barflyman/gbert-legal-ner-onnx-q4

Model Card: PaDaS-Lab/gbert-legal-ner ONNX Conversion quantized in 4-Bit

Model: PaDaS-Lab/gbert-legal-ner Task: Named Entity Recognition (NER) on German legal texts. Architecture: gBERT-based Transformer. Key Entities: PER (Persons), ORG (Organizations), GRT (Courts), GS (Laws), ST (Cities), STR (Streets).

This model is based on the following work. If you use this model in your own research or application, please cite the authors.

@conference{icaart23, author={Harshil Darji. and Jelena Mitrović. and Michael Granitzer.}, title={German BERT Model for Legal Named Entity Recognition}, booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,}, year={2023}, pages={723-728}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0011749400003393}, isbn={978-989-758-623-1}, issn={2184-433X}, }