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: 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 hmTEAMS 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: `[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 | [0.825][1]   | [0.8248][2]  | [0.8288][3]  | [0.8309][4]  | [0.8281][5]  | 82.75 ± 0.23 |
+| bs4-e10-lr3e-05 | [0.83][6]    | [0.8345][7]  | [0.8162][8]  | [0.8223][9]  | [0.8346][10] | 82.75 ± 0.72 |
+| bs8-e10-lr5e-05 | [0.8237][11] | [0.8165][12] | [0.8189][13] | [0.8297][14] | [0.8283][15] | 82.34 ± 0.51 |
+| bs4-e10-lr5e-05 | [0.8114][16] | [0.8061][17] | [0.8112][18] | [0.8131][19] | [0.8182][20] | 81.2 ± 0.39  |
+
+[1]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-newseye-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 ❤️