learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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
lr_scheduler_warmup_steps: 100
num_epochs: 5

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bertscore Precision	Bertscore Recall	Bertscore F1	Gen Len
3.0413	1.0	144	2.0856	0.1652	0.0878	0.15	0.1502	0.7758	0.6413	0.7017	20.9917
2.8167	2.0	288	1.8682	0.1726	0.0867	0.1556	0.1555	0.7745	0.6437	0.7026	21.0
2.6382	3.0	432	1.8175	0.1654	0.0859	0.1522	0.1521	0.7715	0.6408	0.6996	21.0
2.6705	4.0	576	1.7983	0.1664	0.0885	0.1542	0.1541	0.7713	0.6397	0.6988	21.0

aku47z/mt5-small-nepali-final

mt5-small-nepali-final