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Nirij3m/
distilbert-synth-vishing

Name: distilbert-synth-vishing
Availability: InStock

distilbert/distilbert-base-uncased

apache-2.0

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distilbert-synth-vishing

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: 0.0826
Accuracy: 0.9702
F1: 0.9765
Precision: 0.9909
Recall: 0.9626

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: 32
eval_batch_size: 16
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
lr_scheduler_warmup_steps: 100
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6093	1.0	99	0.6051	0.6449	0.7841	0.6449	1.0
0.4064	2.0	198	0.3617	0.8338	0.8833	0.8069	0.9758
0.2010	3.0	297	0.2013	0.9304	0.9454	0.9571	0.9339
0.1415	4.0	396	0.1442	0.9446	0.9559	0.9814	0.9317
0.1127	5.0	495	0.1155	0.9545	0.9643	0.9774	0.9515
0.1100	6.0	594	0.0996	0.9616	0.9699	0.9819	0.9581
0.0878	7.0	693	0.0922	0.9659	0.9731	0.9909	0.9559
0.0969	8.0	792	0.0867	0.9688	0.9754	0.9909	0.9604
0.0744	9.0	891	0.0831	0.9702	0.9765	0.9909	0.9626
0.0778	10.0	990	0.0826	0.9702	0.9765	0.9909	0.9626

Framework versions

PEFT 0.19.1
Transformers 5.10.2
Pytorch 2.11.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Nirij3m/distilbert-synth-vishing

distilbert-synth-vishing

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Nirij3m/
distilbert-synth-vishing