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-bt is trained on German 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.
See schlenker/parl-german-ner which is trained on a mix of news data and parliamentary speeches.
🔍 Model Overview
- Task Type: Token classification
- Base Model: GBERT large
- Fine-tuning method: full fine-tuning
- Language: German
📚 Dataset
Models were trained and evaluated on 267 manually annotated parliamentary speeches (resulting in 10,564 annotated entities).
Data Splits
- Train: 3,020 instances
- Dev: 405 instances
- Test: 1,409 instances
🏋️ Model Training
📊 Evaluation
🚀 How to Use
Please, refer to our GitHub repo for detailed instructions on the required input format and how to run the model.