distilbert-scam-classifier-v1.1

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

• Loss: 0.0022
• 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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	40	0.0981	{'accuracy': 0.9875}	{'precision': 0.9878048780487806}	{'recall': 0.9875}	{'f1': 0.9874980465697764}
No log	2.0	80	0.0588	{'accuracy': 0.9875}	{'precision': 0.9878048780487806}	{'recall': 0.9875}	{'f1': 0.9874980465697764}
No log	3.0	120	0.0035	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	4.0	160	0.0033	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	5.0	200	0.0029	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	6.0	240	0.0028	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	7.0	280	0.0025	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}
No log	8.0	320	0.0022	{'accuracy': 1.0}	{'precision': 1.0}	{'recall': 1.0}	{'f1': 1.0}

Framework versions

• Transformers 4.40.1
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1