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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc	Precision	Recall	F1	F1-macro	F1-micro	F1-weighted
0.5286	0.3960	100	0.4608	0.769	0.855	0.847	0.756	0.799	0.764	0.769	0.771
0.4399	0.7921	200	0.4506	0.787	0.871	0.861	0.775	0.815	0.782	0.787	0.789
0.3929	1.1861	300	0.4363	0.791	0.878	0.829	0.826	0.828	0.781	0.791	0.791
0.368	1.5822	400	0.4003	0.797	0.884	0.881	0.77	0.822	0.794	0.797	0.8
0.3503	1.9782	500	0.4075	0.794	0.888	0.847	0.806	0.826	0.787	0.794	0.795
0.2828	2.3723	600	0.4678	0.798	0.888	0.898	0.753	0.819	0.796	0.798	0.801
0.2647	2.7683	700	0.4296	0.796	0.89	0.893	0.755	0.818	0.793	0.796	0.799
0.2197	3.1624	800	0.5130	0.798	0.889	0.862	0.795	0.827	0.793	0.798	0.8
0.1739	3.5584	900	0.5262	0.8	0.891	0.842	0.826	0.834	0.792	0.8	0.801
0.1517	3.9545	1000	0.6064	0.793	0.889	0.875	0.77	0.819	0.789	0.793	0.795
0.1189	4.3485	1100	0.6257	0.793	0.887	0.848	0.803	0.825	0.786	0.793	0.795
0.0976	4.7446	1200	0.6765	0.796	0.886	0.861	0.793	0.825	0.791	0.796	0.798

adity12345/BioBert_pubHealth

BioBert_pubHealth