marian-finetuned-en-to-fr

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asingh14/marian-finetuned-en-to-fr

Model Details

Model Description

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This is a fine-tuned Marian model for English-to-French translation, fine-tuned on bilingual datasets to provide accurate and fluent translations between English and French. It leverages the MarianMT architecture from Hugging Face, which is specifically designed for machine translation tasks.

• Developed by: Asmita Singh and Shubhangi Nikam
• Funded by [optional]: NA
• Shared by [optional]: NA
• Model type: translation
• Language(s) (NLP): (English (source) and French (target))
• License: Apache 2.0 (inheritance from the original model and dataset)
• Finetuned from model [optional]: Helsinki-NLP/opus-mt-en-fr

Model Sources [optional]

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• Repository: https://huggingface.co/asingh14/marian-finetuned-en-to-fr
• Paper [optional]: NA
• Demo [optional]:

To test the code on google collab run the below code.

from transformers import pipeline

# Load the translation pipeline for English to French
translator = pipeline("translation_en_to_fr", model="asingh14/marian-finetuned-en-to-fr")

# Translate some English sentences to French
translations = translator(
    [
        "I've been waiting for a HuggingFace course my whole life.",
        "I hate this so much!",
    ]
)

# Print the translated text
for translation in translations:
    print(translation['translation_text'])
    

3. Run the file

4. It will give the output:

"J'ai attendu un parcours de HuggingFace toute ma vie. Je hais tant cela!"

which means that the text got translated

Uses

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Direct Use

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This model can be directly used to translate English text into French. It is suitable for bilingual communication, text localization, or any task requiring high-quality English-to-French translation.

Downstream Use [optional]

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The model can be incorporated into larger systems, such as multilingual chatbots, automated translation pipelines, or content localization workflows.

Out-of-Scope Use

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This model is not suitable for translating languages other than English to French. It may not perform well on highly domain-specific or informal text.

Bias, Risks, and Limitations

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• The model may exhibit translation biases present in the dataset used for training. For example, gender-neutral terms in English might be translated into gendered terms in French (e.g., "doctor" → "docteur" or "doctoresse").
• May struggle with idiomatic expressions, rare words, or culturally specific references.

Recommendations

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Users should review translations, especially for critical applications or when using the model for sensitive or nuanced text.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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Citation [optional]

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Glossary [optional]

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