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upadted README with docs

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@@ -20,4 +20,81 @@ configs:
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  data_files:
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  - split: train
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  path: data/train-*
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  data_files:
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  - split: train
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  path: data/train-*
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+ license: mit
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+ task_categories:
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+ - text-generation
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+ language:
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+ - en
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+ tags:
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+ - code
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+ pretty_name: python docstring dataset
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+ size_categories:
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+ - 100K<n<1M
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  ---
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+ # Python Docstring Diff Dataset
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+
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+ This dataset contains training samples for models that generate Python documentation patches.
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+ Each example provides a Python source file with its docstrings removed and a corresponding unified diff patch that restores the documentation.
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+
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+ The dataset is designed for training or evaluating language models that assist with:
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+
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+ * Automatic code documentation
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+ * Docstring generation
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+ * Code review automation
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+ * Developer tooling
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+ * Dataset Structure
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+
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+ Each entry contains the following fields:
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+
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+ Field Description
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+ -------------------
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+ instruction| Task instruction given to the model
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+ code| Python source code with docstrings removed
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+ response| A unified diff patch that adds the correct docstrings
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+ file| Original file path from the source project
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+
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+ ## Task Format
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+
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+ The model receives a Python file missing its documentation and must produce a unified diff that adds appropriate docstrings.
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+
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+ Example input:
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+
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+ ```python
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+ def load_json(path):
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+ with open(path) as f:
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+ return json.load(f)
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+ ```
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+
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+ Example expected output:
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+ ```diff
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+ --- a/file.py
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+ +++ b/file.py
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+ @@
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+ def load_json(path):
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+ + """Load JSON data from a file path."""
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+ with open(path) as f:
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+ return json.load(f)
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+ ```
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+
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+ ## Data Sources
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+
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+ The dataset was generated by scanning Python packages within a virtual environment's site-packages directory.
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+ Docstrings were extracted from functions, classes, async functions, methods, and modules using Python's AST parser.
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+
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+ * Low-quality documentation was filtered out using heuristics such as:
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+ * Minimum docstring length
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+ * Removal of TODO or placeholder documentation
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+ * Deduplication of similar examples
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+
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+ ## Intended Use
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+
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+ This dataset is useful for training models that perform:
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+
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+ * automatic docstring generation
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+ * documentation patch creation
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+ * codebase documentation improvement tools
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+ * AI-assisted code review systems
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+
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+ ## License
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+
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+ This dataset is released under the MIT License.