Instructions to use dbmdz/flair-historic-ner-onb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use dbmdz/flair-historic-ner-onb with Flair:
from flair.data import Sentence from flair.models import SequenceTagger tagger = SequenceTagger.load("dbmdz/flair-historic-ner-onb") sentence = Sentence("George Washington went to Washington.") tagger.predict(sentence) print(sentence) - Notebooks
- Google Colab
- Kaggle
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tags:
- flair
- token-classification
- sequence-tagger-model
language: de
widget:
- text: "April Martin Ansclm, K. Gefangen-Auffehers Georg Sausgruber."
license: mit
---
# 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.
|