marian-finetuned-kde4-en-to-fr

This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr, specifically tailored for English-to-French translation tasks. It was trained on the kde4 dataset, which consists of parallel texts from the KDE project, making it highly specialized in technical and software documentation translation.

Model Description

MarianMT is a neural machine translation model based on the Marian framework, designed for rapid training and inference. This particular model, marian-finetuned-kde4-en-to-fr, leverages the capabilities of the pre-trained opus-mt-en-fr model and further enhances its performance on the KDE4 dataset, which is focused on the translation of software and technical documentation.

Key Features:

• Base Model: Helsinki-NLP/opus-mt-en-fr, a robust English-to-French translation model.
• Fine-Tuned For: Specialized translation of technical and software documentation.
• Architecture: Transformer-based MarianMT, known for efficient and scalable translation capabilities.

Intended Uses & Limitations

Intended Uses:

• Technical Documentation Translation: Translate software documentation, user manuals, and other technical texts from English to French.
• Software Localization: Aid in the localization process by translating software interfaces and messages.
• General English-to-French Translation: While specialized for technical texts, it can also handle general translation tasks.

Limitations:

• Domain-Specific Performance: The model's fine-tuning on technical texts means it excels in those areas but may not perform as well with colloquial language or literary texts.
• Biases: The model may reflect biases present in the training data, particularly around technical jargon and software terminology.
• Limited Language Support: This model is designed specifically for English-to-French translation. It is not suitable for other language pairs without further fine-tuning.

Training and Evaluation Data

Dataset:

• Training Data: The kde4 dataset, which includes parallel English-French sentences derived from the KDE project. This dataset primarily consists of translations relevant to software documentation, user interfaces, and related technical content.
• Evaluation Data: A subset of the kde4 dataset was used for evaluation to ensure the model's effectiveness in the same domain it was trained on.

Data Characteristics:

• Domain: Technical documentation, software localization.
• Language: Primarily English and French.
• Dataset Size: Contains thousands of sentence pairs, providing a robust dataset for fine-tuning in the technical domain.

Training Procedure

Training Hyperparameters:

• Learning Rate: 2e-05
• Train Batch Size: 32
• Eval Batch Size: 64
• Seed: 42
• Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
• Learning Rate Scheduler Type: Linear
• Number of Epochs: 3
• Mixed Precision Training: Native AMP (Automatic Mixed Precision) to optimize training time and memory usage.

Training Results:

Metric	Value
Training Loss	1.0371
Evaluation Loss	1.0371
BLEU Score	49.6480

• Final Evaluation Loss: 1.0371
• BLEU Score: 49.6480, indicating a high level of accuracy in translation.

Framework Versions

• Transformers: 4.42.4
• PyTorch: 2.3.1+cu121
• Datasets: 2.21.0
• Tokenizers: 0.19.1

Usage

You can use this model in a Hugging Face pipeline for translation tasks:

from transformers import pipeline

model_checkpoint = "ashaduzzaman/marian-finetuned-kde4-en-to-fr"
translator = pipeline("translation", model=model_checkpoint)

# Example usage
input_text = "The user manual provides detailed instructions on how to use the software."
translation = translator(input_text)
print(translation)

Acknowledgments

This model was developed using the Hugging Face Transformers library and fine-tuned using the kde4 dataset. Special thanks to the contributors of the KDE project for providing a rich source of multilingual technical content.