A text classification model for determining if a social media post in Danish or Norwegian contains a verbal attack.
The model is based on the north/t5_large_scand (by Per E. Kummervold, not publicly available) which is a Scandinavian language pretrained for 1.700.000 steps starting with the mT5 checkpoint on a Scandinavian corpus (Bokmål, Nynorsk, Danish, Swedish and Icelandic (+ a tiny bit Faeroyish)).
The model is finetuned for 20.000 steps in batches of 8. The data consists of ~70k Norwegian and ~67k Danish social media posts which have been classified as either 'verbal attack' or 'nothing', making it a text-to-text model restricted to do classification. The model is described in Danish in this report.
- Developed by: The development team at Analyse & Tal
- Model type: Language model restricted to classification
- Language(s) (NLP): Danish and Norwegian
- License: [More Information Needed]
- Finetuned from model: north/t5_large_scand (by Per E. Kummervold, not publicly available)
This model can be used for classifying Danish and Norwegian social media posts (or other texts) as either 'verbal attack' or 'nothing'.
A collection of ~70k Norwegian and ~67k Danish social media posts have been manually annotated as 'verbal attack' or 'nothing' by annotators. 5% of the posts have been annotated by more then one annotator, with the annotators in agreement for 83% of annotations.
Norwegian data are split in 70% training, 20% validation and 10% test. The Danish data are split in 70% training, 15% validation and 15% test.
Macro-averaged f1-score for Danish data: 0.87 Macro-averaged f1-score for Norwegian data: 0.76
This model card was written by the developer team at Analyse & Tal. Contact: firstname.lastname@example.org.
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Download/load tokenizer and language model tokenizer = AutoTokenizer.from_pretrained("ogtal/A-og-ttack2") model = AutoModelForSeq2SeqLM.from_pretrained("ogtal/A-og-ttack2") # Give sample text. The example is from a social media comment. sample_text = "Velbekomme dit klamme usle løgnersvin!" input_ids = tokenizer(sample_text, return_tensors="pt").input_ids # Forward pass and print the output outputs = model.generate(input_ids) print(tokenizer.decode(outputs, skip_special_tokens=True))
Running the above code will print "angreb" (attack in Danish).
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