
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).

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/))

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](https://github.com/tommasoc80/DALC)
{"language": ["nl"], "license": "apache-2.0", "pipeline_tag": "text-classification"}
transformers pytorch safetensors bert text-classification nl license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
