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 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 package to do this.

Contact

Feel free to contact author Lars Kjeldgaard on lars.kjeldgaard@eb.dk.

Shout-outs

Props to Malte Højmark-Berthelsen for pretraining Danish BERT and helping out adding a TensorFlow backend for senda.

pin/senda

Danish BERT fine-tuned for Sentiment Analysis with senda

Performance

Contact

Shout-outs

pin/
senda

Danish BERT fine-tuned for Sentiment Analysis with `senda`