spam-detection_m1

This model is a fine-tuned version of google-bert/bert-base-uncased on an spam-detection dataset. It achieves the following results on the evaluation set:

• Loss: 0.0202
• Accuracy: 0.9967

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: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	256	0.1144	0.9919
0.22	2.0	512	0.0483	0.9923
0.22	3.0	768	0.0321	0.9949
0.0361	4.0	1024	0.0275	0.9949
0.0361	5.0	1280	0.0245	0.9952
0.0233	6.0	1536	0.0232	0.9960
0.0233	7.0	1792	0.0220	0.9967
0.0171	8.0	2048	0.0209	0.9967
0.0171	9.0	2304	0.0211	0.9967
0.0148	10.0	2560	0.0202	0.9967

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0