--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: hmteams/teams-base-historic-multilingual-discriminator widget: - text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les tragiques , partont ailleurs qu ’ au premier pied . CÉ . cependant QEd , Roi , 719 , 826 , 4496 . --- # Fine-tuned Flair Model on AjMC French NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md) NER Dataset using hmTEAMS as backbone LM. The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics, and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/) project. The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`. # 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 | [0.8417][1] | [0.8404][2] | [0.8414][3] | [0.8344][4] | [0.8375][5] | 83.91 ± 0.28 | | bs4-e10-lr0.00015 | [0.824][6] | [0.8352][7] | [0.8385][8] | [0.8204][9] | [0.8362][10] | 83.09 ± 0.72 | | bs8-e10-lr0.00016 | [0.801][11] | [0.8155][12] | [0.8248][13] | [0.8292][14] | [0.8462][15] | 82.33 ± 1.5 | | bs8-e10-lr0.00015 | [0.8098][16] | [0.8079][17] | [0.8248][18] | [0.8193][19] | [0.842][20] | 82.08 ± 1.23 | [1]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/hmbench/hmbench-ajmc-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/hmbench/hmbench-ajmc-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 ❤️