language: fr
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
base_model: dbmdz/bert-base-historic-multilingual-64k-td-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 64k 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:
[4, 8] - Learning Rates:
[3e-05, 5e-05]
And report micro F1-score on development set:
| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average |
|---|---|---|---|---|---|---|
bs4-e10-lr3e-05 |
0.7562 | 0.7716 | 0.7747 | 0.7735 | 0.774 | 0.77 ± 0.0078 |
bs8-e10-lr5e-05 |
0.7669 | 0.7605 | 0.7691 | 0.7665 | 0.7795 | 0.7685 ± 0.0069 |
bs8-e10-lr3e-05 |
0.7716 | 0.7642 | 0.7765 | 0.7629 | 0.7657 | 0.7682 ± 0.0057 |
bs4-e10-lr5e-05 |
0.7139 | 0.7613 | 0.7536 | 0.7548 | 0.7026 | 0.7372 ± 0.0269 |
The training log and TensorBoard logs (not available for hmBERT Base model) 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 ❤️