distilbert-base-uncased-finetuned-spam-detection-dataset-splits

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

• Loss: 0.0203
• Accuracy: 0.9963
• F1: 0.9963

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: 64
• eval_batch_size: 64
• 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	F1
0.0954	1.0	128	0.0204	0.9963	0.9963
0.0051	2.0	256	0.0203	0.9963	0.9963

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1