readme: # German sentiment BERT finetuned on news data
  
  Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.
  
  This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sentiment development in German news articles about migration between 2007 and 2019.
  
  Code for inference (predicting sentiment polarity) on raw text can be found at https://github.com/text-analytics-20/news-sentiment-development/blob/main/sentiment_analysis/bert.py
  
  If you are not interested in polarity but just want to predict discrete class labels (0: positive, 1: negative, 2: neutral), you can also use the model with Oliver Guhr's `germansentiment` package as follows:
  
  First install the package from PyPI:
  
  ```bash
  pip install germansentiment
  ```
  
  Then you can use the model in Python:
  
  ```python
  from germansentiment import SentimentModel
  
  model = SentimentModel('mdraw/german-news-sentiment-bert')
  
  # Examples from our validation dataset
  texts = [
      '[...], schwärmt der parteilose Vizebürgermeister und Historiker Christian Matzka von der "tollen Helferszene".',
      'Flüchtlingsheim 11.05 Uhr: Massenschlägerei',
      'Rotterdam habe einen Migrantenanteil von mehr als 50 Prozent.',
  ]
  
  result = model.predict_sentiment(texts)
  
  print(result)
  ```
  
  The code above will print:
  
  ```python
  ['positive', 'negative', 'neutral']
  ```
  