This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0102
Accuracy: 0.9987
F1: 0.9987
Precision: 0.9987
Recall: 0.9987
Roc Auc: 0.9987

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: 0.0002
train_batch_size: 16
eval_batch_size: 32
seed: 42
optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
0.0186	1.0	3167	0.0101	0.9986	0.9986	0.9986	0.9986	0.9987
0.0186	2.0	6334	0.0096	0.9988	0.9988	0.9988	0.9988	0.9988
0.0194	3.0	9501	0.0071	0.9991	0.9991	0.9991	0.9991	0.9990
0.018	4.0	12668	0.0101	0.9987	0.9987	0.9987	0.9987	0.9987
0.0198	5.0	15835	0.0102	0.9987	0.9987	0.9987	0.9987	0.9987

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.1.0
Tokenizers 0.21.0

MANMEET75/Payment-NonPayment-distilbert-base-uncased_V2

Payment-NonPayment-distilbert-base-uncased_V2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MANMEET75/
Payment-NonPayment-distilbert-base-uncased_V2