--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-64k-td-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 64k 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: `[3e-05, 5e-05]` And report micro F1-score on development set: | Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |-------------------|--------------|--------------|--------------|-----------------|--------------|-----------------| | `bs8-e10-lr3e-05` | [0.7869][1] | [0.7909][2] | [0.7897][3] | [0.7868][4] | [0.7836][5] | 0.7876 ± 0.0028 | | `bs4-e10-lr3e-05` | [0.7814][6] | [0.7767][7] | [0.7783][8] | [**0.7747**][9] | [0.7826][10] | 0.7787 ± 0.0033 | | `bs8-e10-lr5e-05` | [0.7761][11] | [0.768][12] | [0.791][13] | [0.7758][14] | [0.7806][15] | 0.7783 ± 0.0084 | | `bs4-e10-lr5e-05` | [0.7714][16] | [0.7733][17] | [0.7723][18] | [0.7739][19] | [0.7746][20] | 0.7731 ± 0.0013 | [1]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-hipe2020-de-hmbert_64k-bs4-wsFalse-e10-lr5e-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 ❤️