ner-bert-german

This model can be used to do named-entity recognition in German. It is trained on a fine-tuned version of bert-base-multilingual-cased on the German wikiann dataset.

It achieves the following results on the evaluation set:

• Loss: 0.2450
• Overall Precision: 0.8767
• Overall Recall: 0.8893
• Overall F1: 0.8829
• Overall Accuracy: 0.9606
• Loc F1: 0.9067
• Org F1: 0.8278
• Per F1: 0.9152

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Loc F1	Org F1	Per F1
0.252	0.8	1000	0.1724	0.8422	0.8368	0.8395	0.9501	0.8702	0.7593	0.8921
0.1376	1.6	2000	0.1679	0.8388	0.8607	0.8497	0.9528	0.8814	0.7712	0.8971
0.0982	2.4	3000	0.1880	0.8631	0.8598	0.8614	0.9564	0.8847	0.7915	0.9070
0.0681	3.2	4000	0.1956	0.8599	0.8775	0.8686	0.9574	0.8905	0.8084	0.9097
0.0477	4.0	5000	0.2115	0.8738	0.8814	0.8776	0.9593	0.9003	0.8207	0.9144
0.031	4.8	6000	0.2274	0.8751	0.8826	0.8788	0.9598	0.9017	0.8246	0.9115
0.0229	5.6	7000	0.2317	0.8715	0.8888	0.8801	0.9598	0.9061	0.8208	0.9145
0.0181	6.4	8000	0.2450	0.8767	0.8893	0.8829	0.9606	0.9067	0.8278	0.9152

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.1
• Datasets 2.8.0
• Tokenizers 0.13.2