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--- |
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language: fr |
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license: mit |
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tags: |
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- flair |
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- token-classification |
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- sequence-tagger-model |
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base_model: dbmdz/bert-base-historic-multilingual-cased |
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widget: |
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- text: Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous |
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ne pouvons céder parce que l' état moral de nos troupe* en souffrirait trop . |
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( Fournier . ) Des avions ennemis lancent dix-sept bombes sur Dunkerque LONDRES |
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. 31 décembre . |
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--- |
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# Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset |
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This Flair model was fine-tuned on the |
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[French ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar) |
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NER Dataset using hmBERT as backbone LM. |
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The ICDAR-Europeana NER Dataset is a preprocessed variant of the |
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[Europeana NER Corpora](https://github.com/EuropeanaNewspapers/ner-corpora) for Dutch and French. |
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The following NEs were annotated: `PER`, `LOC` and `ORG`. |
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# Results |
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We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: |
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* Batch Sizes: `[8, 4]` |
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* Learning Rates: `[3e-05, 5e-05]` |
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And report micro F1-score on development set: |
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| Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. | |
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|-----------------|--------------|--------------|--------------|--------------|--------------|--------------| |
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| bs4-e10-lr3e-05 | [0.7731][1] | [0.7696][2] | [0.7666][3] | [0.7823][4] | [0.7714][5] | 77.26 ± 0.53 | |
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| bs4-e10-lr5e-05 | [0.774][6] | [0.7571][7] | [0.7685][8] | [0.7694][9] | [0.7704][10] | 76.79 ± 0.57 | |
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| bs8-e10-lr5e-05 | [0.7675][11] | [0.7698][12] | [0.7601][13] | [0.7657][14] | [0.7641][15] | 76.54 ± 0.33 | |
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| bs8-e10-lr3e-05 | [0.7596][16] | [0.7697][17] | [0.7711][18] | [0.7628][19] | [0.7574][20] | 76.41 ± 0.54 | |
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[1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. |
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More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). |
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# Acknowledgements |
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We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and |
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[Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. |
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Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). |
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Many Thanks for providing access to the TPUs ❤️ |
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