exp3_10partition_modelo6000

vania2911/exp3_10partition_modelo6000

exp3_10partition_modelo6000 is a machine learning model from vania2911. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: marian
Access: Public
Created: Mar 15, 2025
Updated: Mar 26, 2025
SHA: 0d6cbf37

Base models

Helsinki-NLP/opus-mt-es-es

Model card

From the Hugging Face model README

exp3_10partition_modelo6000

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: 0.5689
Model Preparation Time: 0.0034
Bleu Msl: 0
Bleu 1 Msl: 0.8320
Bleu 2 Msl: 0.7371
Bleu 3 Msl: 0.6111
Bleu 4 Msl: 0.4747
Ter Msl: 25.5682
Bleu Asl: 0
Bleu 1 Asl: 0.9731
Bleu 2 Asl: 0.9522
Bleu 3 Asl: 0.9291
Bleu 4 Asl: 0.9010
Ter Asl: 3.0735

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 1 Msl	Bleu 2 Msl	Bleu 3 Msl	Bleu 4 Msl	Ter Msl	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	150	1.3308	0.0034	0.5309	0.3589	0.2253	0.1104	63.2378	0.9398	0.9034	0.8665	0.8255	7.3964
No log	2.0	300	1.1970	0.0034	0.2508	0.1620	0.1042	0.0581	112.1417	0.9605	0.9342	0.9066	0.8729	4.8817
No log	3.0	450	1.1426	0.0034	0.1355	0.0829	0.0489	0.0249	195.9528	0.9644	0.9381	0.9099	0.8770	4.3639
0.5052	4.0	600	1.1307	0.0034	0.4226	0.2837	0.1899	0.1238	78.3305	0.9651	0.9391	0.9109	0.8784	4.3639
0.5052	5.0	750	1.0743	0.0034	0.6482	0.4648	0.3387	0.2374	56.4081	0.9677	0.9436	0.9185	0.8884	3.9201
0.5052	6.0	900	1.0719	0.0034	0.6376	0.4993	0.3970	0.2993	47.3862	0.9651	0.9395	0.9130	0.8822	4.4379
0.0684	7.0	1050	1.1431	0.0034	0.6444	0.4855	0.3685	0.2702	50.5902	0.9644	0.9393	0.9130	0.8817	4.1420
0.0684	8.0	1200	1.1090	0.0034	0.6562	0.4803	0.3638	0.2656	51.1804	0.7332	0.6959	0.6578	0.6083	3.9201
0.0684	9.0	1350	1.1236	0.0034	0.6855	0.5231	0.4066	0.3001	49.0725	0.9664	0.9425	0.9171	0.8874	3.7722
0.031	10.0	1500	1.1382	0.0034	0.6427	0.4649	0.3347	0.2369	55.9865	0.9638	0.9383	0.9113	0.8786	4.2160
0.031	11.0	1650	1.1146	0.0034	0.6577	0.4821	0.3636	0.2490	51.0118	0.9651	0.9412	0.9151	0.8840	3.9201
0.031	12.0	1800	1.1889	0.0034	0.6732	0.5063	0.3848	0.2800	52.0236	0.9651	0.9410	0.9150	0.8839	3.9201
0.031	13.0	1950	1.1853	0.0034	0.6673	0.4892	0.3712	0.2689	52.8668	0.9696	0.9479	0.9242	0.8957	3.6243
0.0208	14.0	2100	1.2047	0.0034	0.6737	0.5096	0.3857	0.2800	50.3373	0.9702	0.9477	0.9242	0.8954	3.6243
0.0208	15.0	2250	1.1953	0.0034	0.6619	0.4949	0.3772	0.2729	51.4334	0.9657	0.9426	0.9168	0.8865	3.9201
0.0208	16.0	2400	1.1772	0.0034	0.6871	0.5183	0.4026	0.2952	48.9882	0.9696	0.9474	0.9240	0.8957	3.4763
0.014	17.0	2550	1.1982	0.0034	0.6947	0.5251	0.4045	0.2893	47.0489	0.9709	0.9502	0.9277	0.9002	3.3284
0.014	18.0	2700	1.2218	0.0034	0.6736	0.5080	0.3875	0.2808	48.9039	0.9715	0.9515	0.9294	0.9022	3.4024
0.014	19.0	2850	1.2035	0.0034	0.6864	0.5211	0.4033	0.2935	49.8314	0.9702	0.9493	0.9263	0.8986	3.4763
0.0092	20.0	3000	1.2138	0.0034	0.6824	0.5164	0.3985	0.2927	48.0607	0.9722	0.9520	0.9299	0.9030	3.1805
0.0092	21.0	3150	1.2348	0.0034	0.6718	0.5070	0.3923	0.2902	50.1686	0.9696	0.9470	0.9226	0.8936	3.5503
0.0092	22.0	3300	1.2350	0.0034	0.6721	0.5071	0.3915	0.2894	49.4941	0.9683	0.9457	0.9211	0.8919	3.6243
0.0092	23.0	3450	1.2337	0.0034	0.6900	0.5233	0.4046	0.2993	48.4823	0.9702	0.9489	0.9256	0.8972	3.5503
0.0075	24.0	3600	1.2332	0.0034	0.6919	0.5196	0.3982	0.2903	50.6745	0.9702	0.9493	0.9263	0.8983	3.4763
0.0075	25.0	3750	1.2324	0.0034	0.6989	0.5313	0.4142	0.3059	48.8196	0.9670	0.9455	0.9221	0.8942	3.8462
0.0075	26.0	3900	1.2377	0.0034	0.6982	0.5309	0.4119	0.3025	48.3980	0.9683	0.9478	0.9253	0.8980	3.6982
0.0068	27.0	4050	1.2398	0.0034	0.6893	0.5225	0.4056	0.2996	49.9157	0.9677	0.9467	0.9235	0.8956	3.7722
0.0068	28.0	4200	1.2441	0.0034	0.6850	0.5211	0.4055	0.3016	49.4098	0.9677	0.9467	0.9239	0.8962	3.7722
0.0068	29.0	4350	1.2470	0.0034	0.6817	0.5178	0.4043	0.3013	49.2411	0.9683	0.9478	0.9253	0.8980	3.6982
0.0051	30.0	4500	1.2511	0.0034	0.6795	0.5164	0.4029	0.3006	49.4941	0.9683	0.9478	0.9253	0.8980	3.6982

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Library: transformers
Type: marian
License: apache-2.0
Created: Mar 15, 2025
Updated: Mar 26, 2025