readme: add initial version

README.md

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+---
+language: fi
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax
+inference: false
+widget:
+- text: Rooseveltin sihteeri ilmoittaa perättö - mäksi tiedon , että Rooseveltia olisi
+    kehotettu käymään Englannissa , Saksassa ja Venäjällä puhumassa San Franciscon
+    näyttelyn puolesta .
+---
+
+# Fine-tuned Flair Model on Finnish NewsEye NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[Finnish NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md)
+NER Dataset using hmByT5 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`.
+
+# ⚠️ Inference Widget ⚠️
+
+Fine-Tuning ByT5 models in Flair is currently done by implementing an own [`ByT5Embedding`][1] class.
+
+This class needs to be present when running the model with Flair.
+
+Thus, the inference widget is not working with hmByT5 at the moment on the Model Hub and is currently disabled.
+
+This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly.
+
+[1]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py
+
+# Results
+
+We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
+
+* Batch Sizes: `[8, 4]`
+* Learning Rates: `[0.00015, 0.00016]`
+
+And report micro F1-score on development set:
+
+| Configuration     | Run 1        | Run 2        | Run 3        | Run 4        | Run 5        | Avg.         |
+|-------------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| bs4-e10-lr0.00016 | [0.8017][1]  | [0.7406][2]  | [0.7706][3]  | [0.7759][4]  | [0.7983][5]  | 77.74 ± 2.2  |
+| bs4-e10-lr0.00015 | [0.7729][6]  | [0.7553][7]  | [0.7526][8]  | [0.7547][9]  | [0.7913][10] | 76.54 ± 1.49 |
+| bs8-e10-lr0.00016 | [0.6638][11] | [0.5875][12] | [0.78][13]   | [0.7804][14] | [0.7176][15] | 70.59 ± 7.34 |
+| bs8-e10-lr0.00015 | [0.6783][16] | [0.5867][17] | [0.7229][18] | [0.7761][19] | [0.697][20]  | 69.22 ± 6.22 |
+
+[1]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-newseye-fi-hmbyt5-bs8-wsFalse-e10-lr0.00015-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 ❤️