Towards Robust Named Entity Recognition for Historic German
Based on our paper we release a new model trained on the LFT dataset.
Note: We use BPEmbeddings instead of the combination of Wikipedia, Common Crawl and character embeddings (as used in the paper), so save space and training/inferencing time.
Results
| Dataset \ Run | Run 1 | Run 2 | Run 3† | Avg. |
|---|---|---|---|---|
| Development | 76.32 | 76.13 | 76.36 | 76.27 |
| Test | 77.07 | 77.35 | 77.20 | 77.21 |
Paper reported an averaged F1-score of 77.51.
† denotes that this model is selected for upload.