README.md

---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation, supervised, kde4
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: kde4
      type: kde4
      config: en-fr
      split: train
      args: en-fr
    metrics:
    - name: Bleu
      type: bleu
      value: 49.64800786424299
language:
- en
pipeline_tag: translation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# marian-finetuned-kde4-en-to-fr

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co./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](https://huggingface.co./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:

```python
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](https://huggingface.co./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.