learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
num_epochs: 50

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro	Rouge1	Rougel	Rougelsum
No log	0	0	1.1169	0	2.7925	0.4884	0.3379	0.4884	0.4884	0.4884
No log	1	2104	0.8378	0.0078	10.0779	0.5093	0.3374	0.5093	0.5093	0.5081
No log	2	4208	0.7417	0.0156	27.8648	0.5266	0.4047	0.5266	0.5266	0.5266
0.0196	3	6312	0.7644	0.0312	56.2378	0.5799	0.5023	0.5799	0.5799	0.5787
0.4924	4	8416	0.4683	0.0625	88.4913	0.7928	0.7927	0.7917	0.7928	0.7940
0.4069	5	10520	0.5063	0.125	121.6539	0.7836	0.7799	0.7830	0.7847	0.7824
0.3124	6	12624	0.4481	0.25	216.3907	0.8171	0.8171	0.8160	0.8160	0.8177
0.2551	7	14728	0.4696	0.5	421.9573	0.8044	0.8039	0.8044	0.8044	0.8044
0.2074	8.0	16832	0.5031	1.0	822.0675	0.7917	0.7906	0.7917	0.7917	0.7917
0.1439	9.0	18936	0.6701	1.0	816.7059	0.7836	0.7830	0.7818	0.7836	0.7836
0.1038	10.0	21040	0.7750	1.0	817.6334	0.7859	0.7857	0.7847	0.7859	0.7859

contemmcm/33b457a1c91e57d4de1193ccbdd427dd

33b457a1c91e57d4de1193ccbdd427dd