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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: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les |
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tragiques , partont ailleurs qu ’ au premier pied . CÉ . cependant QEd , Roi , |
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719 , 826 , 4496 . |
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--- |
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# Fine-tuned Flair Model on AjMC French NER Dataset (HIPE-2022) |
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This Flair model was fine-tuned on the |
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[AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md) |
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NER Dataset using hmByT5 as backbone LM. |
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The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics, |
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and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/) |
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project. |
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The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`. |
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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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| bs4-e10-lr0.00016 | [0.8417][1] | [0.8404][2] | [0.8414][3] | [0.8344][4] | [0.8375][5] | 83.91 ± 0.28 | |
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| bs4-e10-lr0.00015 | [0.824][6] | [0.8352][7] | [0.8385][8] | [0.8204][9] | [0.8362][10] | 83.09 ± 0.72 | |
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| bs8-e10-lr0.00016 | [0.801][11] | [0.8155][12] | [0.8248][13] | [0.8292][14] | [0.8462][15] | 82.33 ± 1.5 | |
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| bs8-e10-lr0.00015 | [0.8098][16] | [0.8079][17] | [0.8248][18] | [0.8193][19] | [0.842][20] | 82.08 ± 1.23 | |
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[1]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/hmbench/hmbench-ajmc-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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