setfit-italian-hate-speech
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
This model detects the hate speech for italian language:
- 1 --> is hate speech
- 0 --> isn't hate speech
Dataset
setfit-italian-hate-speech is trained on HaSpeeDe-FB dataset.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("nickprock/setfit-italian-hate-speech")
# Run inference
preds = model(["Lei è una brutta bugiarda!", "Mi piace la pizza"])
BibTeX entry and citation info
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
Dataset Citation
@inproceedings{VignaCDPT17,
title = {Hate Me, Hate Me Not: Hate Speech Detection on Facebook},
author = {Fabio Del Vigna and Andrea Cimino and Felice dell'Orletta and Marinella Petrocchi and Maurizio Tesconi},
year = {2017},
url = {http://ceur-ws.org/Vol-1816/paper-09.pdf},
researchr = {https://researchr.org/publication/VignaCDPT17},
cites = {0},
citedby = {0},
pages = {86-95},
booktitle = {Proceedings of the First Italian Conference on Cybersecurity (ITASEC17), Venice, Italy, January 17-20, 2017},
editor = {Alessandro Armando and Roberto Baldoni and Riccardo Focardi},
volume = {1816},
series = {CEUR Workshop Proceedings},
publisher = {CEUR-WS.org},
}