readme: add initial version
Browse filesThis introduces the initial version of model card.
README.md
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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: hmteams/teams-base-historic-multilingual-discriminator
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widget:
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- text: On Wednesday , a public dinner was given by the Conservative Burgesses of
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Leads , to the Conservative members of the Leeds Town Council , in the Music Hall
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, Albion-street , which was very numerously attended .
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---
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# Fine-tuned Flair Model on TopRes19th English NER Dataset (HIPE-2022)
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This Flair model was fine-tuned on the
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[TopRes19th English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md)
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NER Dataset using hmTEAMS as backbone LM.
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The TopRes19th dataset consists of NE-annotated historical English newspaper articles from 19C.
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The following NEs were annotated: `BUILDING`, `LOC` and `STREET`.
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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 | [0.8089][1] | [0.8137][2] | [0.8083][3] | [0.8145][4] | [0.8082][5] | 81.07 ± 0.28 |
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| bs4-e10-lr3e-05 | [0.8068][6] | [0.8008][7] | [0.8195][8] | [0.8086][9] | [0.8049][10] | 80.81 ± 0.63 |
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| bs8-e10-lr5e-05 | [0.818][11] | [0.795][12] | [0.7992][13] | [0.804][14] | [0.7938][15] | 80.2 ± 0.88 |
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| bs4-e10-lr5e-05 | [0.8109][16] | [0.8114][17] | [0.7951][18] | [0.7901][19] | [0.795][20] | 80.05 ± 0.89 |
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[1]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
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[2]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
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[3]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
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[4]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
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[5]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
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[6]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
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[7]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
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[8]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
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[9]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
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[10]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
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[11]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
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[12]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
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[13]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
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[14]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
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[15]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
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[16]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
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[17]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
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[18]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
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[19]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
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[20]: https://hf.co/stefan-it/hmbench-topres19th-en-hmteams-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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