Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,77 @@
+
+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - python
+  - code-search
+  - text-to-code
+  - code-to-text
+  - source-code
+
+---
+
+# Python CodeSearch Dataset (Shuu12121/python-treesitter-filtered-datasetsV2)
+
+## Dataset Description
+This dataset contains Python functions paired with their documentation strings (docstrings), extracted from open-source Python repositories on GitHub.
+It is formatted similarly to the CodeSearchNet challenge dataset.
+
+Each entry includes:
+- `code`: The source code of a python function or method.
+- `docstring`: The docstring or Javadoc associated with the function/method.
+- `func_name`: The name of the function/method.
+- `language`: The programming language (always "python").
+- `repo`: The GitHub repository from which the code was sourced (e.g., "owner/repo").
+- `path`: The file path within the repository where the function/method is located.
+- `url`: A direct URL to the function/method's source file on GitHub (approximated to master/main branch).
+- `license`: The SPDX identifier of the license governing the source repository (e.g., "MIT", "Apache-2.0").
+Additional metrics if available (from Lizard tool):
+- `ccn`: Cyclomatic Complexity Number.
+- `params`: Number of parameters of the function/method.
+- `nloc`: Non-commenting lines of code.
+- `token_count`: Number of tokens in the function/method.
+
+## Dataset Structure
+The dataset is divided into the following splits:
+
+- `train`: 1,083,527 examples
+- `validation`: 18,408 examples
+- `test`: 17,552 examples
+
+## Data Collection
+The data was collected by:
+1. Identifying popular and relevant Python repositories on GitHub.
+2. Cloning these repositories.
+3. Parsing Python files (`.py`) using tree-sitter to extract functions/methods and their docstrings/Javadoc.
+4. Filtering functions/methods based on code length and presence of a non-empty docstring/Javadoc.
+5. Using the `lizard` tool to calculate code metrics (CCN, NLOC, params).
+6. Storing the extracted data in JSONL format, including repository and license information.
+7. Splitting the data by repository to ensure no data leakage between train, validation, and test sets.
+
+## Intended Use
+This dataset can be used for tasks such as:
+- Training and evaluating models for code search (natural language to code).
+- Code summarization / docstring generation (code to natural language).
+- Studies on Python code practices and documentation habits.
+
+## Licensing
+The code examples within this dataset are sourced from repositories with permissive licenses (typically MIT, Apache-2.0, BSD).
+Each sample includes its original license information in the `license` field.
+The dataset compilation itself is provided under a permissive license (e.g., MIT or CC-BY-SA-4.0),
+but users should respect the original licenses of the underlying code.
+
+## Example Usage
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Shuu12121/python-treesitter-filtered-datasetsV2")
+
+# Access a split (e.g., train)
+train_data = dataset["train"]
+
+# Print the first example
+print(train_data[0])
+```