Related papers: Generate with CodeXHug: A Dataset to Enhance Model Cards with Code Usage Patterns

Generate with CodeXHug: A Dataset to Enhance Model Cards with Code Usage Patterns

URL: http://arxiv.org/abs/2606.23329v1
Date: Mon, 22 Jun 2026 13:39:25 GMT
Title: Generate with CodeXHug: A Dataset to Enhance Model Cards with Code Usage Patterns
Abstract summary: Pre-trained models (PTMs) are becoming increasingly popular in the software engineering community.<n>Many of them are used in toy projects or simply as a mirror for the HF repository.<n>In this paper, we present CodeXHug, a curated dataset of HuggingFace PTMs exploited in the Github ecosystem.
Score: 4.036517187104569
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Pre-trained models (PTMs) are becoming increasingly popular in the software engineering community. Their usage is facilitated by model repositories, e.g., HuggingFace, which collect, store, and maintain a wide range of PTMs. However, the actual adoption of these models in real-world projects is still an open question, i.e., many of them are used in toy projects or simply as a mirror for the HF repository. In addition, most of the available model cards and textual documents that contain critical information about their usage do not include explanatory code patterns, thus increasing the difficulty for newcomers. Thus, we see the need for a curated codebase related to PTMs to support developers and practitioners who are interested in using them in their projects. In this paper, we present CodeXHug, a curated dataset of HuggingFace PTMs exploited in the Github ecosystem and the related code usage patterns. Starting from the latest HF dump, we first conduct a data curation to collect PTMs with a tag and a model card. Then, the Github platform has been queried to find actual usages of the identified PTMs, resulting in 7,325 different models and 20,545 Python files. To demonstrate a concrete application of CodeXHug, we propose a usage scenario focused on extracting representative code usage patterns for specific PTMs through a statistical analysis and clustering techniques applied to relevant code snippets.

Related papers

Figma2Code: Automating Multimodal Design to Code in the Wild [85.29510079067464]
We introduce Figma2Code, a new task that advances design-to-code into a multimodal setting.<n>We collect paired design images and their corresponding metadata files from the Figma community.<n>This process yields 3,055 samples, from which designers curate a balanced dataset of 213 high-quality cases.
arXiv Detail & Related papers (2026-04-15T09:17:09Z)
Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects [9.22889135297242]
Pre-trained models (PTMs) are machine learning models that have been trained in advance, often on large-scale data, and can be reused for new tasks.<n>Software Dependencies 2.0 introduces a new class of software dependency, which we term Software Dependencies 2.0.
arXiv Detail & Related papers (2025-09-07T15:00:22Z)
How do Pre-Trained Models Support Software Engineering? An Empirical Study in Hugging Face [52.257764273141184]
Open-Source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks.<n>These resources lack a classification tailored to Software Engineering (SE) needs.<n>We derive a taxonomy encompassing 147 SE tasks and apply an SE-oriented classification to PTMs in a popular open-source ML repository, Hugging Face (HF)<n>We find that code generation is the most common SE task among PTMs, while requirements engineering and software design activities receive limited attention.
arXiv Detail & Related papers (2025-06-03T15:51:17Z)
SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving [90.32201622392137]
We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs)<n>Unlike traditional static benchmarks, SwingArena models the collaborative process of software by pairing LLMs as iterations, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines.
arXiv Detail & Related papers (2025-05-29T18:28:02Z)
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering [52.257764273141184]
Open-source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks. These resources lack a classification tailored to Software Engineering (SE) needs. We apply an SE-oriented classification to PTMs and datasets on a popular open-source ML repository, Hugging Face (HF), and analyze the evolution of PTMs over time.
arXiv Detail & Related papers (2024-11-14T18:52:05Z)
Automated categorization of pre-trained models for software engineering: A case study with a Hugging Face dataset [9.218130273952383]
Software engineering activities have been revolutionized by the advent of pre-trained models (PTMs) The Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. This paper introduces an approach to enable the automatic classification of PTMs for SE tasks.
arXiv Detail & Related papers (2024-05-21T20:26:17Z)
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) [57.30228361181045]
This survey connects key advancements in recommender systems using Generative Models (Gen-RecSys) It covers: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges.
arXiv Detail & Related papers (2024-03-31T06:57:57Z)
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software [6.243303627949341]
This paper presents the PeaTMOSS dataset, which comprises metadata for 281,638 PTMs and detailed snapshots for all PTMs. The dataset includes 44,337 mappings from 15,129 downstream GitHub repositories to the 2,530 PTMs they use. Our analysis provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation.
arXiv Detail & Related papers (2024-02-01T15:55:50Z)
Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code [24.936022005837415]
We review the recent advancements in software engineering with language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 related works. We break down code processing models into general language models represented by the GPT family and specialized models that are specifically pretrained on code. We also go beyond programming and review LLMs' application in other software engineering activities including requirement engineering, testing, deployment, and operations.
arXiv Detail & Related papers (2023-11-14T08:34:26Z)
"I see models being a whole other thing": An Empirical Study of Pre-Trained Model Naming Conventions and A Tool for Enhancing Naming Consistency [4.956536094440504]
We conduct the first empirical investigation of PTM naming practices in the Hugging Face PTM registry.<n>We introduce DARA, the first automated DNN ARchitecture Assessment technique designed to detect PTM naming inconsistencies.
arXiv Detail & Related papers (2023-10-02T21:13:32Z)
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse [59.500060790983994]
This paper introduces ZhiJian, a comprehensive and user-friendly toolbox for model reuse, utilizing the PyTorch backend. ZhiJian presents a novel paradigm that unifies diverse perspectives on model reuse, encompassing target architecture construction with PTM, tuning target model with PTM, and PTM-based inference.
arXiv Detail & Related papers (2023-08-17T19:12:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.