|
--- |
|
language: de |
|
license: mit |
|
tags: |
|
- flair |
|
- token-classification |
|
- sequence-tagger-model |
|
base_model: dbmdz/bert-base-historic-multilingual-cased |
|
widget: |
|
- text: — Dramatiſch war der Stoff vor Sophokles von Äſchylos behandelt worden in |
|
den Θροῇσσαι , denen vielleicht in der Trilogie das Stüc>"OnJw» κοίσις vorherging |
|
, das Stück Σαλαμίνιαι folgte . |
|
--- |
|
|
|
# Fine-tuned Flair Model on AjMC German NER Dataset (HIPE-2022) |
|
|
|
This Flair model was fine-tuned on the |
|
[AjMC German](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md) |
|
NER Dataset using hmBERT as backbone LM. |
|
|
|
The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics, |
|
and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/) |
|
project. |
|
|
|
The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`. |
|
|
|
# Results |
|
|
|
We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: |
|
|
|
* Batch Sizes: `[8, 4]` |
|
* Learning Rates: `[3e-05, 5e-05]` |
|
|
|
And report micro F1-score on development set: |
|
|
|
| Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. | |
|
|-----------------|--------------|--------------|--------------|--------------|--------------|--------------| |
|
| bs4-e10-lr5e-05 | [0.8937][1] | [0.8849][2] | [0.8977][3] | [0.8867][4] | [0.886][5] | 88.98 ± 0.5 | |
|
| bs8-e10-lr5e-05 | [0.8816][6] | [0.8952][7] | [0.8766][8] | [0.8934][9] | [0.8875][10] | 88.69 ± 0.7 | |
|
| bs4-e10-lr3e-05 | [0.8738][11] | [0.879][12] | [0.8951][13] | [0.8889][14] | [0.8772][15] | 88.28 ± 0.79 | |
|
| bs8-e10-lr3e-05 | [0.8743][16] | [0.8741][17] | [0.8722][18] | [0.8932][19] | [0.8809][20] | 87.89 ± 0.77 | |
|
|
|
[1]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
|
[2]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
|
[3]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
|
[4]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
|
[5]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
|
[6]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
|
[7]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
|
[8]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
|
[9]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
|
[10]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
|
[11]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
|
[12]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
|
[13]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
|
[14]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
|
[15]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
|
[16]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
|
[17]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
|
[18]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
|
[19]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
|
[20]: https://hf.co/hmbench/hmbench-ajmc-de-hmbert-bs8-wsFalse-e10-lr3e-05-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 ❤️ |
|
|
