learning_rate: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use adamw_torch 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: 5
num_epochs: 3

Training Loss	Epoch	Step	F1 Micro	F1 Macro	Precision At 5	Recall At 5	Psp At 5	Precision At 8	Recall At 8	Psp At 8	Precision At 15	Recall At 15	Psp At 15	Precision At 25	Recall At 25	Psp At 25	Rare F1 Micro	Rare F1 Macro	Rare Precision	Rare Recall	Rare Precision At 5	Rare Recall At 5	Rare Precision At 8	Rare Recall At 8	Rare Precision At 15	Rare Recall At 15	Rare Precision At 25	Rare Recall At 25	Not Rare F1 Micro	Not Rare F1 Macro	Not Rare Precision	Not Rare Recall	Not Rare Precision At 5	Not Rare Recall At 5	Not Rare Precision At 8	Not Rare Recall At 8	Not Rare Precision At 15	Not Rare Recall At 15	Not Rare Precision At 25	Not Rare Recall At 25	Validation Loss
-3.5077	1.0	10850	0.1363	0.0081	0.4242	0.1163	0.1328	0.3148	0.1359	0.1194	0.1935	0.1539	0.1045	0.1237	0.1625	0.0986	0.0038	0.0010	0.0348	0.0020	0.0028	0.0073	0.0018	0.0076	0.0010	0.0079	0.0006	0.0083	0.1439	0.0129	0.5919	0.0819	0.4252	0.1240	0.3157	0.1448	0.1935	0.1633	0.1231	0.1715	-3.3029
-4.2213	2.0	21700	0.0247	0.0014	0.0708	0.0181	0.0211	0.0465	0.0189	0.0166	0.0262	0.0199	0.0132	0.0164	0.0208	0.0124	0.0000	0.0001	0.0312	0.0000	0.0002	0.0006	0.0001	0.0006	0.0001	0.0007	0.0001	0.0010	0.0262	0.0023	0.7033	0.0134	0.0710	0.0192	0.0467	0.0201	0.0259	0.0208	0.0158	0.0212	-3.3665
-4.3344	2.9998	32547	0.0057	0.0005	0.0146	0.0038	0.0048	0.0099	0.0041	0.0040	0.0062	0.0047	0.0037	0.0043	0.0055	0.0039	0.0	0.0	0.0	0.0	0.0001	0.0004	0.0001	0.0005	0.0001	0.0006	0.0001	0.0008	0.0060	0.0008	0.5636	0.0030	0.0147	0.0040	0.0102	0.0044	0.0059	0.0048	0.0037	0.0050	-3.3437

deb101/llama-8b-wiki10-31k-adapt-multilabel-classify

llama-8b-wiki10-31k-adapt-multilabel-classify