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	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	150	1.2803	0.0034	0.6793	0.5456	0.3942	0.2008	47.0772	0.8508	0.7752	0.7072	0.6397	18.5776
No log	2.0	300	1.0222	0.0034	0.7352	0.6454	0.5466	0.4151	42.4843	0.8848	0.8238	0.7679	0.7111	14.2235
No log	3.0	450	0.9065	0.0034	0.7557	0.6691	0.5770	0.4421	37.6827	0.9055	0.8516	0.8027	0.7522	11.9013
1.7579	4.0	600	0.9370	0.0034	0.7106	0.6262	0.5307	0.3905	43.8413	0.9287	0.8872	0.8472	0.8047	9.0711
1.7579	5.0	750	0.8798	0.0034	0.744	0.6483	0.5455	0.3693	37.6827	0.9301	0.8896	0.8513	0.8104	8.8534
1.7579	6.0	900	0.8268	0.0034	0.7777	0.6941	0.5919	0.4367	32.2547	0.9348	0.8988	0.8631	0.8230	8.5631
0.2488	7.0	1050	0.8773	0.0034	0.7458	0.6463	0.5394	0.3955	39.7704	0.9305	0.8931	0.8572	0.8183	8.7808
0.2488	8.0	1200	0.8616	0.0034	0.7766	0.6871	0.5825	0.4373	33.9248	0.9417	0.9095	0.8787	0.8464	7.2569
0.2488	9.0	1350	0.8660	0.0034	0.7496	0.6521	0.5392	0.3802	39.3528	0.9429	0.9116	0.8811	0.8464	7.1118
0.1035	10.0	1500	0.9077	0.0034	0.7380	0.6450	0.5310	0.3551	39.8747	0.9427	0.9122	0.8840	0.8534	7.4746
0.1035	11.0	1650	0.9008	0.0034	0.6960	0.6035	0.5006	0.3589	40.7098	0.9427	0.9110	0.8808	0.8483	7.5472
0.1035	12.0	1800	0.9089	0.0034	0.7222	0.6306	0.5310	0.3713	40.7098	0.9422	0.9117	0.8820	0.8496	7.6197
0.1035	13.0	1950	0.8632	0.0034	0.7502	0.6575	0.5511	0.4091	38.5177	0.9416	0.9090	0.8776	0.8423	7.6923
0.0495	14.0	2100	0.9132	0.0034	0.6990	0.5972	0.4719	0.3275	45.6159	0.9326	0.9003	0.8683	0.8325	8.5631
0.0495	15.0	2250	0.8947	0.0034	0.6974	0.6036	0.4808	0.3461	44.1545	0.9409	0.9097	0.8803	0.8484	7.9826
0.0495	16.0	2400	0.9675	0.0034	0.6752	0.5754	0.4583	0.3035	47.7035	0.9398	0.9100	0.8812	0.8490	7.8374
0.0279	17.0	2550	0.8933	0.0034	0.6144	0.5142	0.4073	0.2809	52.0877	0.9386	0.9080	0.8788	0.8462	7.6923
0.0279	18.0	2700	0.9261	0.0034	0.6965	0.5974	0.4745	0.3043	45.3027	0.9356	0.9043	0.8748	0.8428	8.1277
0.0279	19.0	2850	0.9349	0.0034	0.6877	0.5828	0.4633	0.3246	47.2860	0.9422	0.9120	0.8831	0.8524	7.5472
0.0179	20.0	3000	0.9240	0.0034	0.7345	0.6479	0.5332	0.3636	40.6054	0.9418	0.9131	0.8855	0.8546	7.2569
0.0179	21.0	3150	0.9256	0.0034	0.7046	0.5958	0.4713	0.3168	44.0501	0.9434	0.9137	0.8849	0.8531	7.2569
0.0179	22.0	3300	0.9344	0.0034	0.7389	0.6511	0.5386	0.3806	37.8914	0.9371	0.9054	0.8743	0.8406	8.0552
0.0179	23.0	3450	0.9214	0.0034	0.7286	0.6367	0.5203	0.3562	42.5887	0.9389	0.9089	0.8807	0.8493	7.7649
0.0111	24.0	3600	0.9181	0.0034	0.7315	0.6346	0.5186	0.3594	40.3967	0.9400	0.9101	0.8810	0.8494	7.7649
0.0111	25.0	3750	0.8888	0.0034	0.7639	0.6738	0.5603	0.3930	38.9353	0.9477	0.9188	0.8904	0.8584	6.6763
0.0111	26.0	3900	0.9291	0.0034	0.6978	0.5936	0.4724	0.3117	45.0939	0.9370	0.9055	0.8757	0.8434	8.0552
0.0093	27.0	4050	0.9178	0.0034	0.7327	0.6379	0.5191	0.3538	41.3361	0.9405	0.9109	0.8833	0.8531	7.5472
0.0093	28.0	4200	0.9152	0.0034	0.7329	0.6373	0.5154	0.3502	41.7537	0.9458	0.9166	0.8893	0.8591	7.1843
0.0093	29.0	4350	0.9204	0.0034	0.7399	0.6449	0.5254	0.3587	41.1273	0.9418	0.9124	0.8844	0.8535	7.4746
0.0066	30.0	4500	0.9122	0.0034	0.7397	0.6463	0.5293	0.3629	41.0230	0.9465	0.9179	0.8902	0.8600	6.8940

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/barto_exp1_10partition_modelo_msl6000

barto_exp1_10partition_modelo_msl6000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
barto_exp1_10partition_modelo_msl6000