Banglish To Bangla

🚀 Banglish-to-Bangla Transliteration Model

This model is designed to convert Banglish (Bengali written in Roman script) into Bengali script, offering a practical solution for language conversion in various communication scenarios.

🚀 Quick Start

If you want to use this Banglish-to-Bangla transliteration model, you can refer to the following code example:

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model = MBartForConditionalGeneration.from_pretrained("your-username/banglish-to-bangla-mbart")
tokenizer = MBart50TokenizerFast.from_pretrained("your-username/banglish-to-bangla-mbart")

def translate(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)
    outputs = model.generate(inputs.input_ids, max_length=64, num_beams=5, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(translate("ami tomake valobashi"))

✨ Features

• Direct Use: It can perform transliteration of Banglish text to Bengali script for social media, messaging, and formal communication.
• Downstream Use: It supports fine - tuning for translation tasks between Bengali and other languages and can be integrated into chatbots or virtual assistants.

📚 Documentation

Model Details

Model Description

This model is designed to transliterate Banglish (Bengali written in Roman script) into Bengali script. It is fine - tuned from the facebook/mbart-large-50-many-to-many-mmt model using the SKNahin/bengali-transliteration-data dataset.

Model Sources

• Repository: https://huggingface.co/your-username/banglish-to-bangla-mbart
• Dataset: SKNahin/bengali-transliteration-data
• Demo (Optional): [Colab Notebook Link or Web Demo]

Uses

Direct Use

• Transliteration of Banglish text to Bengali script for social media, messaging, and formal communication.

Downstream Use

• Fine - tuning for translation tasks between Bengali and other languages.
• Integration into chatbots or virtual assistants.

Out - of - Scope Use

• General - purpose language translation between unrelated languages.
• Handling code - mixed languages (e.g., Banglish + English combinations).

Bias, Risks, and Limitations

Biases

• The dataset may include informal phrases, potentially reducing performance on formal language.
• Performance may degrade for long or complex sentences.

Limitations

• Model performance may vary for rare phrases or slang.
• Does not support mixed language inputs effectively.

Recommendations

⚠️ Important Note

Users should evaluate outputs for their specific use cases, especially in formal contexts. Additional filtering or pre - processing may be required.