metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: dbmdz/bert-base-german-cased
model-index:
  - name: bert-german-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: validation
          args: conll2003
        metrics:
          - type: precision
            value: 0.8333588604686782
            name: Precision
          - type: recall
            value: 0.8620088719898605
            name: Recall
          - type: f1
            value: 0.8474417880227396
            name: F1
          - type: accuracy
            value: 0.9292245320451997
            name: Accuracy

bert-german-ner

This model is a fine-tuned version of dbmdz/bert-base-german-cased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.3196
Precision: 0.8334
Recall: 0.8620
F1: 0.8474
Accuracy: 0.9292

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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	300	0.3617	0.7310	0.7733	0.7516	0.8908
0.5428	2.0	600	0.2897	0.7789	0.8395	0.8081	0.9132
0.5428	3.0	900	0.2805	0.8147	0.8465	0.8303	0.9221
0.2019	4.0	1200	0.2816	0.8259	0.8498	0.8377	0.9260
0.1215	5.0	1500	0.2942	0.8332	0.8599	0.8463	0.9285
0.1215	6.0	1800	0.3053	0.8293	0.8619	0.8452	0.9287
0.0814	7.0	2100	0.3190	0.8249	0.8634	0.8437	0.9267
0.0814	8.0	2400	0.3196	0.8334	0.8620	0.8474	0.9292

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2