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--- |
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language: da |
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tags: |
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- danish |
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- bert |
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- sentiment |
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- analytical |
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license: cc-by-4.0 |
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widget: |
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- text: "Jeg synes, det er en elendig film" |
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--- |
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# Danish BERT fine-tuned for Detecting 'Analytical' |
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This model detects if a Danish text is 'subjective' or 'objective'. |
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It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/senda) package. |
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Here is an example of how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("pin/analytical") |
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model = AutoModelForSequenceClassification.from_pretrained("pin/analytical") |
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# create 'senda' sentiment analysis pipeline |
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analytical_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
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text = "Jeg synes, det er en elendig film" |
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# in English: 'I think, it is a terrible movie' |
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analytical_pipeline(text) |
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``` |
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## Performance |
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The `senda` model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that [Alexandra Institute](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#twitter-sentiment) provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the [`senda`](https://github.com/ebanalyse/senda) package to do this. |
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#### Contact |
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Feel free to contact author Lars Kjeldgaard on [[email protected]](mailto:[email protected]). |
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