bert-fraud-classification-test

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

• Loss: 0.3850
• F1: 0.7886
• Precision: 0.8400
• Val Accuracy: 0.8429

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: 5e-05
• train_batch_size: 44
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 88
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Val Accuracy
0.5784	0.1386	40	0.4995	0.6845	0.8251	0.7873
0.4545	0.2773	80	0.4295	0.7430	0.8504	0.8200
0.4566	0.4159	120	0.4116	0.7564	0.8483	0.8266
0.4468	0.5546	160	0.4149	0.7366	0.8827	0.8217
0.3454	0.6932	200	0.4062	0.7442	0.8812	0.8254
0.3333	0.8319	240	0.4046	0.7475	0.8993	0.8296
0.4618	0.9705	280	0.3973	0.7797	0.8279	0.8358
0.3581	1.1092	320	0.3869	0.7843	0.8431	0.8410
0.3946	1.2478	360	0.3869	0.7823	0.8471	0.8405
0.3263	1.3865	400	0.3875	0.7850	0.8379	0.8405
0.31	1.5251	440	0.3907	0.7721	0.8835	0.8404
0.2547	1.6638	480	0.3822	0.7855	0.8561	0.8437
0.2613	1.8024	520	0.3886	0.7883	0.8346	0.8418
0.3142	1.9411	560	0.3850	0.7886	0.8400	0.8429

Framework versions

• Transformers 4.46.0.dev0
• Pytorch 2.4.0+cu121
• Datasets 3.0.1
• Tokenizers 0.20.0