readme: add initial version of model card

Hey,

this PR adds the initial version of model card.

README.md

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+---
+language: de
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased
+widget:
+- text: — Dramatiſch war der Stoff vor Sophokles von Äſchylos behandelt worden in
+    den Θροῇσσαι , denen vielleicht in der Trilogie das Stüc>"OnJw» κοίσις vorherging
+    , das Stück Σαλαμίνιαι folgte .
+---
+
+# Fine-tuned Flair Model on AjMC German NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[AjMC German](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: `[5e-05, 3e-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` | [0.8806][1]  | [0.8988][2]  | [0.8967][3]      | [0.8924][4] | [0.8994][5]  | 0.8936 ± 0.0078 |
+| `bs8-e10-lr5e-05` | [0.8951][6]  | [0.8972][7]  | [0.8933][8]      | [0.8892][9] | [0.8902][10] | 0.893 ± 0.0033  |
+| `bs4-e10-lr5e-05` | [0.8789][11] | [0.891][12]  | [0.9012][13]     | [0.891][14] | [0.8873][15] | 0.8899 ± 0.008  |
+| `bs8-e10-lr3e-05` | [0.88][16]   | [0.8889][17] | [**0.8764**][18] | [0.897][19] | [0.8948][20] | 0.8874 ± 0.009  |
+
+[1]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-ajmc-de-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 ❤️