exp2_10partition_modelo6000

vania2911/exp2_10partition_modelo6000

exp2_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: 78bc8731

Base models

Helsinki-NLP/opus-mt-es-es

Model card

From the Hugging Face model README

exp2_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: 0.7177
Model Preparation Time: 0.0055
Bleu Msl: 0
Bleu 1 Msl: 0.8613
Bleu 2 Msl: 0.8246
Bleu 3 Msl: 0.7705
Bleu 4 Msl: 0.6471
Ter Msl: 19.5145
Bleu Asl: 0
Bleu 1 Asl: 0.9566
Bleu 2 Asl: 0.9339
Bleu 3 Asl: 0.9089
Bleu 4 Asl: 0.8790
Ter Asl: 5.3897

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	0.5531	0.0055	0.8827	0.7822	0.6773	0.5163	22.9645	0.9342	0.8941	0.8566	0.8168	8.3744
No log	2.0	300	0.4809	0.0055	0.8034	0.7089	0.6134	0.4689	33.1942	0.9424	0.9112	0.8790	0.8425	6.8262
No log	3.0	450	0.3903	0.0055	0.8921	0.8197	0.7195	0.5580	19.1023	0.9501	0.9212	0.8910	0.8579	6.2632
0.5309	4.0	600	0.4627	0.0055	0.8615	0.7749	0.6636	0.4943	25.7829	0.9468	0.9181	0.8873	0.8520	6.5447
0.5309	5.0	750	0.4495	0.0055	0.8941	0.8267	0.7307	0.5639	18.6848	0.9544	0.9290	0.9017	0.8707	5.6298
0.5309	6.0	900	0.4334	0.0055	0.8580	0.7896	0.6922	0.5148	23.7996	0.9531	0.9273	0.8990	0.8675	5.6298
0.0697	7.0	1050	0.4502	0.0055	0.8456	0.7671	0.6754	0.5213	25.4697	0.9586	0.9348	0.9085	0.8782	5.2076
0.0697	8.0	1200	0.4398	0.0055	0.8704	0.7887	0.6880	0.5276	23.7996	0.9562	0.9309	0.9034	0.8711	5.4187
0.0697	9.0	1350	0.4880	0.0055	0.8646	0.7850	0.6854	0.5280	24.2171	0.9538	0.9287	0.9015	0.8695	5.4187
0.0347	10.0	1500	0.4460	0.0055	0.8800	0.7952	0.6902	0.5186	23.4864	0.9525	0.9289	0.9044	0.8751	5.4891
0.0347	11.0	1650	0.4399	0.0055	0.8599	0.7817	0.6885	0.5307	23.6952	0.9531	0.9287	0.9023	0.8710	5.7002
0.0347	12.0	1800	0.4512	0.0055	0.8430	0.7587	0.6638	0.5103	26.4092	0.9586	0.9388	0.9174	0.8906	4.7854
0.0347	13.0	1950	0.4328	0.0055	0.9009	0.8295	0.7326	0.5642	19.2067	0.9580	0.9368	0.9142	0.8864	4.9261
0.0192	14.0	2100	0.4584	0.0055	0.8582	0.7826	0.6892	0.5327	23.9040	0.9569	0.9338	0.9089	0.8790	5.0669
0.0192	15.0	2250	0.4435	0.0055	0.8741	0.7961	0.7016	0.5362	23.0689	0.9529	0.9302	0.9060	0.8767	5.4891
0.0192	16.0	2400	0.4444	0.0055	0.8720	0.7965	0.7007	0.5298	23.3820	0.9593	0.9376	0.9132	0.8841	4.8557
0.013	17.0	2550	0.4424	0.0055	0.8866	0.8089	0.7089	0.5366	22.2338	0.9617	0.9414	0.9192	0.8915	4.5742
0.013	18.0	2700	0.4504	0.0055	0.8710	0.7954	0.6969	0.5273	23.9040	0.9623	0.9412	0.9173	0.8885	4.6446
0.013	19.0	2850	0.4472	0.0055	0.8780	0.8056	0.7023	0.5262	21.5031	0.9599	0.9371	0.9115	0.8800	4.9261
0.0098	20.0	3000	0.4748	0.0055	0.8770	0.8067	0.7067	0.5298	21.7119	0.9612	0.9412	0.9185	0.8902	4.6446
0.0098	21.0	3150	0.4512	0.0055	0.8704	0.7949	0.6956	0.5270	23.4864	0.9612	0.9412	0.9189	0.8904	4.6446
0.0098	22.0	3300	0.4549	0.0055	0.8527	0.7754	0.6816	0.5131	25.4697	0.9606	0.9398	0.9167	0.8878	4.7854
0.0098	23.0	3450	0.4658	0.0055	0.8541	0.7802	0.6885	0.5277	24.7390	0.9606	0.9394	0.9157	0.8863	4.7854
0.0076	24.0	3600	0.4437	0.0055	0.8746	0.8003	0.7011	0.5268	23.0689	0.9624	0.9421	0.9196	0.8918	4.5039
0.0076	25.0	3750	0.4636	0.0055	0.8654	0.7940	0.7017	0.5403	23.3820	0.9624	0.9425	0.9202	0.8923	4.5039
0.0076	26.0	3900	0.4425	0.0055	0.8741	0.8019	0.7051	0.5345	22.9645	0.9612	0.9404	0.9170	0.8886	4.6446
0.0067	27.0	4050	0.4544	0.0055	0.8547	0.7792	0.6867	0.5270	24.9478	0.9618	0.9415	0.9183	0.8900	4.5742
0.0067	28.0	4200	0.4502	0.0055	0.8626	0.7914	0.6990	0.5378	23.9040	0.9618	0.9415	0.9183	0.8900	4.5742
0.0067	29.0	4350	0.4536	0.0055	0.8616	0.7880	0.6934	0.5309	24.2171	0.9618	0.9415	0.9183	0.8900	4.5742
0.005	30.0	4500	0.4535	0.0055	0.8616	0.7880	0.6934	0.5309	24.2171	0.9618	0.9415	0.9183	0.8900	4.5742

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