Instructions to use larskjeldgaard/senda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use larskjeldgaard/senda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="larskjeldgaard/senda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("larskjeldgaard/senda") model = AutoModelForSequenceClassification.from_pretrained("larskjeldgaard/senda") - Notebooks
- Google Colab
- Kaggle
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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