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--- |
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language: sv |
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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: dbmdz/bert-base-historic-multilingual-64k-td-cased |
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widget: |
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- text: Värri , Teittinen , Forsman , Tensik - kala m . fl . anslöto sig till reservatio |
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- nen , hvaremot lm Fieandt , Huopo - nen , Koskelin , Leppänen , ( Li - belits |
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) , Eklund m . fl . förordade ut - skottets formulering af § 11 . |
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--- |
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# Fine-tuned Flair Model on Swedish NewsEye NER Dataset (HIPE-2022) |
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This Flair model was fine-tuned on the |
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[Swedish NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md) |
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NER Dataset using hmBERT 64k as backbone LM. |
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The NewsEye dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 |
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in French, German, Finnish, and Swedish. |
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More information can be found [here](https://dl.acm.org/doi/abs/10.1145/3404835.3463255). |
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The following NEs were annotated: `PER`, `LOC`, `ORG` and `HumanProd`. |
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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: `[4, 8]` |
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* Learning Rates: `[3e-05, 5e-05]` |
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And report micro F1-score on development set: |
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| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |
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|-------------------|--------------|--------------|--------------|--------------|------------------|-----------------| |
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| `bs4-e10-lr3e-05` | [0.846][1] | [0.8305][2] | [0.8275][3] | [0.8281][4] | [0.8349][5] | 0.8334 ± 0.0076 | |
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| `bs8-e10-lr5e-05` | [0.8227][6] | [0.8407][7] | [0.8309][8] | [0.8222][9] | [0.825][10] | 0.8283 ± 0.0077 | |
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| `bs4-e10-lr5e-05` | [0.8327][11] | [0.8066][12] | [0.8556][13] | [0.8266][14] | [**0.8073**][15] | 0.8258 ± 0.0203 | |
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| `bs8-e10-lr3e-05` | [0.819][16] | [0.8183][17] | [0.8281][18] | [0.8059][19] | [0.8145][20] | 0.8172 ± 0.008 | |
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[1]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) 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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