learning_rate: 0.0001
train_batch_size: 32
eval_batch_size: 64
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: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu Msl	Bleu 1 Msl	Bleu 2 Msl	Bleu 3 Msl	Bleu 4 Msl	Ter Msl	Ter Asl
No log	1.0	67	0.4200	42.7287	0.6604	0.0111	0.0030	0.0014	100	100
No log	2.0	134	0.3958	50.8133	0.7186	0.0116	0.0031	0.0015	100	100
No log	3.0	201	0.3869	70.7107	0.7317	0.0117	0.0031	0.0015	100	100
No log	4.0	268	0.3764	70.7107	0.7336	0.0117	0.0031	0.0015	100	100
No log	5.0	335	0.3818	50.8133	0.7261	0.0117	0.0031	0.0015	100	100
No log	6.0	402	0.3615	50.8133	0.7467	0.0118	0.0032	0.0015	100	100
No log	7.0	469	0.3790	50.8133	0.7336	0.0117	0.0031	0.0015	100	100
0.1217	8.0	536	0.3946	50.0000	0.7411	0.0118	0.0031	0.0015	100	100
0.1217	9.0	603	0.4057	45.1801	0.7448	0.0118	0.0032	0.0015	100	100
0.1217	10.0	670	0.3968	70.7107	0.7392	0.0118	0.0031	0.0015	100	100
0.1217	11.0	737	0.4003	70.7107	0.7411	0.0118	0.0031	0.0015	100	100
0.1217	12.0	804	0.3982	70.7107	0.7561	0.0119	0.0032	0.0015	100	100
0.1217	13.0	871	0.4016	70.7107	0.7486	0.0119	0.0032	0.0015	100	100
0.1217	14.0	938	0.4065	50.8133	0.7542	0.0119	0.0032	0.0015	100	100
0.0287	15.0	1005	0.3989	50.0000	0.7561	0.0119	0.0032	0.0015	100	100
0.0287	16.0	1072	0.4009	50.0000	0.7523	0.0119	0.0032	0.0015	100	100
0.0287	17.0	1139	0.4154	50.0000	0.7505	0.0119	0.0032	0.0015	100	100
0.0287	18.0	1206	0.4111	70.7107	0.7411	0.0118	0.0031	0.0015	100	100
0.0287	19.0	1273	0.4163	70.7107	0.7448	0.0118	0.0032	0.0015	100	100
0.0287	20.0	1340	0.4134	70.7107	0.7542	0.0119	0.0032	0.0015	100	100
0.0287	21.0	1407	0.4165	70.7107	0.7542	0.0119	0.0032	0.0015	100	100
0.0287	22.0	1474	0.4120	50.0000	0.7486	0.0119	0.0032	0.0015	100	100
0.0132	23.0	1541	0.4199	50.0000	0.7467	0.0118	0.0032	0.0015	100	100
0.0132	24.0	1608	0.4200	50.0000	0.7523	0.0119	0.0032	0.0015	100	100
0.0132	25.0	1675	0.4226	70.7107	0.7523	0.0119	0.0032	0.0015	100	100
0.0132	26.0	1742	0.4199	70.7107	0.7542	0.0119	0.0032	0.0015	100	100
0.0132	27.0	1809	0.4216	70.7107	0.7523	0.0119	0.0032	0.0015	100	100
0.0132	28.0	1876	0.4225	50.8133	0.7542	0.0119	0.0032	0.0015	100	100
0.0132	29.0	1943	0.4232	50.8133	0.7523	0.0119	0.0032	0.0015	100	100
0.0087	30.0	2010	0.4235	50.8133	0.7523	0.0119	0.0032	0.0015	100	100

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0

vania2911/2661

2661

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
2661