  Software engineering (SE) activities have been revolutionized by the advent
of pre-trained models (PTMs), defined as large machine learning (ML) models
that can be fine-tuned to perform specific SE tasks. However, users with
limited expertise may need help to select the appropriate model for their
current task. To tackle the issue, the Hugging Face (HF) platform simplifies
the use of PTMs by collecting, storing, and curating several models.
Nevertheless, the platform currently lacks a comprehensive categorization of
PTMs designed specifically for SE, i.e., the existing tags are more suited to
generic ML categories.
  This paper introduces an approach to address this gap by enabling the
automatic classification of PTMs for SE tasks. First, we utilize a public dump
of HF to extract PTMs information, including model documentation and associated
tags. Then, we employ a semi-automated method to identify SE tasks and their
corresponding PTMs from existing literature. The approach involves creating an
initial mapping between HF tags and specific SE tasks, using a similarity-based
strategy to identify PTMs with relevant tags. The evaluation shows that model
cards are informative enough to classify PTMs considering the pipeline tag.
Moreover, we provide a mapping between SE tasks and stored PTMs by relying on
model names.
