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: 0.0368
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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	40	0.1258	{'accuracy': 0.9875}	{'precision': 0.9878048780487806}	{'recall': 0.9875}	{'f1': 0.9874980465697764}
No log	2.0	80	0.0368	{'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

BothBosu/distilbert-scam-classifier-v1.2

distilbert-scam-classifier-v1.2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BothBosu/
distilbert-scam-classifier-v1.2