This is a fine-tuned mBERT model specifically designed to detect hate speech in text in Galician (Spanish variety). The model is based on the bert-base-multilingual-cased architecture and has been fine-tuned on a custom dataset for the task of binary text classification, where the labels are no hate and hate.
1@misc{piot2025bridginggapshatespeech,
2 title={Bridging Gaps in Hate Speech Detection: Meta-Collections and Benchmarks for Low-Resource Iberian Languages},
3 author={Paloma Piot and José Ramom Pichel Campos and Javier Parapar},
4 year={2025},
5 eprint={2510.11167},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL},
8 url={https://arxiv.org/abs/2510.11167},
9}
The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).
1from transformers import pipeline
2
3# Load the model
4classifier = pipeline("text-classification", model="irlab-udc/MetaHate-mBERT-GL-es")
5
6# Test the model
7result = classifier("Your input text here")
8print(result) # Should print the labels "no hate" or "hate"
