README.md

---
language:
- 'da'
- 'no'
library_name: transformers
f1-score: 0.76
---
# Model Card for A&ttack2

Text classification model that determines whether a not a short text contains an attack.


# Model Description

The model is based on the [North-T5-NCC Large](https://huggingface.co/north/t5_large_NCC) (developed by Per E. Kummervold) which is a Scandinavian language built upon [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x). The model is further trained on ~70k Norwegian and ~67k Danish social media posts which have been classified as either 'attack' or 'not attack', making it a text-to-text model manipulated to do classification. The model is described in Danish in [this report](https://strapi.ogtal.dk/uploads/966f1ebcfa9942d3aef338e9920611f4.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:** [More information needed] 


# Direct Use
The model can be used directly to classify Danish and Norwegian social media posts (or similar pieces of text).


# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]

# Training Data
A collection of ~70k Norwegian and ~67k Danish social media posts have been manually annotated as 'attack' or 'not attack' by six individual coders. 5% of the posts have been annotated by more then one annotator, with the annotators in agreement for 83% of annotations.

*Hvad er data-split metoden? Hvad er training-validation-test split?*

# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics

### Testing Data
<!-- This should link to a Data Card if possible. -->

[More Information Needed]

### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

### Metrics

Macro-averaged f1-score: 0.76

[More Information Needed]

## Results

[More Information Needed]

### Summary




# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** Azure
- **Compute Region:** North-Europe
- **Carbon Emitted:** [More Information Needed]


# 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("Velbekomme", 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)