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--- |
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language: en |
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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-cased |
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widget: |
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- text: Cp . Eur . Phoen . 240 , 1 , αἷμα ddiov φλέγέι . |
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--- |
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# Fine-tuned Flair Model on AjMC English NER Dataset (HIPE-2022) |
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This Flair model was fine-tuned on the |
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[AjMC English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md) |
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NER Dataset using hmBERT 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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# 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: `[3e-05, 5e-05]` |
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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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| bs8-e10-lr3e-05 | [0.8473][1] | [0.8494][2] | [0.8558][3] | [0.8578][4] | [0.8541][5] | 85.29 ± 0.39 | |
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| bs8-e10-lr5e-05 | [0.8504][6] | [0.8474][7] | [0.8501][8] | [0.8486][9] | [0.8491][10] | 84.91 ± 0.11 | |
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| bs4-e10-lr3e-05 | [0.8376][11] | [0.8302][12] | [0.8487][13] | [0.8615][14] | [0.8517][15] | 84.59 ± 1.1 | |
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| bs4-e10-lr5e-05 | [0.8498][16] | [0.8341][17] | [0.8405][18] | [0.8528][19] | [0.8359][20] | 84.26 ± 0.74 | |
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[1]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[2]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[3]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[4]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[5]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[6]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[7]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[8]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[9]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[10]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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[11]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 |
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[12]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 |
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[13]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 |
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[14]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 |
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[15]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 |
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[16]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 |
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[17]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 |
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[18]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 |
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[19]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 |
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[20]: https://hf.co/hmbench/hmbench-ajmc-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 |
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The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) 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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