ner-bert-german

mschiesser/ner-bert-german

ner-bert-german is a token classification model from mschiesser. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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wikiann

Model card

From the Hugging Face model README

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

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Languages: de
Created: Jan 16, 2023
Updated: Mar 23, 2023