---
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](https://github.com/stefan-it/historic-domain-adaptation-icdar)
NER Dataset using hmBERT 64k as backbone LM.

The ICDAR-Europeana NER Dataset is a preprocessed variant of the
[Europeana NER Corpora](https://github.com/EuropeanaNewspapers/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][1]      | [0.7716][2]  | [0.7747][3]  | [0.7735][4]  | [0.774][5]   | 0.77 ± 0.0078   |
| `bs8-e10-lr5e-05` | [0.7669][6]      | [0.7605][7]  | [0.7691][8]  | [0.7665][9]  | [0.7795][10] | 0.7685 ± 0.0069 |
| `bs8-e10-lr3e-05` | [**0.7716**][11] | [0.7642][12] | [0.7765][13] | [0.7629][14] | [0.7657][15] | 0.7682 ± 0.0057 |
| `bs4-e10-lr5e-05` | [0.7139][16]     | [0.7613][17] | [0.7536][18] | [0.7548][19] | [0.7026][20] | 0.7372 ± 0.0269 |

[1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
[11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[20]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5

The [training log](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](https://github.com/stefan-it/hmBench).

# Acknowledgements

We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
[Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.

Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs ❤️