learning_rate: 4e-05
train_batch_size: 8
eval_batch_size: 8
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: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 4

Training Loss	Epoch	Step	Validation Loss	Rouge1 Precision	Rouge1 Recall	Rouge1 F1	Rouge2 Precision	Rouge2 Recall	Rouge2 F1	Rougel Precision	Rougel Recall	Rougel F1
5.0516	1.0	1205	1.1929	59.7863	59.5963	59.5842	45.0261	44.9939	44.927	54.0853	53.9468	53.9196
1.4427	2.0	2410	1.0989	61.8195	61.8793	61.752	47.117	47.2719	47.1179	55.9932	56.0903	55.9544
1.3238	3.0	3615	1.0684	62.3185	62.3787	62.2513	47.481	47.6336	47.4809	56.5177	56.6169	56.4799
1.2959	4.0	4820	1.0635	62.4589	62.5583	62.4147	47.675	47.8652	47.6954	56.6815	56.8183	56.6653

marcussunderl0040/mt5-small-finetuned-persian-news-summarization-with-additional-hyperparameters-2

mt5-small-finetuned-persian-news-summarization-with-additional-hyperparameters-2