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This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. This multilanguage model was trained on the Europarl Dataset provided by the SEPP-NLG Shared Task. *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.* The model restores the following punctuation markers: **"." "," "?" "-" ":"** We provide a simple python package that allows you to process text of any length.

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This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. This multilanguage model was trained on the Europarl Dataset provided by the SEPP-NLG Shared Task and for the Dutch language we included the SoNaR Dataset. *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.* The model restores the following punctuation markers: **"." "," "?" "-" ":"** We provide a simple python package that allows you to process text of any length.

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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. To get started install the package from pypi:

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