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	1.7039	0.0032	0.1241	0.0887	0.0609	0.0353	186.5717	100
No log	2.0	150	1.2533	0.0032	0.0633	0.0461	0.0327	0.0198	374.9746	100
No log	3.0	225	1.1708	0.0032	0.7083	0.5967	0.4821	0.3334	35.0966	100
No log	4.0	300	1.1708	0.0032	0.6275	0.5313	0.4212	0.2860	33.6724	100
No log	5.0	375	1.2124	0.0032	0.6280	0.5237	0.4195	0.2871	35.4018	100
No log	6.0	450	1.2659	0.0032	0.7330	0.6390	0.5212	0.3616	32.2482	100
0.5393	7.0	525	1.2558	0.0032	0.7346	0.6547	0.5445	0.3905	31.2309	100
0.5393	8.0	600	1.2707	0.0032	0.7318	0.6494	0.5454	0.3867	31.7396	100
0.5393	9.0	675	1.2536	0.0032	0.7497	0.6642	0.5502	0.3825	30.0102	100
0.5393	10.0	750	1.1947	0.0032	0.7477	0.6674	0.5470	0.3748	29.8067	100
0.5393	11.0	825	1.3127	0.0032	0.7238	0.6425	0.5318	0.3704	31.6378	100
0.5393	12.0	900	1.2285	0.0032	0.7659	0.6718	0.5562	0.3969	29.0946	100
0.5393	13.0	975	1.2194	0.0032	0.7558	0.6752	0.5585	0.4031	28.6877	100
0.0401	14.0	1050	1.2710	0.0032	0.7419	0.6661	0.5555	0.3996	30.2136	100
0.0401	15.0	1125	1.2230	0.0032	0.7714	0.6850	0.5688	0.4073	27.5687	100
0.0401	16.0	1200	1.2397	0.0032	0.7654	0.6854	0.5685	0.4114	28.9929	100
0.0401	17.0	1275	1.2646	0.0032	0.7694	0.6900	0.5772	0.4145	27.6704	100
0.0401	18.0	1350	1.2835	0.0032	0.7618	0.6865	0.5728	0.4085	28.1790	100
0.0401	19.0	1425	1.2831	0.0032	0.7568	0.6809	0.5669	0.4015	28.6877	100
0.0189	20.0	1500	1.2742	0.0032	0.7632	0.6812	0.5609	0.3968	28.8911	100
0.0189	21.0	1575	1.2997	0.0032	0.7572	0.6774	0.5635	0.3996	29.8067	100
0.0189	22.0	1650	1.2868	0.0032	0.7641	0.6860	0.5734	0.4094	28.4842	100
0.0189	23.0	1725	1.2870	0.0032	0.7630	0.6850	0.5718	0.4083	28.4842	100
0.0189	24.0	1800	1.3061	0.0032	0.7626	0.6904	0.5810	0.4212	28.1790	100
0.0189	25.0	1875	1.3040	0.0032	0.7621	0.6843	0.5724	0.4111	29.1963	100
0.0189	26.0	1950	1.2923	0.0032	0.7641	0.6892	0.5782	0.4138	28.3825	100
0.0129	27.0	2025	1.3121	0.0032	0.7635	0.6873	0.5744	0.4099	28.5860	100
0.0129	28.0	2100	1.3181	0.0032	0.7600	0.6857	0.5731	0.4072	28.4842	100
0.0129	29.0	2175	1.3134	0.0032	0.7621	0.6862	0.5728	0.4074	28.4842	100
0.0129	30.0	2250	1.3114	0.0032	0.7611	0.6850	0.5721	0.4070	28.4842	100

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

vania2911/exp4_10partition_modelo3000

exp4_10partition_modelo3000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp4_10partition_modelo3000