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readme: add initial version

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Hi,
This PR introduces the initial version of model card

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  1. README.md +87 -0
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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: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax
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+ inference: false
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+ widget:
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+ - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern
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+ Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee
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+ persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen .
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+ Lacke mit 6000 Mann ihm entgegen marschirt .
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+ ---
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+
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+ # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022)
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+
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+ This Flair model was fine-tuned on the
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+ [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md)
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+ NER Dataset using hmByT5 as backbone LM.
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+
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+ The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found
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+ [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21).
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+
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+ The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`.
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+
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+ # ⚠️ Inference Widget ⚠️
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+
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+ Fine-Tuning ByT5 models in Flair is currently done by implementing an own [`ByT5Embedding`][1] class.
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+
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+ This class needs to be present when running the model with Flair.
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+
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+ Thus, the inference widget is not working with hmByT5 at the moment on the Model Hub and is currently disabled.
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+
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+ This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly.
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+
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+ [1]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py
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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: `[8, 4]`
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+ * Learning Rates: `[0.00015, 0.00016]`
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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 | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. |
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+ |-------------------|--------------|--------------|--------------|--------------|--------------|--------------|
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+ | bs4-e10-lr0.00016 | [0.7596][1] | [0.7466][2] | [0.7771][3] | [0.7894][4] | [0.7717][5] | 76.89 ± 1.47 |
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+ | bs8-e10-lr0.00015 | [0.7593][6] | [0.7663][7] | [0.7611][8] | [0.7647][9] | [0.7667][10] | 76.36 ± 0.29 |
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+ | bs8-e10-lr0.00016 | [0.7607][11] | [0.7736][12] | [0.7567][13] | [0.756][14] | [0.746][15] | 75.86 ± 0.89 |
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+ | bs4-e10-lr0.00015 | [0.7541][16] | [0.7466][17] | [0.7575][18] | [0.7579][19] | [0.7599][20] | 75.52 ± 0.47 |
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+
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+ [1]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
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+ [2]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
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+ [3]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
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+ [4]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
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+ [5]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
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+ [6]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
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+ [7]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
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+ [8]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
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+ [9]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
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+ [10]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
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+ [11]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
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+ [12]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
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+ [13]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
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+ [14]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
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+ [15]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
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+ [16]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
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+ [17]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
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+ [18]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
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+ [19]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
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+ [20]: https://hf.co/hmbench/hmbench-hipe2020-de-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
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+
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+ The [training log](training.log) and TensorBoard logs 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 ❤️