aslandmsl

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: 0.1788
• Model Preparation Time: 0.0058
• Bleu Msl: 88.0304
• Bleu Asl: 0
• Ter Msl: 7.4110
• 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: 1e-05
• train_batch_size: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Use 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 Asl	Ter Msl	Ter Asl
No log	1.0	225	1.5653	0.0058	6.5801	55.0209	107.8081	37.3399
No log	2.0	450	0.9988	0.0058	36.1652	80.6836	45.5315	8.5089
1.7595	3.0	675	0.6401	0.0058	50.4479	83.7950	32.2672	7.3110
1.7595	4.0	900	0.4573	0.0058	61.3116	66.8757	25.3057	6.1545
0.6205	5.0	1125	0.3856	0.0058	66.5991	88.5773	21.8250	5.1219
0.6205	6.0	1350	0.3448	0.0058	43.1115	89.5128	31.3264	4.5849
0.3287	7.0	1575	0.3144	0.0058	65.9756	89.9086	20.4139	4.5023
0.3287	8.0	1800	0.2754	0.0058	45.0564	90.8438	28.8805	4.0479
0.2225	9.0	2025	0.2410	0.0058	72.2558	90.5190	16.5569	4.2131
0.2225	10.0	2250	0.2229	0.0058	72.6469	90.9231	15.6162	4.0892
0.2225	11.0	2475	0.2126	0.0058	73.4167	91.5905	14.9577	3.8827
0.1448	12.0	2700	0.2049	0.0058	74.4555	70.4375	14.8636	4.0892
0.1448	13.0	2925	0.1993	0.0058	73.3591	91.3585	15.0517	4.0066
0.11	14.0	3150	0.1958	0.0058	73.9381	91.3182	14.0169	3.8827
0.11	15.0	3375	0.1890	0.0058	75.5526	91.6437	14.2051	3.8001
0.0882	16.0	3600	0.1881	0.0058	73.7777	91.8284	14.4873	3.7588
0.0882	17.0	3825	0.1851	0.0058	75.4362	91.4902	14.2051	3.7588
0.0723	18.0	4050	0.1850	0.0058	75.6099	92.0202	14.4873	3.6349
0.0723	19.0	4275	0.1822	0.0058	76.2459	91.9730	14.0169	3.6349
0.0641	20.0	4500	0.1839	0.0058	75.0209	91.9730	14.0169	3.6349
0.0641	21.0	4725	0.1806	0.0058	75.7669	92.0658	13.8288	3.5936
0.0641	22.0	4950	0.1809	0.0058	76.2001	92.0484	13.2643	3.5936
0.0576	23.0	5175	0.1793	0.0058	75.9506	92.2068	13.7347	3.5109
0.0576	24.0	5400	0.1781	0.0058	76.3576	92.3340	13.4525	3.4696
0.0515	25.0	5625	0.1789	0.0058	75.8648	92.1142	13.3584	3.5936
0.0515	26.0	5850	0.1784	0.0058	76.3297	92.2886	12.8881	3.5109
0.0479	27.0	6075	0.1788	0.0058	76.0603	92.5564	13.2643	3.3870
0.0479	28.0	6300	0.1778	0.0058	76.3080	92.3287	13.0762	3.5109
0.0469	29.0	6525	0.1780	0.0058	76.3707	92.3287	13.0762	3.5109
0.0469	30.0	6750	0.1781	0.0058	76.3707	92.3287	13.0762	3.5109

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3