PyTorrent: A Python Library Corpus for Large-scale Language Models
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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
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Oct 2021.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
8 papers.
- 2023Summarization is (Almost) Deadcited by 43
- 2022One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Codecited by 26
- 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
- 2022MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completioncited by 6
- 2026Knowledge Distillation for Large Language Modelscited by 5
- 2021AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Modelscited by 3
- 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3
- 2024Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuningcited by 1
- 2026Knowledge Distillation for Large Language Models14 Mar 2026
- 2024Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning7 May 2024
- 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit30 Dec 2023
- 2023Summarization is (Almost) Dead18 Sep 2023
- 2022MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion19 Dec 2022
- 2022One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code12 May 2022
- 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective10 Feb 2022
- 2021AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models16 Oct 2021