This model is a fine-tuned version of
cardiffnlp/twitter-xlm-roberta-base using the
HaterNet dataset and the Spanish subset of
SemEval-2019 Task 5.
-
on the test split of SemEval-2019 Task 5
- F1 (weighted): 0.7866
- F1 (macro): 0.7935
- Accuracy: 0.7937
-
on custom test split of Haternet
- F1 (weighted): 0.7815
- F1 (macro): 0.6981
- Accuracy: 0.7933
-
on Haternet & SemEval-2019 Task 5
- F1 (weighted): 0.7908
- F1 (macro): 0.7657
- Accuracy: 0.7936
Install tweetnlp via pip.
Load the model in python.
1import tweetnlp
2model = tweetnlp.Classifier("cardiffnlp/twitter-xlm-roberta-base-hate-spanish")
3model.predict('Ismael es egocentrico porque se vuelve loca si le dicen que tiene el pelo bonito😂😂😂😂 eso se define con otro objetivo #FirstDates251')
4>> {'label': 'NOT-HATE'}
5
@inproceedings{basile-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
author = "Basile, Valerio and
Bosco, Cristina and
Fersini, Elisabetta and
Nozza, Debora and
Patti, Viviana and
Rangel Pardo, Francisco Manuel and
Rosso, Paolo and
Sanguinetti, Manuela",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "
https://aclanthology.org/S19-2007",
doi = "10.18653/v1/S19-2007",
pages = "54--63",
}
@article{quijano2019haternet,
title={HaterNet a system for detecting and analyzing hate speech in Twitter (Version 1.0)[Data set]},
author={Quijano-Sanchez, Lara and Kohatsu, Juan Carlos Pereira and Liberatore, Federico and Camacho-Collados, Miguel},
journal={Zenodo},
year={2019}
}
