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	0.9046	0.0033	0.3703	0.2970	0.2268	0.1442	78.0793	100
No log	2.0	150	0.6660	0.0033	0.8752	0.8014	0.7035	0.5440	20.5637	100
No log	3.0	225	0.7901	0.0033	0.8289	0.7487	0.6546	0.5102	27.5574	100
No log	4.0	300	0.6702	0.0033	0.8519	0.7724	0.6515	0.4803	20.9812	100
No log	5.0	375	0.6241	0.0033	0.8704	0.8036	0.7068	0.5441	20.5637	100
No log	6.0	450	0.6839	0.0033	0.8641	0.7903	0.6911	0.5385	24.8434	100
0.6205	7.0	525	0.6668	0.0033	0.8694	0.8052	0.7063	0.5480	20.2505	100
0.6205	8.0	600	0.6896	0.0033	0.8639	0.7863	0.6773	0.5052	22.5470	100
0.6205	9.0	675	0.7457	0.0033	0.8305	0.7641	0.6696	0.5162	25.6785	100
0.6205	10.0	750	0.7002	0.0033	0.8723	0.8149	0.7157	0.5472	17.9541	100
0.6205	11.0	825	0.6898	0.0033	0.8603	0.7944	0.6951	0.5361	22.1294	100
0.6205	12.0	900	0.6749	0.0033	0.8759	0.8201	0.7297	0.5663	18.1628	100
0.6205	13.0	975	0.6903	0.0033	0.8761	0.8184	0.7254	0.5674	17.9541	100
0.0488	14.0	1050	0.6715	0.0033	0.8676	0.8067	0.7131	0.5525	20.3549	100
0.0488	15.0	1125	0.6555	0.0033	0.8666	0.8139	0.7199	0.5503	17.2234	100
0.0488	16.0	1200	0.7132	0.0033	0.8789	0.8168	0.7237	0.5601	19.2067	100
0.0488	17.0	1275	0.6874	0.0033	0.8837	0.8264	0.7310	0.5636	18.2672	100
0.0488	18.0	1350	0.7008	0.0033	0.8875	0.8278	0.7335	0.5729	18.7891	100
0.0488	19.0	1425	0.6363	0.0033	0.8681	0.8200	0.7279	0.5545	16.1795	100
0.0215	20.0	1500	0.6844	0.0033	0.8789	0.8182	0.7231	0.5615	19.3111	100
0.0215	21.0	1575	0.6466	0.0033	0.8718	0.8139	0.7213	0.5575	18.2672	100
0.0215	22.0	1650	0.6860	0.0033	0.8742	0.8151	0.7227	0.5584	19.6242	100
0.0215	23.0	1725	0.6485	0.0033	0.8700	0.8126	0.7211	0.5592	18.3716	100
0.0215	24.0	1800	0.6594	0.0033	0.8788	0.8196	0.7280	0.5653	19.1023	100
0.0215	25.0	1875	0.6605	0.0033	0.8758	0.8193	0.7286	0.5654	18.0585	100
0.0215	26.0	1950	0.6482	0.0033	0.8699	0.8133	0.7198	0.5562	18.2672	100
0.014	27.0	2025	0.6410	0.0033	0.8689	0.8123	0.7185	0.5545	18.2672	100
0.014	28.0	2100	0.6564	0.0033	0.8729	0.8158	0.7238	0.5611	18.3716	100
0.014	29.0	2175	0.6571	0.0033	0.8690	0.8113	0.7175	0.5538	18.4760	100
0.014	30.0	2250	0.6563	0.0033	0.8690	0.8113	0.7175	0.5538	18.4760	100

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

vania2911/exp2_10partition_modelo3000

exp2_10partition_modelo3000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp2_10partition_modelo3000