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bert-mapa-german

This model is a fine-tuned version of google-bert/bert-base-german-cased on the MAPA german dataset. It's purpose is to discern private information within German texts.

It achieves the following results on the test set:

Category Precision Recall F1 Number
Address 0.5882 0.6667 0.625 15
Age 0.0 0.0 0.0 3
Amount 1.0 1.0 1.0 1
Date 0.9455 0.9455 0.9455 55
Name 0.7 0.9545 0.8077 22
Organisation 0.5405 0.6452 0.5882 31
Person 0.5385 0.5 0.5185 14
Role 0.0 0.0 0.0 1
Overall 0.7255 0.7817 0.7525
  • Loss: 0.0325
  • Overall Accuracy: 0.9912

Intended uses & limitations

This model is engineered for the purpose of discerning private information within German texts. Its training corpus comprises only 1744 example sentences, thereby leading to a higher frequency of errors in its predictions.

Training and evaluation data

Random split of the MAPA german dataset into 80% train, 10% valdiation and 10% test.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Overall Precision Overall Recall Overall F1 Overall Accuracy
No log 1.0 218 0.0607 0.6527 0.7786 0.7101 0.9859
No log 2.0 436 0.0479 0.7355 0.8143 0.7729 0.9896
0.116 3.0 654 0.0414 0.7712 0.8429 0.8055 0.9908
0.116 4.0 872 0.0421 0.7857 0.8643 0.8231 0.9917

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1
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