twitter_bank_scam_classifier

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

• Loss: 0.6347
• Accuracy: 0.64
• Auc: 0.7

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: 0.001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc
0.7507	1.0	11	0.6825	0.59	0.64
0.6629	2.0	22	0.6347	0.64	0.7

Framework versions

• PEFT 0.16.0
• Transformers 4.53.3
• Pytorch 2.7.1+cu126
• Datasets 4.0.0
• Tokenizers 0.21.2