PyTorrent: A Python Library Corpus for Large-scale Language Models
This provides a curated resource for data scientists and students to accelerate code-related ML tasks, but it is incremental as it focuses on collecting existing packages rather than new methods.
The authors tackled the need for a large-scale, high-quality corpus for software engineering research by creating PyTorrent, a dataset of 218,814 Python package libraries from PyPI and Anaconda, which enables users to build machine learning models without extensive infrastructure effort.
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 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. The dataset, schema and a pretrained language model is available at: https://github.com/fla-sil/PyTorrent