tenghowtan/fraud-model

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fraud-model

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

• —Loss: 0.0003
• —Accuracy: {'accuracy': 1.0}
• —Precision: {'precision': 1.0}
• —Recall: {'recall': 1.0}
• —F1: {'f1': 1.0}

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
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 3

Training results

Framework versions

• —Transformers 4.57.1
• —Pytorch 2.8.0+cu126
• —Datasets 4.0.0
• —Tokenizers 0.22.1