readme: add initial version

README.md

@@ -0,0 +1,87 @@
+---
+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: Le Moniteur universel fait ressortir les avantages de la situation de l '
+    Allemagne , sa force militaire , le peu d ' intérêts personnels qu ' elle peut
+    avoir dans la question d ' Orient .
+---
+
+# Fine-tuned Flair Model on French NewsEye NER Dataset (HIPE-2022)
+
+This Flair model was fine-tuned on the
+[French 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 | [0.793][1]   | [0.803][2]   | [0.8054][3]  | [0.8069][4]  | [0.8133][5]  | 80.43 ± 0.66 |
+| bs8-e10-lr0.00016 | [0.7888][6]  | [0.8094][7]  | [0.8043][8]  | [0.8011][9]  | [0.8117][10] | 80.31 ± 0.8  |
+| bs4-e10-lr0.00015 | [0.7884][11] | [0.8109][12] | [0.8005][13] | [0.8083][14] | [0.8022][15] | 80.21 ± 0.78 |
+| bs8-e10-lr0.00015 | [0.788][16]  | [0.8003][17] | [0.8067][18] | [0.8035][19] | [0.8064][20] | 80.1 ± 0.69  |
+
+[1]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[2]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[3]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[4]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[5]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[6]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
+[7]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
+[8]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
+[9]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
+[10]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
+[11]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[12]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[13]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[14]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[15]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
+[16]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
+[17]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
+[18]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
+[19]: https://hf.co/hmbench/hmbench-newseye-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
+[20]: https://hf.co/hmbench/hmbench-newseye-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 ❤️