mbart-large-50-en-es-translation Open Source Model - Free Deployment for Precise English-Spanish Translation

Mbart Large 50 En Es Translation Lr 1e 05 Weight Decay 0.0

Developed by DrishtiSharma

This model is a fine-tuned English-to-Spanish translation model based on facebook/mbart-large-50, achieving a BLEU score of 45.03 on the evaluation set.

Machine Translation

Transformers

Open Source License:MIT #English-Spanish translation #Multilingual model #High BLEU score

Downloads 27

Release Time : 9/2/2023

Model Overview

An English-to-Spanish neural machine translation model based on the mBART-large-50 architecture, suitable for high-quality text translation tasks.

Model Features

High-quality translation

Achieved a BLEU score of 45.03 and a ROUGE-1 score of 0.70 on the evaluation set, indicating high translation quality.

Based on mBART architecture

Utilizes the mBART-large-50 pre-trained model for fine-tuning, inheriting its robust multilingual representation capabilities.

Optimized training

Trained for 4 epochs with a learning rate of 1e-5 and a linear learning rate scheduler, achieving stable performance improvements.

Model Capabilities

English-to-Spanish text translation

Fluid translation preserving original semantics

Handling long-text translation

Use Cases

Text translation

Document translation

Translate English documents into Spanish while maintaining the accuracy of professional terminology.

Achieved a BLEU score of 45.03 on the test set

Content localization

Localize English website or application content into Spanish versions.

🚀 mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.0

This model is a fine - tuned translation model. It is based on facebook/mbart-large-50, aiming to provide high - quality English - Spanish translation services.

🚀 Quick Start

This model is a fine - tuned version of facebook/mbart-large-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9549
Bleu: 45.0307
Rouge: {'rouge1': 0.7049318825090395, 'rouge2': 0.5238048751750992, 'rougeL': 0.684187379601513, 'rougeLsum': 0.6843574853855577}

📚 Documentation

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: 1e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Rouge
1.4627	1.0	4500	1.0255	42.1880	{'rouge1': 0.6725633216905762, 'rouge2': 0.48605402524493657, 'rougeL': 0.6498853764470456, 'rougeLsum': 0.6501981166312041}
0.8878	2.0	9000	0.9572	44.1734	{'rouge1': 0.6912686406245903, 'rouge2': 0.5093695171345348, 'rougeL': 0.6701896043455414, 'rougeLsum': 0.6703473419504804}
0.7125	3.0	13500	0.9414	44.8709	{'rouge1': 0.7051197958532004, 'rouge2': 0.5210482863677958, 'rougeL': 0.6843075431636916, 'rougeLsum': 0.6846265298079588}
0.6092	4.0	18000	0.9549	45.0821	{'rouge1': 0.7047932899349161, 'rouge2': 0.523739339466653, 'rougeL': 0.6840127607742443, 'rougeLsum': 0.684202100852132}

Framework versions

Transformers 4.33.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.4.dev0
Tokenizers 0.13.3

📄 License

This model is released under the MIT license.

📦 Information Table

Property	Details
Model Type	Fine - tuned version of facebook/mbart-large-50 for English - Spanish translation
Training Data	None
Metrics	Bleu, Rouge

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