The open-source English-Spanish translation model mbart-large-50-en-es-translation shows excellent evaluation performance!

Mbart Large 50 En Es Translation Lr 1e 05 Weight Decay 0.1

Developed by DrishtiSharma

An English-Spanish translation model fine-tuned from facebook/mbart-large-50, demonstrating excellent performance on evaluation sets.

Machine Translation

Transformers

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

Downloads 20

Release Time : 9/2/2023

Model Overview

This model is a sequence-to-sequence model optimized for English-to-Spanish translation tasks, implementing cross-language conversion based on the mBART architecture.

Model Features

High-quality translation

Achieves a BLEU score of 45.15 and ROUGE-1 score of 0.707 on evaluation sets, indicating excellent translation quality.

Multilingual support

Based on the mBART-large-50 architecture, capable of handling 50 languages.

Optimized training

Fine-tuned with a learning rate of 1e-5 and weight decay of 0.1, achieving optimal results after 4 epochs.

Model Capabilities

English-to-Spanish text translation

Cross-language text conversion

Sequence-to-sequence modeling

Use Cases

Content localization

Document translation

Translate English technical documents, news, or business files into Spanish

Maintains original semantics while achieving fluent target language expression

Multilingual applications

Chatbot backend

Provide real-time translation support for multilingual chat systems

🚀 mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.1

This is a fine - tuned model for English - Spanish translation based on the mbart - large - 50 architecture, achieving good performance on evaluation metrics.

🚀 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: 0.9532
Bleu: 45.1551
Rouge: {'rouge1': 0.707093830119779, 'rouge2': 0.5240989044660875, 'rougeL': 0.6865395711179825, 'rougeLsum': 0.6867643949864491}

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

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
1.4485	1.0	4500	1.0236	42.1586	{'rouge1': 0.6728104679322686, 'rouge2': 0.4866267759088613, 'rougeL': 0.6507619922873461, 'rougeLsum': 0.6508024989844624}
0.8867	2.0	9000	0.9542	44.1945	{'rouge1': 0.6933374960151913, 'rouge2': 0.5090654274262618, 'rougeL': 0.6722360570050694, 'rougeLsum': 0.6723972406375381}
0.7112	3.0	13500	0.9408	44.9173	{'rouge1': 0.7047659807760827, 'rouge2': 0.5200169348076622, 'rougeL': 0.6839031690668775, 'rougeLsum': 0.6842067045539153}
0.6075	4.0	18000	0.9532	45.2020	{'rouge1': 0.7070170730434684, 'rouge2': 0.5239391023023636, 'rougeL': 0.6863309446860562, 'rougeLsum': 0.6866635686411662}

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.

Property	Details
Model Type	Fine - tuned mbart-large-50 for English - Spanish translation
Training Data	Not provided
Metrics	Bleu, Rouge

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