german-sentiment-bert EU AI Act Compliance Profile

German Sentiment Classification with Bert

This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage of the model, we provide a Python package that bundles the code need for the preprocessing and inferencing.

The model uses the Googles Bert architecture and was trained on 1.834 million German-language samples. The training data contains texts from various domains like Twitter, Facebook and movie, app and hotel reviews. You can find more information about the dataset and the training process in the paper.

Using the Python package

To get started install the package from pypi:

pip install germansentiment

from germansentiment import SentimentModel

model = SentimentModel()

texts = [
    "Mit keinem guten Ergebniss","Das ist gar nicht mal so gut",
    "Total awesome!","nicht so schlecht wie erwartet",
    "Der Test verlief positiv.","Sie fährt ein grünes Auto."]
       
result = model.predict_sentiment(texts)
print(result)

The code above will output following list:

["negative","negative","positive","positive","neutral", "neutral"]

Output class probabilities

from germansentiment import SentimentModel

model = SentimentModel()

classes, probabilities = model.predict_sentiment(["das ist super"], output_probabilities = True) 
print(classes, probabilities)

['positive'] [[['positive', 0.9761366844177246], ['negative', 0.023540444672107697], ['neutral', 0.00032294404809363186]]]

Model and Data

If you are interested in code and data that was used to train this model please have a look at this repository and our [paper](http://www.lrec-conf.org/pro

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