Update README.md

README.md

@@ -24,6 +24,9 @@ model-index:
     - 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
@@ -31,44 +34,85 @@ should probably proofread and complete it, then remove this comment. -->
 
 # 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) on the kde4 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.0371
-- Bleu: 49.6480
+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
+## Model Description
 
-More information needed
+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.
 
-## Intended uses & limitations
+### 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.
 
-More information needed
+## Intended Uses & Limitations
 
-## Training and evaluation data
+### 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.
 
-More information needed
+### 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 procedure
+## Training and Evaluation Data
 
-### Training hyperparameters
+### 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.
 
-The following hyperparameters were used during training:
-- 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
-- lr_scheduler_type: linear
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+### 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 results
+## 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:
 
-### Framework versions
+| Metric        | Value    |
+|---------------|----------|
+| Training Loss | 1.0371   |
+| Evaluation Loss | 1.0371 |
+| BLEU Score    | 49.6480  |
 
-- Transformers 4.42.4
-- Pytorch 2.3.1+cu121
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+- **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.