readme: add initial version of model card (#1)

- readme: add initial version of model card (856a70371f791af46512a7545b7f5b1d1b2a0eec)

README.md

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+---
+language: en
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: dbmdz/bert-tiny-historic-multilingual-cased
+widget:
+- text: Cp . Eur . Phoen . 240 , 1 , αἷμα ddiov φλέγέι .
+---
+
+# Fine-tuned Flair Model on AjMC English NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[AjMC English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
+NER Dataset using hmBERT Tiny as backbone LM.
+
+The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
+and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
+project.
+
+The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`.
+
+# Results
+
+We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
+
+* Batch Sizes: `[4, 8]`
+* Learning Rates: `[5e-05, 3e-05]`
+
+And report micro F1-score on development set:
+
+| Configuration     | Seed 1          | Seed 2       | Seed 3       | Seed 4       | Seed 5       | Average         |
+|-------------------|-----------------|--------------|--------------|--------------|--------------|-----------------|
+| `bs4-e10-lr5e-05` | [**0.5579**][1] | [0.5082][2]  | [0.5434][3]  | [0.4949][4]  | [0.4882][5]  | 0.5185 ± 0.0306 |
+| `bs8-e10-lr5e-05` | [0.5301][6]     | [0.468][7]   | [0.525][8]   | [0.4989][9]  | [0.4707][10] | 0.4985 ± 0.0292 |
+| `bs4-e10-lr3e-05` | [0.4972][11]    | [0.427][12]  | [0.4745][13] | [0.4394][14] | [0.4249][15] | 0.4526 ± 0.0319 |
+| `bs8-e10-lr3e-05` | [0.4402][16]    | [0.3531][17] | [0.4141][18] | [0.3808][19] | [0.4073][20] | 0.3991 ± 0.0333 |
+
+[1]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/stefan-it/hmbench-ajmc-en-hmbert_tiny-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 ❤️