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.
Runs of dbmdz flair-historic-ner-lft on huggingface.co
17
Total runs
2
24-hour runs
2
3-day runs
3
7-day runs
11
30-day runs
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