learning_rate: 5.6e-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: linear
num_epochs: 8

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.7228	1.0	1559	2.3641	37.9447	18.4166	34.6063	34.6165
2.7319	2.0	3118	2.2304	39.249	19.5698	35.7668	35.7701
2.5555	3.0	4677	2.1440	40.1999	20.1181	36.6426	36.6353
2.4415	4.0	6236	2.1555	40.793	20.7479	37.3027	37.2843
2.3686	5.0	7795	2.1158	41.2065	20.9893	37.7881	37.7686
2.3065	6.0	9354	2.1125	41.3501	21.0604	37.8817	37.8791
2.2729	7.0	10913	2.1053	41.6526	21.3397	38.1622	38.1656
2.2526	8.0	12472	2.1000	41.6338	21.3577	38.202	38.2183

wilsonmarciliojr/mt5-small-finetuned-en-fr-news

mt5-small-finetuned-en-fr-news