learning_rate: 0.0056
train_batch_size: 12
eval_batch_size: 12
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: 20

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
5.9607	1.0	100	4.8449	0.1763	0.0684	0.1763	0.1761
4.9088	2.0	200	3.9878	0.3076	0.1348	0.2803	0.2815
2.9924	3.0	300	2.2397	0.2790	0.1378	0.2575	0.2592
2.2734	4.0	400	1.9866	0.2987	0.1629	0.2868	0.2872
1.9431	5.0	500	1.7408	0.2251	0.1380	0.2231	0.2237
2.317	6.0	600	1.9235	0.2421	0.0922	0.2276	0.2282
1.8526	7.0	700	1.6342	0.3120	0.1636	0.2943	0.2944
1.7029	8.0	800	1.6244	0.2469	0.1361	0.2421	0.2427
1.6725	9.0	900	1.5803	0.2637	0.1362	0.2551	0.2560
1.5852	10.0	1000	1.5617	0.2963	0.1634	0.2907	0.2917
1.4625	11.0	1100	1.4049	0.2750	0.1383	0.2570	0.2576
1.3895	12.0	1200	1.4234	0.2969	0.1646	0.2917	0.2927
1.3584	13.0	1300	1.3807	0.3370	0.1601	0.3088	0.3099
1.2759	14.0	1400	1.3524	0.2890	0.1307	0.2654	0.2663
1.222	15.0	1500	1.3110	0.2718	0.1339	0.2566	0.2597
1.1515	16.0	1600	1.2297	0.3314	0.1626	0.3033	0.3038
1.0888	17.0	1700	1.1897	0.3028	0.1358	0.2769	0.2792
1.039	18.0	1800	1.1970	0.2833	0.1521	0.2758	0.2768
0.9907	19.0	1900	1.1790	0.2833	0.1521	0.2758	0.2768
0.9563	20.0	2000	1.1778	0.2833	0.1521	0.2758	0.2768

ak2603/mt5-small-mt5-finetuned-final

mt5-small-mt5-finetuned-final