readme: add initial version

This introduces the initial version of model card.

README.md

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+---
+language: fr
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: hmteams/teams-base-historic-multilingual-discriminator
+widget:
+- text: 'Parmi les remèdes recommandés par la Société , il faut mentionner celui que
+    M . Schatzmann , de Lausanne , a proposé :'
+---
+
+# Fine-tuned Flair Model on LeTemps French NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[LeTemps French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md)
+NER Dataset using hmTEAMS as backbone LM.
+
+The LeTemps dataset consists of NE-annotated historical French newspaper articles from mid-19C to mid 20C.
+
+The following NEs were annotated: `loc`, `org` and `pers`.
+
+# 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.         |
+|-----------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| bs8-e10-lr3e-05 | [0.6651][1]  | [0.6542][2]  | [0.66][3]    | [0.6705][4]  | [0.6702][5]  | 66.4 ± 0.62  |
+| bs4-e10-lr3e-05 | [0.66][6]    | [0.6641][7]  | [0.6641][8]  | [0.6595][9]  | [0.6548][10] | 66.05 ± 0.35 |
+| bs8-e10-lr5e-05 | [0.6564][11] | [0.6555][12] | [0.6598][13] | [0.6581][14] | [0.6636][15] | 65.87 ± 0.29 |
+| bs4-e10-lr5e-05 | [0.6415][16] | [0.6602][17] | [0.601][18]  | [0.6505][19] | [0.6638][20] | 64.34 ± 2.26 |
+
+[1]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-letemps-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+
+The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) 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 ❤️