readme: add initial version

README.md

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+---
+language: en
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: hmteams/teams-base-historic-multilingual-discriminator
+widget:
+- text: Cp . Eur . Phoen . 240 , 1 , αἷμα ddiov φλέγέι .
+---
+
+# Fine-tuned Flair Model on AjMC English NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[AjMC English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
+NER Dataset using hmTEAMS 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: `[0.00015, 0.00016]`
+
+And report micro F1-score on development set:
+
+| Configuration     | Run 1        | Run 2        | Run 3        | Run 4        | Run 5        | Avg.         |
+|-------------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| bs4-e10-lr0.00016 | [0.842][1]   | [0.8548][2]  | [0.8407][3]  | [0.8431][4]  | [0.8443][5]  | 84.5 ± 0.51  |
+| bs4-e10-lr0.00015 | [0.8376][6]  | [0.8343][7]  | [0.8495][8]  | [0.8394][9]  | [0.837][10]  | 83.96 ± 0.52 |
+| bs8-e10-lr0.00015 | [0.8172][11] | [0.8242][12] | [0.8217][13] | [0.8367][14] | [0.8323][15] | 82.64 ± 0.71 |
+| bs8-e10-lr0.00016 | [0.8178][16] | [0.8205][17] | [0.8126][18] | [0.8339][19] | [0.8264][20] | 82.22 ± 0.73 |
+
+[1]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+
+The [training log](training.log) and TensorBoard logs 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 ❤️