English To Urdu Translation Mbart

🚀 Fine-tuned mBART Model for English to Urdu Translation

This repository offers a fine-tuned mBART model designed for English to Urdu translation. The model is trained on a custom dataset and evaluated on test data, providing reliable translation results.

🚀 Quick Start

To use the fine-tuned model, you can follow the code example below:

from transformers import MBart50TokenizerFast, MBartForConditionalGeneration

# Load the fine-tuned model
model_name = "abdulwaheed1/english-to-urdu-translation-mbart"
tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="en_XX", tgt_lang="ur_PK")
model = MBartForConditionalGeneration.from_pretrained(model_name)

✨ Features

• Language Pair: Specifically tailored for English to Urdu translation.
• Fine-tuning: Trained on a custom dataset to enhance performance.
• Evaluation: Evaluated on test data with multiple metrics.

📦 Installation

There is no specific installation step provided in the original document. If you want to use the model, you need to have the transformers library installed. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import MBart50TokenizerFast, MBartForConditionalGeneration

# Load the fine-tuned model
model_name = "abdulwaheed1/english-to-urdu-translation-mbart"
tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="en_XX", tgt_lang="ur_PK")
model = MBartForConditionalGeneration.from_pretrained(model_name)

📚 Documentation

Model Information

Evaluation

The model has been evaluated on a test dataset, and the following metrics were obtained:

• BLEU Score: 35.87
• Generation Length: 42.56
• Meteor Score: 0.60

Training Details

The model was trained using the transformers library with the following configuration:

• Training Loss: 1.5697
• Validation Loss: 1.1256

Dataset

The model was fine-tuned on a custom English-Urdu translation dataset. If you wish to use the same dataset, you can find the preprocessing script and dataset files in the data directory.

🔧 Technical Details

The model is based on the facebook/mbart-large-50 architecture and fine-tuned on a custom dataset. The fine-tuning process was carried out using the transformers library, with specific training and validation loss values recorded.

📄 License

The original document does not provide license information, so this section is skipped.

Acknowledgments

The fine-tuning process and code were inspired by the Hugging Face Transformers library.