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--- |
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widget: |
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- text: "Hold da op! Kan det virkelig passe?" |
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language: |
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- "da" |
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tags: |
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- sentiment |
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- emotion |
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- danish |
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--- |
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## BERT-model for danish multi-class classification of emotions |
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Classifies a danish sentence into one of 6 different emotions: |
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| Danish emotion | Ekman's emotion | |
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| ----- | ----- | |
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| 😞 **Foragt** | Disgust | |
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| 😨 **Frygt** | Fear | |
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| 😄 **Glæde** | Joy | |
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| 😱 **Overraskelse** | Surprise | |
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| 😢 **Tristhed** | Sadness | |
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| 😠 **Vrede** | Anger | |
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# How to use |
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```ruby |
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from transformers import pipeline |
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model_path = "NikolajMunch/danish-emotion-classification" |
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classifier = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path) |
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prediction = classifier("Jeg er godt nok ked af at mine SMS'er er slettet") |
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print(prediction) |
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# [{'label': 'Tristhed', 'score': 0.9725030660629272}] |
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``` |
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or |
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```ruby |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("NikolajMunch/danish-emotion-classification") |
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model = AutoModelForSequenceClassification.from_pretrained("NikolajMunch/danish-emotion-classification") |
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``` |
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# Model performance |
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**Accuracy** : 81.48 |
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**F1 Score** : 81.48 |
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*(tested on 3949 labelled sentences)* |
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# Training data |
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- TBA |
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(this page will be updated soon, with more information on the model and training data) |
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