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README.md

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 ---
-dataset_info:
-  features:
-  - name: function_name
-    dtype: string
-  - name: function_code
-    dtype: string
-  - name: documentation
-    dtype: string
-  - name: language
-    dtype: string
-  - name: file_path
-    dtype: string
-  - name: line_number
-    dtype: int32
-  - name: parameters
-    list: string
-  - name: return_type
-    dtype: string
-  - name: has_type_hints
-    dtype: bool
-  - name: complexity
-    dtype: int32
-  - name: quality_score
-    dtype: float32
-  - name: repo_name
-    dtype: string
-  - name: repo_stars
-    dtype: int32
-  - name: docstring_style
-    dtype: string
-  - name: is_async
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 14568245
-    num_examples: 10684
-  - name: val
-    num_bytes: 1852301
-    num_examples: 1334
-  - name: test
-    num_bytes: 1867302
-    num_examples: 1340
-  download_size: 8227994
-  dataset_size: 18287848
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+  - text-generation
+  - text2text-generation
+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 2024)
+
+## Citation
+
+```bibtex
+@dataset{code2doc2024,
+  title={Code2Doc: Function-Documentation Pairs Dataset},
+  author={Kaan R. Karaman},
+  year={2024},
+  url={https://huggingface.co/datasets/kaanrkaraman/code2doc}
+}
+```
+
+## License
+
+This dataset is released under the MIT License. The source code comes from repositories with permissive licenses (MIT, Apache 2.0, BSD).