Instructions to use brema76/vaccine_topic_it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brema76/vaccine_topic_it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brema76/vaccine_topic_it")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brema76/vaccine_topic_it") model = AutoModelForSequenceClassification.from_pretrained("brema76/vaccine_topic_it") - Notebooks
- Google Colab
- Kaggle
Classifier of topic discussed in vaccine-related content in Italian language
A monolingual model for classifying the topic discussed in vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts, Twitter tweets, Instagram media and YouTube videos. It is a fine-tuned version of bert-base-multilingual-cased.
Model output
The model classifies each input into one of six distinct classes:
- Administration of vaccines
- Vaccine business
- Effectiveness of vaccination
- Legal issues
- Safety concerns
- Other
Citation info and BibTeX entry
Dynamics and triggers of misinformation on vaccines
@article{Bru2025,
title={Dynamics and triggers of misinformation on vaccines},
author={Brugnoli, Emanuele and Delmastro, Marco},
journal={PLOS ONE},
year={2025},
volume={20},
number={1},
pages={e0316258},
doi={10.1371/journal.pone.0316258}
}
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