This model is trained to classify statements on the European Union. It finetuned a bert-base-german-cased model on 1700 sentences from the German parliament. It is trained to detect explicit and implicit mentionings of the European Union.
Code Book
If a sentence mentions the EU in an explicit or implicit way, the model categorizes it as EU speech. Examples can be mentioning about policies,
politicians, events, elections on the European level.
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despite this adaptation to European law, the special features of
the special features of German antitrust law must not be
not be thrown overboard.
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but this is something we must do if we have an interest - and the vast majority of this House repeatedly emphasizes this - in using resources more efficiently in Europe through cooperation and in building a common industry in various areas.
Model Details
Finetuned from model: google-bert/bert-based-cased
Epochs: 3
Accuracy: 0.967
Lattmann, J. (2025, March 17). Detecting EU sentiment in texts: A LLM Machine Learning application for Euroscepticism research.
https://doi.org/10.31219/osf.io/mravb_v1
