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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: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax |
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inference: false |
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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 hmByT5 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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# ⚠️ Inference Widget ⚠️ |
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Fine-Tuning ByT5 models in Flair is currently done by implementing an own [`ByT5Embedding`][1] class. |
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This class needs to be present when running the model with Flair. |
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Thus, the inference widget is not working with hmByT5 at the moment on the Model Hub and is currently disabled. |
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This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly. |
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[1]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py |
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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: `[0.00015, 0.00016]` |
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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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| bs8-e10-lr0.00016 | [0.6553][1] | [0.6628][2] | [0.6699][3] | [0.6524][4] | [0.6542][5] | 65.89 ± 0.65 | |
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| bs4-e10-lr0.00015 | [0.6603][6] | [0.6651][7] | [0.654][8] | [0.6575][9] | [0.6575][10] | 65.89 ± 0.37 | |
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| bs4-e10-lr0.00016 | [0.6423][11] | [0.6595][12] | [0.6625][13] | [0.6657][14] | [0.6538][15] | 65.68 ± 0.82 | |
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| bs8-e10-lr0.00015 | [0.6502][16] | [0.6541][17] | [0.6607][18] | [0.6496][19] | [0.6629][20] | 65.55 ± 0.54 | |
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[1]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 |
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The [training log](training.log) and TensorBoard logs 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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