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readme: update model hub links
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metadata
language: fr
license: mit
tags:
  - flair
  - token-classification
  - sequence-tagger-model
base_model: dbmdz/bert-base-historic-multilingual-cased
widget:
  - text: >-
      Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous
      ne pouvons céder parce que l' état moral de nos troupe* en souffrirait
      trop . ( Fournier . ) Des avions ennemis lancent dix-sept bombes sur
      Dunkerque LONDRES . 31 décembre .

Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset

This Flair model was fine-tuned on the French ICDAR-Europeana NER Dataset using hmBERT as backbone LM.

The ICDAR-Europeana NER Dataset is a preprocessed variant of the Europeana NER Corpora for Dutch and French.

The following NEs were annotated: PER, LOC and ORG.

Results

We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:

  • Batch Sizes: [8, 4]
  • Learning Rates: [3e-05, 5e-05]

And report micro F1-score on development set:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
bs4-e10-lr3e-05 0.7731 0.7696 0.7666 0.7823 0.7714 77.26 ± 0.53
bs4-e10-lr5e-05 0.774 0.7571 0.7685 0.7694 0.7704 76.79 ± 0.57
bs8-e10-lr5e-05 0.7675 0.7698 0.7601 0.7657 0.7641 76.54 ± 0.33
bs8-e10-lr3e-05 0.7596 0.7697 0.7711 0.7628 0.7574 76.41 ± 0.54

The training log and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub.

More information about fine-tuning can be found here.

Acknowledgements

We thank Luisa März, Katharina Schmid and Erion Çano for their fruitful discussions about Historic Language Models.

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). Many Thanks for providing access to the TPUs ❤️