readme: add initial version of model card (#1)

- readme: add initial version of model card (abde35116297cb60b70e4b39ca655bc1222dbfcd)

README.md

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+---
+language: en
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased
+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 hmBERT 64k 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: `[4, 8]`
+* Learning Rates: `[3e-05, 5e-05]`
+
+And report micro F1-score on development set:
+
+| Configuration     | Seed 1       | Seed 2       | Seed 3       | Seed 4       | Seed 5         | Average         |
+|-------------------|--------------|--------------|--------------|--------------|----------------|-----------------|
+| `bs4-e10-lr3e-05` | [0.8565][1]  | [0.8592][2]  | [0.8513][3]  | [0.8622][4]  | [0.8534][5]    | 0.8565 ± 0.0044 |
+| `bs4-e10-lr5e-05` | [0.8582][6]  | [0.852][7]   | [0.8517][8]  | [0.8544][9]  | [0.842][10]    | 0.8517 ± 0.006  |
+| `bs8-e10-lr5e-05` | [0.8412][11] | [0.8369][12] | [0.853][13]  | [0.8392][14] | [**0.85**][15] | 0.8441 ± 0.007  |
+| `bs8-e10-lr3e-05` | [0.8483][16] | [0.8431][17] | [0.8443][18] | [0.8486][19] | [0.8359][20]   | 0.844 ± 0.0052  |
+
+[1]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_64k-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 ❤️