readme: add initial version

This introduces the initial version of model card.

README.md

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+---
+language: nl
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: hmteams/teams-base-historic-multilingual-discriminator
+widget:
+- text: Professoren der Geneeskun dige Faculteit te Groningen alsook van de HH , Doctoren
+    en Chirurgijns van Groningen , Friesland , Noordholland , Overijssel , Gelderland
+    , Drenthe , in welke Provinciën dit Elixir als Medicament voor Mond en Tanden
+    reeds jaren bakend is .
+---
+
+# Fine-tuned Flair Model on Dutch ICDAR-Europeana NER Dataset
+
+This Flair model was fine-tuned on the
+[Dutch ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar)
+NER Dataset using hmTEAMS 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: `[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 | [0.8791][1]  | [0.88][2]    | [0.8744][3]  | [0.8843][4]  | [0.8829][5]  | 88.01 ± 0.34 |
+| bs4-e10-lr3e-05 | [0.8599][6]  | [0.8681][7]  | [0.872][8]   | [0.8684][9]  | [0.8851][10] | 87.07 ± 0.82 |
+| bs8-e10-lr5e-05 | [0.8688][11] | [0.86][12]   | [0.8726][13] | [0.8681][14] | [0.8772][15] | 86.93 ± 0.57 |
+| bs4-e10-lr5e-05 | [0.8622][16] | [0.8684][17] | [0.8617][18] | [0.8667][19] | [0.8651][20] | 86.48 ± 0.26 |
+
+[1]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-icdar-nl-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-icdar-nl-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 ❤️