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	Bleu 1 Msl	Bleu 2 Msl	Bleu 3 Msl	Bleu 4 Msl	Ter Msl	Ter Asl
No log	1.0	75	2.7914	0.0033	0.1654	0.0940	0.0494	0.0128	87.5996	100
No log	2.0	150	1.8028	0.0033	0.4191	0.2877	0.2019	0.1101	67.6705	100
No log	3.0	225	1.4135	0.0033	0.5865	0.4578	0.3409	0.2456	52.1701	100
No log	4.0	300	1.5030	0.0033	0.5721	0.4443	0.3334	0.2120	50.1329	100
No log	5.0	375	1.4242	0.0033	0.5794	0.4785	0.3862	0.2714	43.9327	100
No log	6.0	450	1.3609	0.0033	0.5858	0.4832	0.3977	0.3091	44.9070	100
1.2827	7.0	525	1.2037	0.0033	0.6417	0.5654	0.4990	0.4047	36.9353	100
1.2827	8.0	600	1.2053	0.0033	0.6609	0.5676	0.4817	0.3729	36.8468	100
1.2827	9.0	675	1.2424	0.0033	0.5820	0.4509	0.3385	0.2104	48.1842	100
1.2827	10.0	750	1.2929	0.0033	0.5659	0.4539	0.3538	0.2321	48.0957	100
1.2827	11.0	825	1.1589	0.0033	0.6350	0.5270	0.4285	0.3150	42.7812	100
1.2827	12.0	900	1.2968	0.0033	0.5602	0.4495	0.3553	0.2490	48.1842	100
1.2827	13.0	975	1.2490	0.0033	0.5767	0.4619	0.3653	0.2593	47.2099	100
0.0822	14.0	1050	1.4277	0.0033	0.6078	0.5244	0.4553	0.3644	42.3384	100
0.0822	15.0	1125	1.4144	0.0033	0.5879	0.4699	0.3753	0.2648	47.6528	100
0.0822	16.0	1200	1.3332	0.0033	0.5653	0.4617	0.3742	0.2684	49.2471	100
0.0822	17.0	1275	1.5239	0.0033	0.5890	0.4859	0.3901	0.2680	44.5527	100
0.0822	18.0	1350	1.3280	0.0033	0.6151	0.5090	0.4173	0.3072	44.0213	100
0.0822	19.0	1425	1.3868	0.0033	0.5928	0.4979	0.4103	0.2937	44.7298	100
0.0271	20.0	1500	1.3441	0.0033	0.6270	0.5304	0.4450	0.3406	41.4526	100
0.0271	21.0	1575	1.3344	0.0033	0.6260	0.5239	0.4272	0.3237	39.6811	100
0.0271	22.0	1650	1.3984	0.0033	0.6113	0.5151	0.4263	0.3290	41.0983	100
0.0271	23.0	1725	1.3501	0.0033	0.6353	0.5335	0.4476	0.3477	42.1612	100
0.0271	24.0	1800	1.3269	0.0033	0.6330	0.5341	0.4477	0.3481	40.9212	100
0.0271	25.0	1875	1.3610	0.0033	0.6220	0.5271	0.4456	0.3457	41.6298	100
0.0271	26.0	1950	1.3784	0.0033	0.6096	0.5116	0.4175	0.2975	43.0469	100
0.0124	27.0	2025	1.3464	0.0033	0.6247	0.5228	0.4319	0.3250	41.9841	100
0.0124	28.0	2100	1.3658	0.0033	0.6151	0.5130	0.4204	0.3111	42.1612	100
0.0124	29.0	2175	1.3549	0.0033	0.6274	0.5252	0.4337	0.3285	41.7183	100
0.0124	30.0	2250	1.3565	0.0033	0.6263	0.5241	0.4336	0.3290	41.3640	100

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/exp5_10partition_modelo_msl3000

exp5_10partition_modelo_msl3000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp5_10partition_modelo_msl3000