multibert1110_lrate7.5b4

This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7163
  • Precisions: 0.8864
  • Recall: 0.8013
  • F-measure: 0.8374
  • Accuracy: 0.9059

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: 7.5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 14

Training results

Training Loss Epoch Step Validation Loss Precisions Recall F-measure Accuracy
0.7304 1.0 942 0.4905 0.8049 0.6436 0.6549 0.8554
0.4336 2.0 1884 0.6035 0.8585 0.6334 0.6863 0.8477
0.3238 3.0 2826 0.5094 0.8668 0.7014 0.7232 0.8882
0.249 4.0 3768 0.5951 0.8798 0.7110 0.7609 0.8770
0.191 5.0 4710 0.4988 0.8304 0.7761 0.7816 0.8975
0.1513 6.0 5652 0.5998 0.8351 0.7917 0.8062 0.8962
0.1088 7.0 6594 0.5874 0.8427 0.7953 0.8158 0.9003
0.0914 8.0 7536 0.5529 0.8580 0.7885 0.8087 0.9069
0.0682 9.0 8478 0.6882 0.8371 0.7773 0.8024 0.8958
0.0487 10.0 9420 0.7163 0.8864 0.8013 0.8374 0.9059
0.0319 11.0 10362 0.7020 0.8724 0.7867 0.8235 0.9007
0.0305 12.0 11304 0.6886 0.8689 0.8002 0.8311 0.9079
0.0184 13.0 12246 0.6994 0.8680 0.8089 0.8357 0.9085
0.0138 14.0 13188 0.7183 0.8677 0.8105 0.8362 0.9093

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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