K<n<100K
---

# Code2Doc: Function-Documentation Pairs Dataset

A curated dataset of **13,358** high-quality function-documentation pairs extracted from popular open-source repositories on GitHub. Designed for training models to generate documentation from code.

## Dataset Description

This dataset contains functions paired with their docstrings/documentation comments from 5 programming languages, extracted from well-maintained, highly-starred GitHub repositories.

### Languages Distribution

| Language | Train | Val | Test | Total |
|----------|-------|-----|------|-------|
| Java | 6,560 (61.4%) | 820 | 820 | 8,200 |
| Python | 2,885 (27.0%) | 360 | 362 | 3,607 |
| TypeScript | 681 (6.4%) | 85 | 86 | 852 |
| JavaScript | 428 (4.0%) | 53 | 55 | 536 |
| C++ | 130 (1.2%) | 16 | 17 | 163 |
| **Total** | **10,684** | **1,334** | **1,340** | **13,358** |

### Source Repositories

The data was extracted from high-quality open-source projects including:

**Python:** Django, PyTorch, Pandas, NumPy, scikit-learn, FastAPI, Flask, Celery, Airflow, Requests

**Java:** Guava, Elasticsearch, Spring Framework, Spring Boot, Apache Kafka, Commons-Lang

**TypeScript:** TypeScript, VS Code, Angular, Prisma, Grafana, Storybook, NestJS

**JavaScript:** React, Node.js, Lodash, Axios, Express

**C++:** OpenCV, Protobuf, Folly, gRPC, LLVM, TensorFlow

## Dataset Structure

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `function_name` | string | Name of the function/method |
| `function_code` | string | Complete source code of the function |
| `documentation` | string | Extracted docstring/documentation |
| `language` | string | Programming language |
| `file_path` | string | Original file path in repository |
| `line_number` | int | Line number where function starts |
| `parameters` | list[string] | List of parameter names |
| `return_type` | string | Return type annotation (if available) |
| `has_type_hints` | bool | Whether function has type annotations |
| `complexity` | int | Cyclomatic complexity score |
| `quality_score` | float | Documentation quality score (0-10) |
| `repo_name` | string | Source repository (owner/repo) |
| `repo_stars` | int | Repository star count at extraction time |
| `docstring_style` | string | Documentation style (google, numpy, sphinx, jsdoc, javadoc, doxygen) |
| `is_async` | bool | Whether function is async |

### Data Splits

- **Train:** 10,684 samples (80%)
- **Validation:** 1,334 samples (10%)
- **Test:** 1,340 samples (10%)

Splits are stratified by language to maintain consistent distribution across sets.

## Data Processing Pipeline

The dataset was created through a multi-stage pipeline:

1. **Extraction:** Used tree-sitter parsers to accurately extract functions with documentation
2. **Basic Filtering:** Removed test functions, trivial functions, and applied length constraints
3. **Quality Scoring:** Scored documentation completeness (parameters, returns, examples)
4. **Deduplication:** Removed exact and near-duplicates using MinHash LSH
5. **AI Detection:** Filtered potentially AI-generated documentation

### Quality Criteria

- Minimum documentation length: 20 characters
- Maximum documentation length: 10,000 characters
- Minimum code length: 50 characters
- Excluded test functions and trivial getters/setters
- Required meaningful documentation structure

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("kaanrkaraman/code2doc")

# Access splits
train_data = dataset["train"]
val_data = dataset["val"]
test_data = dataset["test"]

# Example: Get a Python function
python_samples = train_data.filter(lambda x: x["language"] == "python")
sample = python_samples[0]

print(f"Function: {sample['function_name']}")
print(f"Code:\n{sample['function_code']}")
print(f"Documentation:\n{sample['documentation']}")
```

### For Fine-tuning

```python
def format_for_training(example):
    return {
        "input": f"Generate documentation for the following {example['language']} function:\n\n{example['function_code']}",
        "output": example["documentation"]
    }

formatted_dataset = dataset.map(format_for_training)
```

## Intended Use

- **Training code documentation generation models**
- **Fine-tuning LLMs for code-to-text tasks**
- **Evaluating documentation quality metrics**
- **Research on code understanding and generation**

## Limitations

- Heavily weighted towards Java due to verbose documentation practices
- C++ representation is small due to different documentation conventions
- Documentation quality varies by repository coding standards
- Extracted from a specific snapshot in time (December 2025)

## Citation

```bibtex
@misc{recep_kaan_karaman_2025,
  author       = {Recep Kaan Karaman and Meftun Akarsu},
  title        = {code2doc (Revision cadd4e4)},
  year         = 2025,
  url          = {https://huggingface.co/datasets/kaanrkaraman/code2doc},
  doi          = {10.57967/hf/7310},
  publisher    = {Hugging Face}
}
```

## License

This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) License. The source code comes from repositories with permissive licenses (MIT, Apache 2.0, BSD).