A large scale collection of both semantic and natural language resources is essential to leverage active Software Engineering research areas such as code reuse and code comprehensibility. Existing machine learning models ingest data from Open Source repositories (like GitHub projects) and forum discussions(like Stackoverflow.com), whereas, in this showcase, we took a step backward to orchestrate a corpus titled PyTorrent that contains 218,814 Python package libraries for the first time and collected from PyPI and Anaconda environment. This is because earlier studies have shown that much of the code is redundant and Python packages from these environments are better in quality and are well-documented. PyTorrent enables users (such as data scientists, students, etc.)to build off the shelf machine learning models directly without spending months of effort on large infrastructure #Scenarios (datasets): Docs: Added Docstrings to the pairs of <NL,PL> UserComments: Added developer comments to the pairs of <NL,PL> Both: Added both Docstrings and developer comments to the pairs of <NL,PL> More detail can be found at PyTorrent GitHub Repository that includes metadata of each package, schema of metadata, dataset schema and how to use the dataset for training any machine-programming language based model. We also publish a pretrained model from PyTorrent and can be found at HuggingFace model Hub as PyTorrent-v1.

Interested in collaboration? please feel free to pull a request at Github: https://github.com/fla-sil/PyTorrent