Mbart For Darija Transaltion

🚀 Moroccan Darija to English Translation Model (Fine-Tuned mBART)

This model is a fine - tuned version of mBART, tailored for translating Moroccan Darija to English. It leverages Facebook's mBART, a multilingual model, and is trained on a Moroccan Darija dataset for accurate translations.

🚀 Quick Start

You can easily load the model and tokenizer using the Hugging Face transformers library. Here's an example:

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

# Load the pre-trained model and tokenizer
model_name = 'echarif/mBART_for_darija_transaltion'
model = MBartForConditionalGeneration.from_pretrained(model_name)
tokenizer = MBart50TokenizerFast.from_pretrained(model_name)

# Prepare your input text (Moroccan Darija)
input_text = "insert your Moroccan Darija sentence here"

# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt", padding=True)

# Generate the translated output
translated_tokens = model.generate(**inputs)
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)

print(f"Translated Text: {translated_text}")

✨ Features

• Accurate Translation: Specifically fine - tuned for Moroccan Darija to English translation, ensuring high - quality results for conversational and informal text.
• Multilingual Base: Built on the mBART multilingual model, capable of handling various language - related tasks.

📦 Installation

The installation mainly involves setting up the Hugging Face transformers library. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

# Load the pre-trained model and tokenizer
model_name = 'echarif/mBART_for_darija_transaltion'
model = MBartForConditionalGeneration.from_pretrained(model_name)
tokenizer = MBart50TokenizerFast.from_pretrained(model_name)

# Prepare your input text (Moroccan Darija)
input_text = "insert your Moroccan Darija sentence here"

# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt", padding=True)

# Generate the translated output
translated_tokens = model.generate(**inputs)
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)

print(f"Translated Text: {translated_text}")

📚 Documentation

Model Overview

Model Details

The mBART model is a transformer - based sequence - to - sequence model, designed to handle multiple languages. It is particularly useful for tasks such as translation, text generation, and summarization.

For this specific task, the model has been fine - tuned to accurately translate text from Moroccan Darija to English, making it suitable for applications involving the translation of conversational and informal text from Morocco.

Intended Use

This model can be used to:

• Translate sentences from Moroccan Darija to English.

📄 License

This model is released under the Apache 2.0 license.