README.md

---
language:
- no
- da
library_name: transformers
metrics:
- f1-score (Danish): 0.87
- f1-score (Norwegian): 0.76
---
# Model Card for A&ttack2

A text classification model for determining if a social media post in Danish or Norwegian contains a verbal attack.

# Model Description

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](https://www.ogtal.dk/assets/files/230403-Analyse-Tall-Angrep-hat-i-den-offentlige-debatten-paa-Facebook.pdf).


- **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)


# Direct Use
This model can be used for classifying Danish and Norwegian social media posts (or other texts) as either 'verbal attack' or 'nothing'.

# Training Data
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. 

# Evaluation Metrics

Macro-averaged f1-score for Danish data: 0.87
Macro-averaged f1-score for Norwegian data: 0.76


# Model Card Authors
This model card was written by the developer team at Analyse & Tal. Contact: oyvind@ogtal.dk.

# How to Get Started with the Model

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[0], skip_special_tokens=True))
```

Running the above code will print "angreb" (attack in Danish).