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echarif/
mBART_for_darija_transaltion

Name: mBART_for_darija_transaltion
Availability: InStock

safetensors

mbart

translation

facebook/mbart-large-cc25

finetune

apache-2.0

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

This model is a fine-tuned version of the mBART model, designed specifically for the Moroccan Darija to English translation task. It is based on Facebook's mBART, a multilingual model capable of handling various language pairs. This fine-tuned model has been trained on a Moroccan Darija dataset to perform accurate translations from Darija to English.

Model Overview

Model Type: mBART (Multilingual BART)
Language Pair: Moroccan Darija → English
Task: Machine Translation
Training Dataset: The model was fine-tuned on a custom dataset containing Moroccan Darija to English translation pairs.

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.

How to Use the Model

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

python

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}")