8_6kmslsamples

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: 0.4729
• Bleu Msl: 77.3631
• Bleu Asl: 0
• Ter Msl: 11.9346
• 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	Bleu Msl	Bleu Asl	Ter Msl	Ter Asl
No log	1.0	217	0.2646	21.2021	84.6183	57.1279	8.2427
No log	2.0	434	0.1929	17.6495	84.9583	34.5912	7.4059
0.4655	3.0	651	0.1742	67.4481	89.3963	19.9161	5.7531
0.4655	4.0	868	0.1567	63.4679	86.5311	24.6855	7.8243
0.0791	5.0	1085	0.1559	63.5398	82.6386	21.5933	9.6234
0.0791	6.0	1302	0.1548	73.9641	86.8345	17.8721	8.1172
0.0421	7.0	1519	0.1606	70.5606	88.7113	16.7715	5.8368
0.0421	8.0	1736	0.1556	35.3257	86.5104	30.7652	7.4686
0.0421	9.0	1953	0.1564	66.0661	85.8235	22.6415	8.2636
0.0269	10.0	2170	0.1584	68.5372	88.0956	20.1782	6.7364
0.0269	11.0	2387	0.1641	64.1749	89.0674	22.8512	5.8996
0.0178	12.0	2604	0.1668	72.4073	88.8679	16.5094	6.0460
0.0178	13.0	2821	0.1663	63.9882	86.2327	18.7107	8.8285
0.0136	14.0	3038	0.1646	73.1200	88.9404	17.1908	6.2343
0.0136	15.0	3255	0.1726	66.6138	87.2488	18.6583	6.9874
0.0136	16.0	3472	0.1716	54.0153	87.6643	22.4843	6.4644
0.0101	17.0	3689	0.1680	65.4861	88.5112	16.5618	5.7531
0.0101	18.0	3906	0.1733	72.5027	88.9905	17.1384	5.8787
0.0078	19.0	4123	0.1701	69.9889	85.8965	18.7631	8.7029
0.0078	20.0	4340	0.1775	69.8437	88.9591	17.6101	6.1297
0.0051	21.0	4557	0.1731	72.1034	88.3815	18.0818	6.5900
0.0051	22.0	4774	0.1744	69.9773	89.2154	17.0860	5.7531
0.0051	23.0	4991	0.1711	72.3725	90.1699	15.6709	5.3975
0.0059	24.0	5208	0.1715	69.6558	89.0297	17.4004	6.1297
0.0059	25.0	5425	0.1710	69.6504	87.9393	17.6101	7.2594
0.0041	26.0	5642	0.1730	62.5723	87.0114	18.2914	8.3473
0.0041	27.0	5859	0.1727	65.0893	88.5422	17.0860	6.9456
0.0031	28.0	6076	0.1725	70.9388	88.1427	16.7191	7.3222
0.0031	29.0	6293	0.1705	70.5254	88.4779	16.8239	6.9456
0.0027	30.0	6510	0.1707	71.1488	88.5933	16.7715	6.9038

Framework versions

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