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Corrected link to paper (#6905)

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Stefan Engl
2020-09-03 15:23:42 +02:00
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model_cards/oliverguhr/german-sentiment-bert/README.md
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@@ -4,7 +4,7 @@ This model was trained for sentiment classification of German language texts. To
we provide a Python package that bundles the code need for the preprocessing and inferencing. 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. 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](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.201.pdf). You can find more information about the dataset and the training process in the [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf).
## Using the Python package ## Using the Python package