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--- |
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language: |
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- no |
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- da |
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library_name: transformers |
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metrics: |
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- f1-score (Danish): 0.87 |
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- f1-score (Norwegian): 0.76 |
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--- |
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# Model Card for A&ttack2 |
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A text classification model for determining if a social media post in Danish or Norwegian contains a verbal attack. |
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# Model Description |
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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 was trained for increasing the understanding of what effect such training has on various languages. |
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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://strapi.ogtal.dk/uploads/966f1ebcfa9942d3aef338e9920611f4.pdf). |
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- **Developed by:** The development team at Analyse & Tal |
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- **Model type:** Language model restricted to classification |
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- **Language(s) (NLP):** Danish and Norwegian |
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- **License:** [More Information Needed] |
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- **Finetuned from model:** north/t5_large_scand (by Per E. Kummervold, not publicly available) |
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# Direct Use |
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This model can be used for classifying Danish and Norwegian social media posts or similar text. |
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# Training Data |
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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. |
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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. |
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# Evaluation Metrics |
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Macro-averaged f1-score for Danish data: 0.87 |
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Macro-averaged f1-score for Norwegian data: 0.76 |
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# Model Card Authors |
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This model card was written by the developer team at Analyse & Tal. Contact: oyvind@ogtal.dk. |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Download/load tokenizer and language model |
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tokenizer = AutoTokenizer.from_pretrained("ogtal/A-og-ttack2") |
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model = AutoModelForSeq2SeqLM.from_pretrained("ogtal/A-og-ttack2") |
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# Give sample text. The example is from a social media comment. |
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sample_text = "Velbekomme dit klamme usle løgnersvin!" |
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input_ids = tokenizer(sample_text, return_tensors="pt").input_ids |
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# Forward pass and print the output |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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Running the above code will print "angreb" (attack in Danish) |
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