Towards Robust Named Entity Recognition for Historic German
Based on our paper we release a new model trained on the ONB 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 | 86.69 | 86.13 | 87.18 | 86.67 |
| Test | 85.27 | 86.05 | 85.75†| 85.69 |
Paper reported an averaged F1-score of 85.31.
†denotes that this model is selected for upload.
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