
# Towards Robust Named Entity Recognition for Historic German

Based on [our paper](https://www.aclweb.org/anthology/W19-4312/)
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.

{"language": "de", "license": "mit", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "April Martin Ansclm, K. Gefangen-Auffehers Georg Sausgruber."}]}
flair pytorch token-classification sequence-tagger-model de license:mit region:us
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us 

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
=======



Paper reported an averaged F1-score of 85.31.


† denotes that this model is selected for upload.



TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us 

passage: TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #license-mit #region-us 
