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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-64k-td-cased |
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widget: |
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- text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral |
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, qui paraîtra à Genève deux fois la semaine . Son but est de représenter l ' |
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élément national du radicalisme genevois , en d ' autres termes , de défendre |
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la politique intransigeante do M . Carteret , en opposition aux tendances du groupe |
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_ > dont le Genevois est l ' organe . Bétail . |
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--- |
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# Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022) |
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This Flair model was fine-tuned on the |
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[French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) |
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NER Dataset using hmBERT 64k as backbone LM. |
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The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found |
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[here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). |
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The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. |
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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: `[3e-05, 5e-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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| `bs8-e10-lr3e-05` | [0.8389][1] | [0.8466][2] | [0.8299][3] | [0.8391][4] | [0.8427][5] | 0.8394 ± 0.0062 | |
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| `bs4-e10-lr3e-05` | [0.8279][6] | [0.8364][7] | [0.8404][8] | [0.8382][9] | [0.8371][10] | 0.836 ± 0.0048 | |
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| `bs8-e10-lr5e-05` | [0.8418][11] | [0.8337][12] | [0.831][13] | [0.8346][14] | [0.8352][15] | 0.8353 ± 0.004 | |
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| `bs4-e10-lr5e-05` | [0.831][16] | [0.8239][17] | [**0.7784**][18] | [0.8313][19] | [0.8191][20] | 0.8167 ± 0.022 | |
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[1]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/stefan-it/hmbench-hipe2020-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-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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