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	Model Preparation Time	Ter Msl	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	150	0.1841	0.0033	100	0.9454	0.9111	0.8754	0.8365	6.7797
No log	2.0	300	0.1321	0.0033	100	0.9637	0.9393	0.9134	0.8832	4.5078
No log	3.0	450	0.1157	0.0033	100	0.9653	0.9414	0.9167	0.8888	4.3996
0.2566	4.0	600	0.1099	0.0033	100	0.9657	0.9435	0.9195	0.8918	4.0750
0.2566	5.0	750	0.1093	0.0033	100	0.9694	0.9491	0.9275	0.9017	3.6783
0.2566	6.0	900	0.1180	0.0033	100	0.9627	0.9405	0.9170	0.8890	4.3996
0.0331	7.0	1050	0.1049	0.0033	100	0.9722	0.9520	0.9313	0.9071	3.5341
0.0331	8.0	1200	0.1063	0.0033	100	0.9668	0.9479	0.9273	0.9027	3.8586
0.0331	9.0	1350	0.1111	0.0033	100	0.9710	0.9512	0.9294	0.9042	3.6062
0.0165	10.0	1500	0.1093	0.0033	100	0.9713	0.9518	0.9306	0.9050	3.5701
0.0165	11.0	1650	0.1173	0.0033	100	0.9697	0.9490	0.9257	0.8989	3.7865
0.0165	12.0	1800	0.1140	0.0033	100	0.9720	0.9531	0.9320	0.9067	3.4259
0.0165	13.0	1950	0.1147	0.0033	100	0.9544	0.9327	0.9095	0.8812	5.4814
0.0094	14.0	2100	0.1118	0.0033	100	0.9700	0.9493	0.9273	0.9023	3.7144
0.0094	15.0	2250	0.1118	0.0033	100	0.9646	0.9440	0.9217	0.8953	4.2914
0.0094	16.0	2400	0.1099	0.0033	100	0.9701	0.9506	0.9293	0.9043	3.5341
0.0057	17.0	2550	0.1165	0.0033	100	0.9710	0.9520	0.9305	0.9051	3.6423
0.0057	18.0	2700	0.1135	0.0033	100	0.9682	0.9486	0.9271	0.9021	3.8226
0.0057	19.0	2850	0.1165	0.0033	100	0.9710	0.9510	0.9294	0.9046	3.4620
0.0039	20.0	3000	0.1169	0.0033	100	0.9659	0.9466	0.9255	0.9003	4.0389
0.0039	21.0	3150	0.1172	0.0033	100	0.9712	0.9512	0.9289	0.9031	3.6423
0.0039	22.0	3300	0.1194	0.0033	100	0.9685	0.9479	0.9250	0.8984	3.8586
0.0039	23.0	3450	0.1242	0.0033	100	0.9688	0.9477	0.9246	0.8982	3.8226
0.0027	24.0	3600	0.1178	0.0033	100	0.9695	0.9489	0.9265	0.9010	3.6062
0.0027	25.0	3750	0.1177	0.0033	100	0.9698	0.9495	0.9268	0.9006	3.6062
0.0027	26.0	3900	0.1154	0.0033	100	0.9683	0.9482	0.9260	0.9002	3.7144
0.0014	27.0	4050	0.1147	0.0033	100	0.9700	0.9493	0.9266	0.9006	3.6783
0.0014	28.0	4200	0.1123	0.0033	100	0.9710	0.9514	0.9300	0.9053	3.4980
0.0014	29.0	4350	0.1123	0.0033	100	0.9716	0.9525	0.9316	0.9072	3.3898
0.0016	30.0	4500	0.1124	0.0033	100	0.9713	0.9518	0.9304	0.9057	3.4620

Framework versions

Transformers 4.50.2
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/exp1_10partition_modelo_asl6000

exp1_10partition_modelo_asl6000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp1_10partition_modelo_asl6000