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--- |
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task_categories: |
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- summarization |
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- text2text-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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size_categories: |
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- 10K<n<100K |
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--- |
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# Overview |
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This dataset contains Python code-docstring pairs, whereas the docstrings are in Google style. A Google style docstring is structured as follows: |
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``` |
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<Description of the code> |
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Args: |
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<var1> (<data-type>) : <description of var1> |
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<var2> (<data_type>) : <description of var2> |
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Returns: |
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<var3> (<data-type>) : <description of var3> |
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Raises: |
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<var4> (<data-type>) : <description of var4> |
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``` |
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The format varies widely (like additional sections such as Examples, Notes, etc) but generally speaking, it should contain an Args/Parameters and Returns section. |
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# Source |
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The dataset was gathered from 3 different sources: |
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## CodeSearchNet |
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From their Python split of ~250k samples, ~23k samples was extracted. A less than 10% sample retention, most samples from CodeSearchNet contained informal docstrings that |
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only contained descriptions and no sections. |
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## Repositories Under Google's GitHub Organization Page |
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You can find the specified page here [here](https://github.com/search?q=topic%3Apython+org%3Agoogle+fork%3Atrue&type=repositories). These repos are dictated by the list: |
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``` |
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repos = [ |
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"https://github.com/google/python-fire", |
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"https://github.com/google/yapf", |
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"https://github.com/google/pytype", |
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"https://github.com/google/tf-quant-finance", |
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"https://github.com/google/budoux", |
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"https://github.com/google/mobly", |
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"https://github.com/google/temporian", |
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"https://github.com/google/pyglove", |
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"https://github.com/google/subpar", |
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"https://github.com/google/weather-tools", |
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"https://github.com/google/ci_edit", |
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"https://github.com/google/etils", |
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"https://github.com/google/pcbdl", |
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"https://github.com/google/starthinker", |
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"https://github.com/google/pytruth", |
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"https://github.com/google/nsscache", |
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"https://github.com/google/megalista", |
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"https://github.com/google/fhir-py", |
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"https://github.com/google/chatbase-python", |
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"https://github.com/tensorflow/tensorflow", |
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"https://github.com/google/project-OCEAN", |
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"https://github.com/google/qhbm-library", |
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"https://github.com/google/data-quality-monitor", |
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"https://github.com/google/genai-processors", |
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"https://github.com/google/python-proto-converter", |
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"https://github.com/google/sprockets", |
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"https://github.com/keras-team/keras", |
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"https://github.com/scikit-learn/scikit-learn", |
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"https://github.com/apache/beam", |
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"https://github.com/huggingface/transformers" |
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] |
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``` |
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A total of ~11k samples was gathered from this source. |
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## Juraj's Python Google-style Docstrings Dataset |
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I found this dataset here and is made my user Juraj-juraj. You can find the dataset [here](https://huggingface.co/datasets/juraj-juraj/python_googlestyle_docstrings). |
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A total of ~25k samples was gathered from this source, after further preprocessing. |
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# Preprocessing Steps |
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The following cleaning, normalizing and preprocessing steps were performed: |
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1. Removed duplicates based on both code and docstring |
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2. Remove samples with empty code and docstrings |
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3. Remove samples with extremely short entries (<20 chars) |
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4. Remove samples with extremely long entries (>5000 chars) |
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5. Removed comments and docstring from the code |
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6. Removed samples where docstring isn't in English (using langdetect) |
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7. Removed samples where docstring contained special characters like html tags or URLS |
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8. Using CodeT5+ tokenizer, removed samples where docstring tokens are < 12 or > 256 |
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9. Normalized all docstring entries by removing any indentions |
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# Data Structure |
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The data structure of the dataset is as follows: |
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``` |
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<code> : <The code, removed of docstrings and comments>, |
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<docstring> : <The corresponding docstring of the code>, |
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<source> : <The source which the code came from> |
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``` |
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The source is any of the following: |
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**CodeSearchNet** - from the CodeSearchNet dataset <br> |
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**github-repos** - from the repositories under Google's Organization GitHub page <br> |
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**juraj-google-style** - from Juraj's Python Google-style docstring dataset |
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