--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-tiny-historic-multilingual-cased widget: - text: Es war am 25sten , als Lord Corn wollis Dublin mit seinem Gefolge und mehrern Truppen verließ , um in einer Central - Lage bey Sligo die Operationen der Armee persönlich zu dirigiren . Der Feind dürfte bald in die Enge kommen , da Gen . Lacke mit 6000 Mann ihm entgegen marschirt . --- # Fine-tuned Flair Model on German HIPE-2020 Dataset (HIPE-2022) This Flair model was fine-tuned on the [German HIPE-2020](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md) NER Dataset using hmBERT Tiny as backbone LM. The HIPE-2020 dataset is comprised of newspapers from mid 19C to mid 20C. For information can be found [here](https://dl.acm.org/doi/abs/10.1007/978-3-030-58219-7_21). The following NEs were annotated: `loc`, `org`, `pers`, `prod`, `time` and `comp`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[4, 8]` * Learning Rates: `[5e-05, 3e-05]` And report micro F1-score on development set: | Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |-------------------|------------------|--------------|--------------|--------------|--------------|-----------------| | `bs4-e10-lr5e-05` | [0.3947][1] | [0.398][2] | [0.3655][3] | [0.3703][4] | [0.3858][5] | 0.3829 ± 0.0145 | | `bs8-e10-lr5e-05` | [0.3744][6] | [0.3819][7] | [0.3486][8] | [0.3506][9] | [0.3645][10] | 0.364 ± 0.0145 | | `bs4-e10-lr3e-05` | [0.3415][11] | [0.3579][12] | [0.3291][13] | [0.3351][14] | [0.3549][15] | 0.3437 ± 0.0124 | | `bs8-e10-lr3e-05` | [**0.3282**][16] | [0.336][17] | [0.3138][18] | [0.3172][19] | [0.3422][20] | 0.3275 ± 0.0121 | [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) 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 ❤️