| --- |
| language: da |
| tags: |
| - danish |
| - bert |
| - sentiment |
| - polarity |
| license: cc-by-4.0 |
| widget: |
| - text: "Sikke en dejlig dag det er i dag" |
| --- |
| # Danish BERT fine-tuned for Sentiment Analysis with `senda` |
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| This model detects polarity ('positive', 'neutral', 'negative') of Danish texts. |
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| It is trained and tested on Tweets 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 |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
| tokenizer = AutoTokenizer.from_pretrained("pin/senda") |
| model = AutoModelForSequenceClassification.from_pretrained("pin/senda") |
| |
| # create 'senda' sentiment analysis pipeline |
| senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
| |
| text = "Sikke en dejlig dag det er i dag" |
| # in English: 'what a lovely day' |
| senda_pipeline(text) |
| ``` |
|
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| ## Performance |
| The `senda` model achieves an accuracy of 0.77 and a macro-averaged F1-score of 0.73 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 |
| Feel free to contact author Lars Kjeldgaard on [lars.kjeldgaard@eb.dk](mailto:lars.kjeldgaard@eb.dk). |
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| #### Shout-outs |
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| Props to [Malte Højmark-Berthelsen](mailto:hjb@kmd.dk) for pretraining Danish BERT and helping out adding a TensorFlow backend for `senda`. |
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