|
--- |
|
language: fi |
|
license: mit |
|
tags: |
|
- flair |
|
- token-classification |
|
- sequence-tagger-model |
|
base_model: dbmdz/bert-tiny-historic-multilingual-cased |
|
widget: |
|
- text: Rooseveltin sihteeri ilmoittaa perättö - mäksi tiedon , että Rooseveltia olisi |
|
kehotettu käymään Englannissa , Saksassa ja Venäjällä puhumassa San Franciscon |
|
näyttelyn puolesta . |
|
--- |
|
|
|
# Fine-tuned Flair Model on Finnish NewsEye NER Dataset (HIPE-2022) |
|
|
|
This Flair model was fine-tuned on the |
|
[Finnish NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md) |
|
NER Dataset using hmBERT Tiny as backbone LM. |
|
|
|
The NewsEye dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 |
|
in French, German, Finnish, and Swedish. |
|
More information can be found [here](https://dl.acm.org/doi/abs/10.1145/3404835.3463255). |
|
|
|
The following NEs were annotated: `PER`, `LOC`, `ORG` and `HumanProd`. |
|
|
|
# Results |
|
|
|
We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: |
|
|
|
* Batch Sizes: `[4, 8]` |
|
* Learning Rates: `[5e-05, 3e-05]` |
|
|
|
And report micro F1-score on development set: |
|
|
|
| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |
|
|-------------------|--------------|--------------|--------------|-----------------|--------------|-----------------| |
|
| `bs4-e10-lr5e-05` | [0.287][1] | [0.2991][2] | [0.2927][3] | [**0.3193**][4] | [0.2961][5] | 0.2988 ± 0.0123 | |
|
| `bs8-e10-lr5e-05` | [0.2365][6] | [0.2617][7] | [0.2667][8] | [0.2893][9] | [0.2164][10] | 0.2541 ± 0.0282 | |
|
| `bs4-e10-lr3e-05` | [0.1927][11] | [0.2165][12] | [0.2442][13] | [0.2513][14] | [0.1848][15] | 0.2179 ± 0.0297 | |
|
| `bs8-e10-lr3e-05` | [0.1222][16] | [0.1524][17] | [0.1858][18] | [0.1063][19] | [0.1056][20] | 0.1345 ± 0.0344 | |
|
|
|
[1]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
|
[2]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
|
[3]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
|
[4]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
|
[5]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
|
[6]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
|
[7]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
|
[8]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
|
[9]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
|
[10]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
|
[11]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
|
[12]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
|
[13]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
|
[14]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
|
[15]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
|
[16]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
|
[17]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
|
[18]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
|
[19]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
|
[20]: https://hf.co/stefan-it/hmbench-newseye-fi-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
|
|
|
The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) 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 ❤️ |
|
|
