bert_spam_detection

This model is a fine-tuned version of DunnBC22/distilbert-base-uncased-SpamFilter-LG on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1079
• Roc Auc: 0.9951

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: 16
• eval_batch_size: 16
• 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	Roc Auc
0.0113	1.0	814	0.0974	0.9948
0.0009	2.0	1628	0.1079	0.9951

Framework versions

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