bert-scam-classifier-v1.6

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

• Loss: 0.0001
• 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	160	0.0325	{'accuracy': 0.990625}	{'precision': 0.99079754601227}	{'recall': 0.990625}	{'f1': 0.9906241759529646}
No log	2.0	320	0.0002	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	3.0	480	0.0001	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
0.0367	4.0	640	0.0001	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1