exp5_10partition_modelo6000

vania2911/exp5_10partition_modelo6000

exp5_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: 8a4256ef

Base models

Helsinki-NLP/opus-mt-es-es

Model card

From the Hugging Face model README

exp5_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: 0.8684
Model Preparation Time: 0.0035
Bleu Msl: 0
Bleu 1 Msl: 0.7062
Bleu 2 Msl: 0.5800
Bleu 3 Msl: 0.4740
Bleu 4 Msl: 0.3480
Ter Msl: 37.7193
Bleu Asl: 0
Bleu 1 Asl: 0.9629
Bleu 2 Asl: 0.9424
Bleu 3 Asl: 0.9231
Bleu 4 Asl: 0.8965
Ter Asl: 4.0580

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	0.9786	0.0035	0.0256	0.0165	0.0107	0.0049	831.3552	0.9330	0.8927	0.8514	0.8121	8.2202
No log	2.0	300	0.6154	0.0035	0.2507	0.1823	0.1371	0.0933	105.4916	0.9532	0.9232	0.8892	0.8542	5.8069
No log	3.0	450	0.5349	0.0035	0.7027	0.5836	0.4828	0.3657	29.5837	0.9532	0.9231	0.8901	0.8561	5.8069
0.5113	4.0	600	0.5290	0.0035	0.6919	0.5959	0.5164	0.4092	33.4810	0.9591	0.9322	0.9021	0.8716	5.1282
0.5113	5.0	750	0.5799	0.0035	0.7036	0.5717	0.4799	0.3644	37.9982	0.9622	0.9363	0.9075	0.8784	5.0528
0.5113	6.0	900	0.6210	0.0035	0.675	0.5696	0.4844	0.3783	37.4668	0.9615	0.9365	0.9094	0.8802	5.0528
0.0686	7.0	1050	0.6507	0.0035	0.6486	0.5360	0.4426	0.3261	40.8326	0.9641	0.9389	0.9117	0.8830	4.5249
0.0686	8.0	1200	0.7079	0.0035	0.6058	0.4845	0.3913	0.2856	45.9699	0.9629	0.9372	0.9089	0.8782	4.6757
0.0686	9.0	1350	0.6309	0.0035	0.6959	0.5640	0.4764	0.3747	38.0868	0.9622	0.9368	0.9093	0.8802	5.0528
0.0315	10.0	1500	0.6555	0.0035	0.6784	0.5393	0.4335	0.3132	40.2126	0.9642	0.9386	0.9105	0.8807	4.6757
0.0315	11.0	1650	0.5855	0.0035	0.6667	0.5319	0.4366	0.3428	39.0611	0.9681	0.9451	0.9190	0.8904	4.2232
0.0315	12.0	1800	0.5110	0.0035	0.7456	0.6591	0.5766	0.4690	28.8751	0.9642	0.9383	0.9096	0.8794	4.6003
0.0315	13.0	1950	0.6370	0.0035	0.7238	0.5590	0.4327	0.3156	37.6439	0.9649	0.9407	0.9144	0.8870	4.4495
0.0192	14.0	2100	0.6059	0.0035	0.6777	0.5252	0.4378	0.3498	41.6298	0.9648	0.9401	0.9128	0.8839	4.6757
0.0192	15.0	2250	0.4800	0.0035	0.7336	0.6387	0.5654	0.4748	30.2923	0.8313	0.7981	0.7608	0.7157	23.2278
0.0192	16.0	2400	0.5424	0.0035	0.7381	0.6389	0.5627	0.4672	29.4951	0.9662	0.9425	0.9155	0.8879	4.2232
0.0113	17.0	2550	0.5310	0.0035	0.7466	0.6593	0.5865	0.4928	27.7236	0.8290	0.7942	0.7569	0.7120	23.5294
0.0113	18.0	2700	0.5556	0.0035	0.7440	0.6364	0.5504	0.4503	29.9380	0.9616	0.9342	0.9042	0.8726	4.8265
0.0113	19.0	2850	0.5521	0.0035	0.7456	0.6411	0.5657	0.4743	29.5837	0.9655	0.9409	0.9141	0.8852	4.4495
0.0101	20.0	3000	0.5658	0.0035	0.7316	0.6419	0.5665	0.4701	29.4066	0.9668	0.9428	0.9150	0.8857	4.2232
0.0101	21.0	3150	0.5093	0.0035	0.7331	0.6524	0.5839	0.4941	25.8636	0.9648	0.9398	0.9120	0.8826	4.5249
0.0101	22.0	3300	0.5516	0.0035	0.7487	0.6693	0.5954	0.5015	27.1922	0.9668	0.9432	0.9155	0.8866	4.2986
0.0101	23.0	3450	0.5263	0.0035	0.7502	0.6698	0.6003	0.5118	26.1293	0.9681	0.9446	0.9174	0.8887	4.2232
0.0069	24.0	3600	0.5287	0.0035	0.7502	0.6698	0.5979	0.5051	25.5979	0.9648	0.9398	0.9120	0.8830	4.4495
0.0069	25.0	3750	0.5944	0.0035	0.7016	0.5979	0.5148	0.4133	33.3924	0.9681	0.9450	0.9188	0.8916	4.1478
0.0069	26.0	3900	0.5547	0.0035	0.7466	0.6598	0.5809	0.4724	27.9008	0.9674	0.9447	0.9190	0.8921	4.2232
0.0051	27.0	4050	0.5918	0.0035	0.7451	0.6390	0.5474	0.4306	30.2037	0.9681	0.9450	0.9192	0.8922	4.1478
0.0051	28.0	4200	0.5634	0.0035	0.7511	0.6606	0.5806	0.4761	28.0779	0.9694	0.9469	0.9214	0.8946	3.9970
0.0051	29.0	4350	0.5602	0.0035	0.7526	0.6626	0.5838	0.4809	27.9894	0.9694	0.9469	0.9214	0.8946	3.9970
0.004	30.0	4500	0.5592	0.0035	0.7536	0.6642	0.5862	0.4797	27.9008	0.9694	0.9469	0.9214	0.8946	3.9970

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