|
--- |
|
language: fr |
|
license: mit |
|
tags: |
|
- flair |
|
- token-classification |
|
- sequence-tagger-model |
|
base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax |
|
inference: false |
|
widget: |
|
- text: Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous |
|
ne pouvons céder parce que l' état moral de nos troupe* en souffrirait trop . |
|
( Fournier . ) Des avions ennemis lancent dix-sept bombes sur Dunkerque LONDRES |
|
. 31 décembre . |
|
--- |
|
|
|
# Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset |
|
|
|
This Flair model was fine-tuned on the |
|
[French ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar) |
|
NER Dataset using hmByT5 as backbone LM. |
|
|
|
The ICDAR-Europeana NER Dataset is a preprocessed variant of the |
|
[Europeana NER Corpora](https://github.com/EuropeanaNewspapers/ner-corpora) for Dutch and French. |
|
|
|
The following NEs were annotated: `PER`, `LOC` and `ORG`. |
|
|
|
# ⚠️ Inference Widget ⚠️ |
|
|
|
Fine-Tuning ByT5 models in Flair is currently done by implementing an own [`ByT5Embedding`][0] class. |
|
|
|
This class needs to be present when running the model with Flair. |
|
|
|
Thus, the inference widget is not working with hmByT5 at the moment on the Model Hub and is currently disabled. |
|
|
|
This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly. |
|
|
|
[0]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py |
|
|
|
# Results |
|
|
|
We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: |
|
|
|
* Batch Sizes: `[8, 4]` |
|
* Learning Rates: `[0.00015, 0.00016]` |
|
|
|
And report micro F1-score on development set: |
|
|
|
| Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. | |
|
|-------------------|--------------|--------------|--------------|--------------|--------------|--------------| |
|
| bs8-e10-lr0.00015 | [0.7743][1] | [0.7654][2] | [0.7604][3] | [0.7729][4] | [0.7736][5] | 76.93 ± 0.55 | |
|
| bs8-e10-lr0.00016 | [0.7686][6] | [0.7648][7] | [0.7678][8] | [0.7653][9] | [0.7755][10] | 76.84 ± 0.38 | |
|
| bs4-e10-lr0.00015 | [0.7757][11] | [0.7549][12] | [0.7693][13] | [0.7597][14] | [0.7696][15] | 76.58 ± 0.75 | |
|
| bs4-e10-lr0.00016 | [0.7625][16] | [0.7575][17] | [0.769][18] | [0.7635][19] | [0.7647][20] | 76.34 ± 0.37 | |
|
|
|
[1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 |
|
[2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 |
|
[3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 |
|
[4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 |
|
[5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 |
|
[6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 |
|
[7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 |
|
[8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 |
|
[9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 |
|
[10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 |
|
[11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 |
|
[12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 |
|
[13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 |
|
[14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 |
|
[15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 |
|
[16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 |
|
[17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 |
|
[18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 |
|
[19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 |
|
[20]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 |
|
|
|
The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. |
|
|
|
More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). |
|
|
|
# Acknowledgements |
|
|
|
We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and |
|
[Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. |
|
|
|
Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). |
|
Many Thanks for providing access to the TPUs ❤️ |
|
|
