schlenker/parl-german-ner

Annotation Layer: NER

This model is part of GePaDeU, which equips parliamentary debates of the German Bundestag with rich semantic and pragmatic information across multiple annotation layers.

parl-german-ner is trained on a mix of news headlines and parliamentary speeches to tag a sequence with fine-grained named entities (e.g., geo-political entities, persons, organizations). The NER tag inventory is adapted from the OntoNotes NER inventory.

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🔍 Model Overview

• Task Type: Token classification
• Base Model: GBERT large
• Fine-tuning method: full fine-tuning
• Language: German

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📚 Dataset

Models were trained and evaluated on a mix of Twitter news headlines data (Ruppenhofer et al., 2020) and 40 manually annotated parliamentary speeches. The latter results in 1,639 annotated entities.

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🏋️ Model Training

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📊 Evaluation

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🚀 How to Use

Please, refer to our GitHub repo for detailed instructions on the required input format and how to run the model.

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⚠️ Limitations