exp3_10partition_modelo12000

vania2911/exp3_10partition_modelo12000

exp3_10partition_modelo12000 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 24, 2025
Updated: Mar 25, 2025
SHA: e9e9f30e

Base models

Helsinki-NLP/opus-mt-es-es

Model card

From the Hugging Face model README

exp3_10partition_modelo12000

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: 0.3817
Model Preparation Time: 0.0034
Bleu Msl: 0
Bleu 1 Msl: 0.7804
Bleu 2 Msl: 0.6823
Bleu 3 Msl: 0.5551
Bleu 4 Msl: 0.4249
Ter Msl: 33.6174
Bleu Asl: 0
Bleu 1 Asl: 0.9531
Bleu 2 Asl: 0.9311
Bleu 3 Asl: 0.9083
Bleu 4 Asl: 0.8815
Ter Asl: 6.1764

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	300	0.6646	0.0034	0.0312	0.0161	0.0082	0.0037	648.9882	0.9510	0.9235	0.8931	0.8592	6.3634
0.4437	2.0	600	0.6571	0.0034	0.4202	0.2571	0.1500	0.0769	82.7150	0.9294	0.9037	0.8753	0.8435	8.3716
0.4437	3.0	900	0.6110	0.0034	0.5792	0.3846	0.2524	0.1452	61.4671	0.8996	0.8695	0.8393	0.8017	6.8715
0.0963	4.0	1200	0.5833	0.0034	0.5626	0.3791	0.2495	0.1544	70.3204	0.9314	0.9020	0.8705	0.8340	9.2911
0.055	5.0	1500	0.6079	0.0034	0.6084	0.4374	0.3042	0.1969	58.0944	0.9647	0.9420	0.9161	0.8872	5.0569
0.055	6.0	1800	0.6174	0.0034	0.5749	0.4031	0.2785	0.1668	60.6239	0.9603	0.9366	0.9108	0.8814	5.5408
0.0355	7.0	2100	0.5909	0.0034	0.3742	0.2703	0.1981	0.1379	86.3406	0.9321	0.9099	0.8850	0.8559	8.4926
0.0355	8.0	2400	0.6109	0.0034	0.5499	0.3849	0.2736	0.1872	72.1754	0.9257	0.9012	0.8726	0.8389	9.9444
0.0273	9.0	2700	0.6152	0.0034	0.4619	0.3279	0.2365	0.1651	73.6931	0.9344	0.9120	0.8858	0.8531	8.8556
0.0192	10.0	3000	0.6463	0.0034	0.6577	0.4867	0.3586	0.2258	52.0236	0.9503	0.9293	0.9065	0.8785	7.0409
0.0192	11.0	3300	0.6566	0.0034	0.6399	0.4874	0.3749	0.2675	55.1433	0.9442	0.9199	0.8933	0.8610	7.3312
0.016	12.0	3600	0.6420	0.0034	0.6451	0.5071	0.3992	0.2958	51.2648	0.9516	0.9305	0.9072	0.8788	6.5812
0.016	13.0	3900	0.6414	0.0034	0.6098	0.4737	0.3643	0.2756	54.3845	0.9544	0.9320	0.9079	0.8793	6.4844
0.0142	14.0	4200	0.6417	0.0034	0.6099	0.4690	0.3572	0.2654	55.9865	0.9484	0.9249	0.9002	0.8710	7.1861
0.0107	15.0	4500	0.6349	0.0034	0.6362	0.4884	0.3773	0.2859	53.8786	0.9610	0.9400	0.9178	0.8910	5.4440
0.0107	16.0	4800	0.6363	0.0034	0.6029	0.4573	0.3366	0.2252	53.5413	0.9403	0.9205	0.8981	0.8701	8.0813
0.0088	17.0	5100	0.6230	0.0034	0.6437	0.4917	0.3669	0.2546	52.4452	0.9543	0.9356	0.9144	0.8886	6.5570
0.0088	18.0	5400	0.6489	0.0034	0.6191	0.4778	0.3523	0.2405	54.7218	0.9511	0.9328	0.9125	0.8875	6.6054
0.0074	19.0	5700	0.6381	0.0034	0.6201	0.4775	0.3588	0.2514	55.1433	0.9382	0.9204	0.8988	0.8720	8.5168
0.0065	20.0	6000	0.6152	0.0034	0.6278	0.4971	0.3948	0.3076	51.7707	0.9474	0.9268	0.9046	0.8778	6.7747
0.0065	21.0	6300	0.6253	0.0034	0.6621	0.5167	0.4071	0.3058	51.6020	0.9567	0.9384	0.9184	0.8932	5.7101
0.0057	22.0	6600	0.6376	0.0034	0.6503	0.5156	0.4066	0.3005	50.0	0.9537	0.9361	0.9158	0.8906	6.5328
0.0057	23.0	6900	0.6378	0.0034	0.6680	0.5163	0.3966	0.2829	54.8904	0.9556	0.9379	0.9172	0.8926	6.4844
0.0045	24.0	7200	0.6320	0.0034	0.6645	0.5203	0.4034	0.2947	50.5902	0.9508	0.9324	0.9111	0.8850	6.7022
0.0042	25.0	7500	0.6383	0.0034	0.6665	0.5116	0.3933	0.2844	51.4334	0.9482	0.9290	0.9073	0.8813	6.8473
0.0042	26.0	7800	0.6403	0.0034	0.6554	0.5060	0.3886	0.2775	51.6020	0.9548	0.9358	0.9144	0.8888	6.2182
0.0035	27.0	8100	0.6438	0.0034	0.6640	0.5096	0.3866	0.2699	50.2530	0.9594	0.9410	0.9203	0.8954	5.2020
0.0035	28.0	8400	0.6459	0.0034	0.6655	0.5088	0.3865	0.2717	51.2648	0.9580	0.9398	0.9197	0.8956	5.5408
0.0029	29.0	8700	0.6455	0.0034	0.6625	0.5096	0.3918	0.2803	51.3491	0.9591	0.9408	0.9203	0.8956	5.4682
0.0028	30.0	9000	0.6424	0.0034	0.6659	0.5118	0.3931	0.2808	51.6020	0.9587	0.9401	0.9193	0.8944	5.4924

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Library: transformers
Type: marian
License: apache-2.0
Created: Mar 24, 2025
Updated: Mar 25, 2025