5661

This model is a fine-tuned version of Helsinki-NLP/opus-mt-es-es on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5476
• Model Preparation Time: 0.0034
• Bleu Msl: 0.0
• Bleu 1 Msl: 0.6746
• Bleu 2 Msl: 0.0151
• Bleu 3 Msl: 0.0045
• Bleu 4 Msl: 0.0023
• Ter Msl: 100
• Bleu Asl: 0
• Bleu 1 Asl: 0
• Bleu 2 Asl: 0
• Bleu 3 Asl: 0
• Bleu 4 Asl: 0
• Ter Asl: 100

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• 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 Msl	Bleu 1 Msl	Bleu 2 Msl	Bleu 3 Msl	Bleu 4 Msl	Ter Msl	Bleu Asl	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	142	0.3764	0.0034	35.3553	0.5627	0.0104	0.0029	0.0014	100	100.0000	0.4152	0.0083	0.0024	0.0012	100
No log	2.0	284	0.3592	0.0034	37.9918	0.5665	0.0104	0.0029	0.0014	100	53.7285	0.3970	0.0081	0.0024	0.0012	100
No log	3.0	426	0.3690	0.0034	50.0000	0.5627	0.0104	0.0029	0.0014	100	32.1729	0.3427	0.0075	0.0022	0.0011	100
0.019	4.0	568	0.3660	0.0034	50.0000	0.5875	0.0106	0.0029	0.0014	100	37.9918	0.4135	0.0083	0.0024	0.0012	100
0.019	5.0	710	0.3824	0.0034	50.0000	0.6350	0.0110	0.0030	0.0014	100	100.0000	0.4465	0.0086	0.0024	0.0012	100
0.019	6.0	852	0.3574	0.0034	50.0000	0.5019	0.0098	0.0028	0.0014	100	42.7287	0.4217	0.0083	0.0024	0.0012	100
0.019	7.0	994	0.3810	0.0034	0.0	0.5361	0.0101	0.0029	0.0014	100	100.0000	0.4300	0.0084	0.0024	0.0012	100
0.0188	8.0	1136	0.3659	0.0034	50.0000	0.5	0.0098	0.0028	0.0014	100	100.0000	0.3904	0.0080	0.0023	0.0012	100
0.0188	9.0	1278	0.3696	0.0034	50.0000	0.6236	0.0109	0.0030	0.0014	100	100.0000	0.4778	0.0089	0.0025	0.0012	100
0.0188	10.0	1420	0.3749	0.0034	50.0000	0.5970	0.0107	0.0030	0.0014	100	100.0000	0.4498	0.0086	0.0025	0.0012	100
0.0107	11.0	1562	0.3746	0.0034	50.0000	0.5228	0.0100	0.0028	0.0014	100	70.7107	0.4349	0.0085	0.0024	0.0012	100
0.0107	12.0	1704	0.3635	0.0034	50.0000	0.5627	0.0104	0.0029	0.0014	100	100.0000	0.4728	0.0088	0.0025	0.0012	100
0.0107	13.0	1846	0.3629	0.0034	50.0000	0.5856	0.0106	0.0029	0.0014	100	100.0000	0.4481	0.0086	0.0024	0.0012	100
0.0107	14.0	1988	0.3697	0.0034	50.0000	0.5760	0.0105	0.0029	0.0014	100	100.0000	0.4778	0.0089	0.0025	0.0012	100
0.0096	15.0	2130	0.3630	0.0034	50.0000	0.5570	0.0103	0.0029	0.0014	100	59.4604	0.4514	0.0086	0.0025	0.0012	100
0.0096	16.0	2272	0.3608	0.0034	50.0000	0.5304	0.0101	0.0028	0.0014	100	100.0000	0.4481	0.0086	0.0024	0.0012	100
0.0096	17.0	2414	0.3612	0.0034	50.0000	0.5646	0.0104	0.0029	0.0014	100	100.0000	0.4415	0.0085	0.0024	0.0012	100
0.0057	18.0	2556	0.3571	0.0034	50.0000	0.5646	0.0104	0.0029	0.0014	100	100.0000	0.4415	0.0085	0.0024	0.0012	100
0.0057	19.0	2698	0.3655	0.0034	50.0000	0.5817	0.0105	0.0029	0.0014	100	100.0000	0.4514	0.0086	0.0025	0.0012	100
0.0057	20.0	2840	0.3682	0.0034	50.0000	0.5760	0.0105	0.0029	0.0014	100	100.0000	0.4580	0.0087	0.0025	0.0012	100
0.0057	21.0	2982	0.3683	0.0034	50.0000	0.5722	0.0104	0.0029	0.0014	100	100.0000	0.4646	0.0088	0.0025	0.0012	100
0.0046	22.0	3124	0.3745	0.0034	50.0000	0.5798	0.0105	0.0029	0.0014	100	100.0000	0.4695	0.0088	0.0025	0.0012	100
0.0046	23.0	3266	0.3680	0.0034	50.0000	0.6198	0.0109	0.0030	0.0014	100	100.0000	0.4811	0.0089	0.0025	0.0012	100
0.0046	24.0	3408	0.3702	0.0034	50.0000	0.5951	0.0106	0.0030	0.0014	100	100.0000	0.4811	0.0089	0.0025	0.0012	100
0.0039	25.0	3550	0.3701	0.0034	50.0000	0.5951	0.0106	0.0030	0.0014	100	100.0000	0.4959	0.0090	0.0025	0.0012	100
0.0039	26.0	3692	0.3689	0.0034	50.0000	0.6065	0.0107	0.0030	0.0014	100	100.0000	0.5041	0.0091	0.0025	0.0012	100
0.0039	27.0	3834	0.3681	0.0034	50.0000	0.6084	0.0108	0.0030	0.0014	100	100.0000	0.5008	0.0091	0.0025	0.0012	100
0.0039	28.0	3976	0.3684	0.0034	50.0000	0.6217	0.0109	0.0030	0.0014	100	100.0000	0.5025	0.0091	0.0025	0.0012	100
0.0033	29.0	4118	0.3702	0.0034	50.0000	0.6122	0.0108	0.0030	0.0014	100	100.0000	0.5008	0.0091	0.0025	0.0012	100
0.0033	30.0	4260	0.3700	0.0034	50.0000	0.6160	0.0108	0.0030	0.0014	100	100.0000	0.5008	0.0091	0.0025	0.0012	100

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0