JoshuaAAX/bert-base-uncased-binary-classification

bert-ia-checkpoint

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

• Loss: 1.7216
• Accuracy: 0.7229
• F1 Macro: 0.6963
• Precision Macro: 0.7200
• Recall Macro: 0.6916
• Auc: 0.7626

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	Auc
No log	1.0	79	0.6736	0.7261	0.7028	0.7210	0.6981	0.7428
No log	2.0	158	0.8024	0.7006	0.6975	0.6995	0.7070	0.7566
No log	3.0	237	0.9896	0.7389	0.7226	0.7307	0.7189	0.7613
No log	4.0	316	1.3463	0.7229	0.7032	0.7145	0.6992	0.7444
No log	5.0	395	1.4706	0.7357	0.7246	0.7256	0.7238	0.7536
No log	6.0	474	1.6432	0.7420	0.7264	0.7339	0.7228	0.7518
0.176	7.0	553	1.7216	0.7229	0.6963	0.7200	0.6916	0.7626
0.176	8.0	632	1.7837	0.7357	0.7078	0.7383	0.7023	0.7596
0.176	9.0	711	1.7627	0.7325	0.7129	0.7256	0.7085	0.7611
0.176	10.0	790	1.7560	0.7357	0.7188	0.7275	0.7149	0.7610

Framework versions

• Transformers 4.57.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1