readme: add initial version (#1)

- readme: add initial version (d835c6b4e42eb054b99c3f8e3bcb8e4efd82c7c3)

README.md

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+---
+language: fr
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax
+inference: false
+widget:
+- text: 'Parmi les remèdes recommandés par la Société , il faut mentionner celui que
+    M . Schatzmann , de Lausanne , a proposé :'
+---
+
+# Fine-tuned Flair Model on LeTemps French NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[LeTemps French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md)
+NER Dataset using hmByT5 as backbone LM.
+
+The LeTemps dataset consists of NE-annotated historical French newspaper articles from mid-19C to mid 20C.
+
+The following NEs were annotated: `loc`, `org` and `pers`.
+
+# ⚠️ 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.         |
+|-------------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| bs8-e10-lr0.00016 | [0.6553][1]  | [0.6628][2]  | [0.6699][3]  | [0.6524][4]  | [0.6542][5]  | 65.89 ± 0.65 |
+| bs4-e10-lr0.00015 | [0.6603][6]  | [0.6651][7]  | [0.654][8]   | [0.6575][9]  | [0.6575][10] | 65.89 ± 0.37 |
+| bs4-e10-lr0.00016 | [0.6423][11] | [0.6595][12] | [0.6625][13] | [0.6657][14] | [0.6538][15] | 65.68 ± 0.82 |
+| bs8-e10-lr0.00015 | [0.6502][16] | [0.6541][17] | [0.6607][18] | [0.6496][19] | [0.6629][20] | 65.55 ± 0.54 |
+
+[1]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-letemps-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-letemps-fr-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 ❤️