learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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: 10
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.2134	1.0	921	1.8395	0.4067	0.1789	0.3387	0.3392	16.9731
1.9967	2.0	1842	1.7883	0.4111	0.1859	0.3447	0.3451	16.9939
1.9323	3.0	2763	1.7518	0.4228	0.1912	0.3547	0.3549	17.2164
1.8926	4.0	3684	1.7444	0.425	0.1989	0.3576	0.3578	17.3166
1.8661	5.0	4605	1.7257	0.4266	0.1992	0.3607	0.361	17.0795
1.839	6.0	5526	1.7169	0.4318	0.2017	0.3614	0.3616	17.4132
1.8294	7.0	6447	1.7098	0.4317	0.2061	0.3662	0.3666	17.2848
1.8135	8.0	7368	1.7058	0.4329	0.2068	0.3657	0.3659	17.2812
1.8111	9.0	8289	1.7040	0.4324	0.2074	0.3666	0.3669	17.2213
1.8057	10.0	9210	1.7032	0.4349	0.2083	0.3675	0.368	17.2885

prabhleenbindra-personal/finetuned-t5-for-samsum-summarization

finetuned-t5-for-samsum-summarization