Add more info to readme

README.md

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 # German sentiment BERT finetuned on news data
-    
-Based on https://huggingface.co/oliverguhr/german-sentiment-bert, with
-additional training on news data.
 
-See https://github.com/text-analytics-20/news-sentiment-development for
-more information.
+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']
+```