Datasets:
File size: 2,398 Bytes
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dataset_info:
features:
- name: instruction
dtype: string
- name: code
dtype: string
- name: response
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 247832934
num_examples: 16440
download_size: 86431840
dataset_size: 247832934
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: python docstring dataset
size_categories:
- 10K<n<100K
---
# Python Docstring Diff Dataset
This dataset contains training samples for models that generate Python documentation patches.
Each example provides a Python source file with its docstrings removed and a corresponding unified diff patch that restores the documentation.
The dataset is designed for training or evaluating language models that assist with:
* Automatic code documentation
* Docstring generation
* Code review automation
* Developer tooling
* Dataset Structure
Each entry contains the following fields:
Field Description
-------------------
instruction| Task instruction given to the model
code| Python source code with docstrings removed
response| A unified diff patch that adds the correct docstrings
file| Original file path from the source project
## Task Format
The model receives a Python file missing its documentation and must produce a unified diff that adds appropriate docstrings.
Example input:
```python
def load_json(path):
with open(path) as f:
return json.load(f)
```
Example expected output:
```diff
--- a/file.py
+++ b/file.py
@@
def load_json(path):
+ """Load JSON data from a file path."""
with open(path) as f:
return json.load(f)
```
## Data Sources
The dataset was generated by scanning Python packages in github.
Docstrings were extracted from functions, classes, async functions, methods, and modules using Python's AST parser.
Low-quality documentation was filtered out using heuristics such as:
* Minimum docstring length
* Removal of TODO or placeholder documentation
* Deduplication of similar examples
## Intended Use
This dataset is useful for training models that perform:
* automatic docstring generation
* documentation patch creation
* codebase documentation improvement tools
* AI-assisted code review systems
## License
This dataset is released under the MIT License. |