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
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	200	0.0011	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	2.0	400	0.0004	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
0.0252	3.0	600	0.0003	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}

Framework versions

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