readme: ---
  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`
  
  This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
  
  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.
  
  Here is an example of how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:
  
  ```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)
  ```
  
  ## 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.
  
  #### Contact
  Feel free to contact author Lars Kjeldgaard on [lars.kjeldgaard@eb.dk](mailto:lars.kjeldgaard@eb.dk).
  
  #### Shout-outs
  
  Props to [Malte Højmark-Berthelsen](mailto:hjb@kmd.dk) for pretraining Danish BERT and helping out adding a TensorFlow backend for `senda`.
  