kugler's picture
kugler/bert-base-german-cased-amdi-synset
113d4ae verified
metadata
library_name: transformers
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: bert-base-german-cased-amdi-synset
    results: []

bert-base-german-cased-amdi-synset

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5931
  • Accuracy: 0.8296
  • F1: 0.6552
  • Precision: 0.6671
  • Recall: 0.6838

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
3.8594 0.3049 50 3.1098 0.3546 0.1123 0.1516 0.1536
2.0232 0.6098 100 1.3093 0.6799 0.3477 0.3439 0.4111
1.2061 0.9146 150 1.0550 0.7005 0.4147 0.4034 0.4774
0.8326 1.2195 200 0.8302 0.7504 0.4769 0.5008 0.5181
0.7385 1.5244 250 0.7518 0.7659 0.5069 0.5359 0.5543
0.6504 1.8293 300 0.7083 0.7762 0.5155 0.4996 0.5648
0.6269 2.1341 350 0.6032 0.8176 0.5909 0.5914 0.6244
0.4735 2.4390 400 0.6070 0.8090 0.6165 0.6480 0.6377
0.4269 2.7439 450 0.6315 0.8090 0.6380 0.6571 0.6666
0.4783 3.0488 500 0.5931 0.8296 0.6552 0.6671 0.6838
0.3407 3.3537 550 0.5612 0.8382 0.6595 0.6541 0.6934
0.3022 3.6585 600 0.5809 0.8262 0.6694 0.6828 0.6933
0.3161 3.9634 650 0.5659 0.8434 0.6834 0.6953 0.7053
0.2405 4.2683 700 0.6109 0.8382 0.6643 0.6651 0.6965
0.2201 4.5732 750 0.5762 0.8485 0.6880 0.6913 0.7115
0.2188 4.8780 800 0.5860 0.8485 0.6875 0.6911 0.7129
0.16 5.1829 850 0.6092 0.8399 0.6630 0.6681 0.6882
0.1456 5.4878 900 0.6303 0.8417 0.6646 0.6718 0.6873
0.1718 5.7927 950 0.6210 0.8468 0.6703 0.6734 0.6947

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.20.3