| | --- |
| | license: apache-2.0 |
| | language: |
| | - nl |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | Fine-tuned model for detecting instances of abusive language in Ducth tweets. The model has been trained with [DALC v2.0 ](https://github.com/tommasoc80/DALC). |
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| | 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](https://aclanthology.org/2022.woah-1.5/)) |
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| | The model achieve the following results on multiple test data: |
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| | - 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 |
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| | More details on the training settings and pre-processind are available [here](https://github.com/tommasoc80/DALC) |
