# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("larskjeldgaard/senda")
model = AutoModelForSequenceClassification.from_pretrained("larskjeldgaard/senda", device_map="auto")Quick Links
Danish BERT fine-tuned for Sentiment Analysis (Polarity)
This model detects polarity ('positive', 'neutral', 'negative') of danish texts.
It is trained and tested on Tweets annotated by Alexandra Institute.
Here is an example on how to load the model in PyTorch using the 🤗Transformers library:
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("larskjeldgaard/senda")
model = AutoModelForSequenceClassification.from_pretrained("larskjeldgaard/senda")
# create 'senda' sentiment analysis pipeline
senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
senda_pipeline("Sikke en dejlig dag det er i dag")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="larskjeldgaard/senda")