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

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- readme: add initial version of model card (619a2a57735bd81cb7b8d4e236966b6b045cbe52)

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+ ---
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+ language: de
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+ license: mit
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+ tags:
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+ - flair
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+ - token-classification
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+ - sequence-tagger-model
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+ base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased
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+ widget:
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+ - text: — Dramatiſch war der Stoff vor Sophokles von Äſchylos behandelt worden in
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+ den Θροῇσσαι , denen vielleicht in der Trilogie das Stüc>"OnJw» κοίσις vorherging
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+ , das Stück Σαλαμίνιαι folgte .
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+ ---
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+
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+ # Fine-tuned Flair Model on AjMC German NER Dataset (HIPE-2022)
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+
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+ This Flair model was fine-tuned on the
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+ [AjMC German](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
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+ NER Dataset using hmBERT 64k as backbone LM.
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+
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+ The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
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+ and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
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+ project.
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+
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+ The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`.
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+
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+ # Results
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+
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+ We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
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+
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+ * Batch Sizes: `[4, 8]`
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+ * Learning Rates: `[5e-05, 3e-05]`
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+
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+ And report micro F1-score on development set:
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+
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+ | Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average |
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+ |-------------------|-----------------|--------------|--------------|-------------|--------------|-----------------|
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+ | `bs4-e10-lr3e-05` | [**0.8806**][1] | [0.8988][2] | [0.8967][3] | [0.8924][4] | [0.8994][5] | 0.8936 ± 0.0078 |
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+ | `bs8-e10-lr5e-05` | [0.8951][6] | [0.8972][7] | [0.8933][8] | [0.8892][9] | [0.8902][10] | 0.893 ± 0.0033 |
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+ | `bs4-e10-lr5e-05` | [0.8789][11] | [0.891][12] | [0.9012][13] | [0.891][14] | [0.8873][15] | 0.8899 ± 0.008 |
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+ | `bs8-e10-lr3e-05` | [0.88][16] | [0.8889][17] | [0.8764][18] | [0.897][19] | [0.8948][20] | 0.8874 ± 0.009 |
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+
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+ [1]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
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+ [2]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
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+ [3]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
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+ [4]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
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+ [5]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
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+ [6]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
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+ [7]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
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+ [8]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
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+ [9]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
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+ [10]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
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+ [11]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
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+ [12]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
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+ [13]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
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+ [14]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
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+ [15]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
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+ [16]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
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+ [17]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
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+ [18]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
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+ [19]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
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+ [20]: https://hf.co/stefan-it/hmbench-ajmc-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
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+
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+ The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) are also uploaded to the model hub.
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+
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+ More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench).
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+
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+ # Acknowledgements
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+
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+ We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
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+ [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.
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+
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+ Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
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+ Many Thanks for providing access to the TPUs ❤️