learning_rate: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
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_steps: 50
num_epochs: 4

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
5.7900	0.2427	200	0.6736	0.6495	0.4193
4.9944	0.4854	400	0.6474	0.6610	0.4330
4.9430	0.7282	600	0.7358	0.6457	0.5942
4.7595	0.9709	800	0.6601	0.6762	0.6694
4.6664	1.2136	1000	0.6659	0.6724	0.6682
4.3585	1.4563	1200	0.6974	0.68	0.6692
4.4865	1.6990	1400	0.6515	0.6857	0.6744
4.4832	1.9417	1600	0.6747	0.6876	0.6751
4.5484	2.1845	1800	0.6741	0.68	0.6687
4.4410	2.4272	2000	0.6652	0.6876	0.6824
4.3377	2.6699	2200	0.6780	0.6857	0.6734
4.2296	2.9126	2400	0.6830	0.6857	0.6793
3.9448	3.1553	2600	0.6907	0.6952	0.6874
3.9506	3.3981	2800	0.7021	0.6876	0.6751
3.8357	3.6408	3000	0.6994	0.6952	0.6882
4.2035	3.8835	3200	0.6913	0.6933	0.6890
4.2035	4.0	3296	0.6909	0.6933	0.6887

CureLink/curelink-biomed-nli-v5

curelink-biomed-nli-v5