readme: add initial version of model card

Hey,

this commit adds the initial version of model card.

README.md

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+---
+language: sv
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased
+widget:
+- text: Värri , Teittinen , Forsman , Tensik - kala m . fl . anslöto sig till reservatio
+    - nen , hvaremot lm Fieandt , Huopo - nen , Koskelin , Leppänen , ( Li - belits
+    ) , Eklund m . fl . förordade ut - skottets formulering af § 11 .
+---
+
+# Fine-tuned Flair Model on Swedish NewsEye NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[Swedish NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md)
+NER Dataset using hmBERT 64k 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: `[3e-05, 5e-05]`
+
+And report micro F1-score on development set:
+
+| Configuration     | Seed 1       | Seed 2       | Seed 3       | Seed 4       | Seed 5           | Average         |
+|-------------------|--------------|--------------|--------------|--------------|------------------|-----------------|
+| `bs4-e10-lr3e-05` | [0.846][1]   | [0.8305][2]  | [0.8275][3]  | [0.8281][4]  | [0.8349][5]      | 0.8334 ± 0.0076 |
+| `bs8-e10-lr5e-05` | [0.8227][6]  | [0.8407][7]  | [0.8309][8]  | [0.8222][9]  | [0.825][10]      | 0.8283 ± 0.0077 |
+| `bs4-e10-lr5e-05` | [0.8327][11] | [0.8066][12] | [0.8556][13] | [0.8266][14] | [**0.8073**][15] | 0.8258 ± 0.0203 |
+| `bs8-e10-lr3e-05` | [0.819][16]  | [0.8183][17] | [0.8281][18] | [0.8059][19] | [0.8145][20]     | 0.8172 ± 0.008  |
+
+[1]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-newseye-sv-hmbert_64k-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 ❤️