readme: add initial version of model card

Hi,

this PR adds the initial version of the model card.

README.md

@@ -0,0 +1,72 @@
+---
+language: en
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-base-historic-multilingual-cased
+widget:
+- text: On Wednesday , a public dinner was given by the Conservative Burgesses of
+    Leads , to the Conservative members of the Leeds Town Council , in the Music Hall
+    , Albion-street , which was very numerously attended .
+---
+
+# Fine-tuned Flair Model on TopRes19th English NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[TopRes19th English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md)
+NER Dataset using hmBERT as backbone LM.
+
+The TopRes19th dataset consists of NE-annotated historical English newspaper articles from 19C.
+
+The following NEs were annotated: `BUILDING`, `LOC` and `STREET`.
+
+# 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 | [0.8024][1]  | [0.7936][2]  | [0.8083][3]  | [0.8042][4]  | [0.8122][5]  | 80.41 ± 0.63 |
+| bs4-e10-lr3e-05 | [0.791][6]   | [0.8143][7]  | [0.8017][8]  | [0.8065][9]  | [0.8065][10] | 80.4 ± 0.77  |
+| bs8-e10-lr5e-05 | [0.7974][11] | [0.7983][12] | [0.8092][13] | [0.8094][14] | [0.7828][15] | 79.94 ± 0.98 |
+| bs4-e10-lr5e-05 | [0.8058][16] | [0.7966][17] | [0.8033][18] | [0.7889][19] | [0.786][20]  | 79.61 ± 0.77 |
+
+[1]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-topres19th-en-hmbert-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 ❤️