stefan-it/hmbench-newseye-fi-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4

Fine-tuned Flair Model on Finnish NewsEye NER Dataset (HIPE-2022)

This Flair model was fine-tuned on the Finnish NewsEye NER Dataset using hmBERT 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.

The following NEs were annotated: PER, LOC, ORG and HumanProd.

Results

We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:

• Batch Sizes: [8, 4]
• Learning Rates: [3e-05, 5e-05]

And report micro F1-score on development set:

Configuration	Run 1	Run 2	Run 3	Run 4	Run 5	Avg.
bs4-e10-lr3e-05	0.7669	0.8009	0.7722	0.7653	0.7579	77.26 ± 1.49
bs8-e10-lr5e-05	0.7837	0.7447	0.778	0.7702	0.7666	76.86 ± 1.34
bs4-e10-lr5e-05	0.7856	0.7722	0.7484	0.7619	0.7556	76.47 ± 1.3
bs8-e10-lr3e-05	0.7669	0.7436	0.766	0.7716	0.7328	75.62 ± 1.52

The training log and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub.

More information about fine-tuning can be found here.

Acknowledgements

We thank Luisa März, Katharina Schmid and Erion Çano for their fruitful discussions about Historic Language Models.

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). Many Thanks for providing access to the TPUs ❤️