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bert-fraud-classification-test

This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6431
  • F1: 0.7435
  • Precision: 0.6605
  • Val Accuracy: {'accuracy': 0.706}

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss F1 Precision Val Accuracy
0.601 0.32 40 0.6152 0.7039 0.6597 {'accuracy': 0.682}
0.6332 0.64 80 0.6143 0.7068 0.6515 {'accuracy': 0.679}
0.4862 0.96 120 0.5791 0.7151 0.7137 {'accuracy': 0.714}
0.6297 1.28 160 0.6323 0.7281 0.6495 {'accuracy': 0.69}
0.6164 1.6 200 0.5010 0.7522 0.8345 {'accuracy': 0.774}
0.6333 1.92 240 0.5824 0.7310 0.6828 {'accuracy': 0.71}
0.4465 2.24 280 0.5335 0.7579 0.7695 {'accuracy': 0.761}
0.5342 2.56 320 0.5065 0.7644 0.8040 {'accuracy': 0.775}
0.5462 2.88 360 0.6431 0.7435 0.6605 {'accuracy': 0.706}

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.4.0+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1
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