readme: add initial version of model card

Hi,

this PR adds the initial version of the model card.

README.md

@@ -0,0 +1,74 @@
+---
+language: sv
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-base-historic-multilingual-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 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.         |
+|-----------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| bs4-e10-lr5e-05 | [0.8433][1]  | [0.829][2]   | [0.8207][3]  | [0.8286][4]  | [0.8318][5]  | 83.07 ± 0.73 |
+| bs4-e10-lr3e-05 | [0.819][6]   | [0.8296][7]  | [0.8439][8]  | [0.8202][9]  | [0.8327][10] | 82.91 ± 0.91 |
+| bs8-e10-lr5e-05 | [0.8214][11] | [0.8073][12] | [0.8309][13] | [0.8324][14] | [0.8324][15] | 82.49 ± 0.97 |
+| bs8-e10-lr3e-05 | [0.817][16]  | [0.8155][17] | [0.8187][18] | [0.8259][19] | [0.8088][20] | 81.72 ± 0.55 |
+
+[1]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-newseye-sv-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+
+The [training log](training.log) and TensorBoard logs 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 ❤️