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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-tiny-historic-multilingual-cased |
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widget: |
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- text: 'Parmi les remèdes recommandés par la Société , il faut mentionner celui que |
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M . Schatzmann , de Lausanne , a proposé :' |
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--- |
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# Fine-tuned Flair Model on LeTemps French NER Dataset (HIPE-2022) |
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This Flair model was fine-tuned on the |
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[LeTemps French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md) |
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NER Dataset using hmBERT Tiny as backbone LM. |
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The LeTemps dataset consists of NE-annotated historical French newspaper articles from mid-19C to mid 20C. |
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The following NEs were annotated: `loc`, `org` and `pers`. |
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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: `[4, 8]` |
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* Learning Rates: `[5e-05, 3e-05]` |
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And report micro F1-score on development set: |
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| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |
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|-------------------|------------------|--------------|--------------|--------------|--------------|-----------------| |
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| `bs4-e10-lr5e-05` | [0.5297][1] | [0.5073][2] | [0.5106][3] | [0.5111][4] | [0.5282][5] | 0.5174 ± 0.0107 | |
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| `bs4-e10-lr3e-05` | [0.5012][6] | [0.4703][7] | [0.5019][8] | [0.4857][9] | [0.5072][10] | 0.4933 ± 0.0151 | |
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| `bs8-e10-lr5e-05` | [**0.5027**][11] | [0.4727][12] | [0.5021][13] | [0.4912][14] | [0.4733][15] | 0.4884 ± 0.0148 | |
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| `bs8-e10-lr3e-05` | [0.489][16] | [0.4226][17] | [0.4656][18] | [0.4744][19] | [0.4511][20] | 0.4605 ± 0.0253 | |
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[1]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) 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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