Fine-tuned model for detecting instances of abusive language in Ducth tweets. The model has been trained with DALC v2.0 .
Abusive language is defined as "Impolite, harsh, or hurtful language (that may contain profanities or vulgar language) that result in a debasement, harassment, threat, or aggression of an individual or a (social) group, but not necessarily of an entity, an institution, an organisations, or a concept." (Ruitenbeek et al., 2022)
The model achieve the following results on multiple test data:
- DALC held-out test set: macro F1: 72.23; F1 Abusive: 51.60
- HateCheck-NL (functional benchmark for hate speech): Accuracy: 60.19; Accuracy non-hateful tests: 57.38 ; Accuracy hateful tests: 59.58
- OP-NL (dynamyc benchmark for offensive language): macro F1: 57.57
More details on the training settings and pre-processind are available here
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