readme: add initial version of model card

Hey,

this commit adds 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: dbmdz/bert-tiny-historic-multilingual-cased
+widget:
+- text: Le Moniteur universel fait ressortir les avantages de la situation de l '
+    Allemagne , sa force militaire , le peu d ' intérêts personnels qu ' elle peut
+    avoir dans la question d ' Orient .
+---
+
+# Fine-tuned Flair Model on French NewsEye NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[French NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md)
+NER Dataset using hmBERT Tiny as backbone LM.
+
+The NewsEye dataset is comprised of diachronic historical newspaper material published between 1850 and 1950
+in French, German, Finnish, and Swedish.
+More information can be found [here](https://dl.acm.org/doi/abs/10.1145/3404835.3463255).
+
+The following NEs were annotated: `PER`, `LOC`, `ORG` and `HumanProd`.
+
+# 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-lr5e-05` | [0.5782][1]  | [0.5584][2]  | [0.5555][3]  | [**0.5685**][4] | [0.5422][5]  | 0.5606 ± 0.0136 |
+| `bs8-e10-lr5e-05` | [0.5486][6]  | [0.5273][7]  | [0.5282][8]  | [0.5288][9]     | [0.5067][10] | 0.5279 ± 0.0148 |
+| `bs4-e10-lr3e-05` | [0.5251][11] | [0.5103][12] | [0.5041][13] | [0.5124][14]    | [0.479][15]  | 0.5062 ± 0.017  |
+| `bs8-e10-lr3e-05` | [0.4815][16] | [0.4879][17] | [0.4783][18] | [0.4648][19]    | [0.4628][20] | 0.4751 ± 0.0109 |
+
+[1]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-newseye-fr-hmbert_tiny-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 ❤️