readme: add initial version of model card

Hi,

this PR adds the initial version of the model card.

README.md

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+---
+language: fr
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-base-historic-multilingual-cased
+widget:
+- text: Nous recevons le premier numéro d ' un nouveau journal , le Radical - Libéral
+ , qui paraîtra à Genève deux fois la semaine . Son but est de représenter l '
+ élément national du radicalisme genevois , en d ' autres termes , de défendre
+ la politique intransigeante do M . Carteret , en opposition aux tendances du groupe
+ _ > dont le Genevois est l ' organe . Bétail .
+---
+
+# Fine-tuned Flair Model on French HIPE-2020 Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[French HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md)
+NER Dataset using hmBERT as backbone LM.
+
+The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found
+[here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21).
+
+The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`.
+
+# Results
+
+We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
+
+* Batch Sizes: `[8, 4]`
+* Learning Rates: `[3e-05, 5e-05]`
+
+And report micro F1-score on development set:
+
+| Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. |
+|-----------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| bs4-e10-lr3e-05 | [0.8314][1] | [0.8377][2] | [0.8359][3] | [0.8214][4] | [0.8364][5] | 83.26 ± 0.6 |
+| bs8-e10-lr3e-05 | [0.83][6] | [0.8274][7] | [0.8358][8] | [0.8234][9] | [0.8327][10] | 82.99 ± 0.43 |
+| bs8-e10-lr5e-05 | [0.8301][11] | [0.8321][12] | [0.8267][13] | [0.8266][14] | [0.8308][15] | 82.93 ± 0.22 |
+| bs4-e10-lr5e-05 | [0.8181][16] | [0.8087][17] | [0.8239][18] | [0.8219][19] | [0.8224][20] | 81.9 ± 0.55 |
+
+[1]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-hipe2020-fr-hmbert-bs4-wsFalse-e10-lr5e-05-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 ❤️