learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Mcc	Auc	Specificity	Sensitivity	True Positives	True Negatives	False Positives	False Negatives
0.4019	1.0	71	0.4525	0.8292	0.8868	0.7231	0.7966	0.6621	0.8957	0.9205	0.7231	94	139	12	36
0.3181	2.0	142	0.4287	0.8399	0.8102	0.8538	0.8315	0.6800	0.8993	0.8278	0.8538	111	125	26	19
0.2571	3.0	213	0.4333	0.8292	0.8661	0.7462	0.8017	0.6587	0.9017	0.9007	0.7462	97	136	15	33
0.2318	4.0	284	0.4991	0.8327	0.8487	0.7769	0.8112	0.6637	0.9047	0.8808	0.7769	101	133	18	29
0.1753	5.0	355	0.4902	0.8399	0.8295	0.8231	0.8263	0.6778	0.9040	0.8543	0.8231	107	129	22	23

align-ric/model

deberta-v3-threat-detection