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ner-bert-german

Token Classification
Transformers
PyTorch
Safetensors
German
bert
Generated from Trainer
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ner-bert-german / README.md
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metadata
license: apache-2.0
language: de
tags:
  - generated_from_trainer
datasets:
  - wikiann
model-index:
  - name: ner-bert-german
    results: []
examples: null
widget:
  - text: Herr Schmidt lebt in Berlin und arbeitet für die UN.
    example_title: Schmidt aus Berlin
  - text: Die Deutsche Bahn hat ihren Hauptsitz in Frankfurt.
    example_title: Deutsche Bahn
  - text: In München gibt es viele Unternehmen, z.B. BMW und Siemens.
    example_title: München
metrics:
  - seqeval

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