# PyTorrent: A Python Library Corpus for Large-scale Language Models

arXiv: https://arxiv.org/abs/2110.01710 · HTML: https://mlsanity.com/paper/2110.01710/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2021.

## Built on by (most cited first, 9 of 9)

- [Summarization is (Almost) Dead](https://mlsanity.com/paper/2309.09558/) (2023)
- [One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code](https://mlsanity.com/paper/2205.06126/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion](https://mlsanity.com/paper/2212.09666/) (2022)
- [Knowledge Distillation for Large Language Models](https://mlsanity.com/paper/2603.13765/) (2026)
- [AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models](https://mlsanity.com/paper/2110.08512/) (2021)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2405.04126/) (2024)
- [Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation](https://mlsanity.com/paper/2602.11213/) (2026)

## Abstract (arXiv)

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