readme: add initial version of model card

Hey,

this PR adds the initial version of model card.

README.md

@@ -0,0 +1,74 @@
+---
+language: fr
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-tiny-historic-multilingual-cased
+widget:
+- text: Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous
+    ne pouvons céder parce que l' état moral de nos troupe* en souffrirait trop .
+    ( Fournier . ) Des avions ennemis lancent dix-sept bombes sur Dunkerque LONDRES
+    . 31 décembre .
+---
+
+# Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset
+
+This Flair model was fine-tuned on the
+[French ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar)
+NER Dataset using hmBERT Tiny as backbone LM.
+
+The ICDAR-Europeana NER Dataset is a preprocessed variant of the
+[Europeana NER Corpora](https://github.com/EuropeanaNewspapers/ner-corpora) for Dutch and French.
+
+The following NEs were annotated: `PER`, `LOC` and `ORG`.
+
+# 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` | [0.6013][1]  | [0.5273][2]  | [0.6086][3]  | [**0.6208**][4] | [0.5731][5]  | 0.5862 ± 0.0373 |
+| `bs8-e10-lr5e-05` | [0.6186][6]  | [0.4917][7]  | [0.6056][8]  | [0.5972][9]     | [0.4881][10] | 0.5602 ± 0.0647 |
+| `bs4-e10-lr3e-05` | [0.6034][11] | [0.4735][12] | [0.5837][13] | [0.578][14]     | [0.4716][15] | 0.542 ± 0.0641  |
+| `bs8-e10-lr3e-05` | [0.5743][16] | [0.4119][17] | [0.551][18]  | [0.5261][19]    | [0.4408][20] | 0.5008 ± 0.0708 |
+
+[1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-icdar-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 ❤️