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  # Danish BERT fine-tuned for Sentiment Analysis (Polarity)
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  This model detects polarity ('positive', 'neutral', 'negative') of danish texts.
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- It is trained on Tweets, that have been annotated by [Alexandra Institute](https://github.com/alexandrainst).
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  Here is an example on how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:
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  ```python
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- from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
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  tokenizer = AutoTokenizer.from_pretrained("larskjeldgaard/senda")
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- model = AutoModelForTokenClassification.from_pretrained("larskjeldgaard/senda")
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  # create 'senda' sentiment analysis pipeline
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  senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
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  # Danish BERT fine-tuned for Sentiment Analysis (Polarity)
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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).
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  Here is an example on how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:
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  ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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  tokenizer = AutoTokenizer.from_pretrained("larskjeldgaard/senda")
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+ model = AutoModelForSequenceClassification.from_pretrained("larskjeldgaard/senda")
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  # create 'senda' sentiment analysis pipeline
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  senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)