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--- |
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license: apache-2.0 |
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metrics: |
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- bleu |
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base_model: |
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- facebook/mbart-large-cc25 |
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pipeline_tag: translation |
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--- |
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# Moroccan Darija to English Translation Model (Fine-Tuned mBART) |
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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. |
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## Model Overview |
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- **Model Type**: mBART (Multilingual BART) |
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- **Language Pair**: Moroccan Darija → English |
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- **Task**: Machine Translation |
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- **Training Dataset**: The model was fine-tuned on a custom dataset containing Moroccan Darija to English translation pairs. |
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## Model Details |
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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. |
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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. |
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## Intended Use |
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This model can be used to: |
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- Translate sentences from Moroccan Darija to English. |
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## How to Use the Model |
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You can easily load the model and tokenizer using the Hugging Face `transformers` library. Here's an example: |
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```python |
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast |
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# Load the pre-trained model and tokenizer |
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model_name = 'echarif/mBART_for_darija_transaltion' |
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model = MBartForConditionalGeneration.from_pretrained(model_name) |
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tokenizer = MBart50TokenizerFast.from_pretrained(model_name) |
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# Prepare your input text (Moroccan Darija) |
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input_text = "insert your Moroccan Darija sentence here" |
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# Tokenize the input text |
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inputs = tokenizer(input_text, return_tensors="pt", padding=True) |
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# Generate the translated output |
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translated_tokens = model.generate(**inputs) |
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translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True) |
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print(f"Translated Text: {translated_text}") |
