Marian Fine-Tuned KDE4 (English-to-French)

This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr using the KDE4 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9620
• BLEU: 50.5445

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Model Description

This English-to-French translation model has been fine-tuned specifically on the KDE4 dataset. The base model, Helsinki-NLP/opus-mt-en-fr, is part of the MarianMT family, renowned for its efficiency and high-quality neural machine translation capabilities.

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Intended Uses & Limitations

Intended Uses

• Translating English text into French.
• High-quality translations in the context of software localization, especially related to KDE4.

Limitations

• Performance may decline on texts outside the KDE4 domain.
• Struggles with idiomatic expressions, specialized technical jargon, or ambiguous content.

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Training & Evaluation Data

The model was fine-tuned on the KDE4 dataset, a specialized resource for machine translation in software localization. The evaluation metrics reflect the model's performance on this domain-specific data.

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Training Procedure

Hyperparameters

• Learning Rate: 2e-05
• Train Batch Size: 32
• Eval Batch Size: 64
• Seed: 42
• Optimizer: AdamW with betas=(0.9, 0.999), epsilon=1e-08
• LR Scheduler: Linear
• Epochs: 1
• Mixed Precision Training: Native AMP

Results

• Loss: 0.9620
• BLEU: 50.5445

Training Loss Progression

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Framework Versions

• Transformers: 4.47.1
• PyTorch: 2.5.1+cu121
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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Example Usage

from transformers import pipeline

# Load the model
model_checkpoint = "ParitKansal/marian-finetuned-kde4-en-to-fr"
translator = pipeline("translation", model=model_checkpoint)

# Translate text
translation = translator("Default to expanded threads")
print(translation)

This script demonstrates how to use the model for English-to-French translation tasks.

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