Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2110.01710 v1 pith:UXAR66ON submitted 2021-10-04 cs.SE

classification cs.SE
keywords pytorrentcodelanguagemodelspythoncorpusdatagithub
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing RLHF with Human Gaze Modeling

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Using predicted human gaze to shape rewards lets RLHF converge 1.3 to 2 times faster with similar final policy quality.

Pith tools