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README.md

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+# Marian Fine-tuned English-French Translation Model
+
+## Model Description
+
+This model is a fine-tuned version of `Helsinki-NLP/opus-mt-en-fr`, specifically trained for English to French translation. The base model was further trained on the `KDE4` dataset to improve translation quality for technical and software-related content. 
+
+## Model Training Details
+
+### Training Dataset
+- **Dataset**: KDE4 Dataset (English-French parallel corpus)
+- **Split Distribution**:
+  - Training set: 189,155 examples (90%)
+  - Test set: 21,018 examples (10%)
+
+### Training Configuration
+- **Base Model**: Helsinki-NLP/opus-mt-en-fr
+- **Training Arguments**:
+  - Learning rate: 2e-5
+  - Batch size: 32 (training), 64 (evaluation)
+  - Number of epochs: 10
+  - Weight decay: 0.01
+  - FP16 training enabled
+  - Evaluation strategy: Before and after training
+  - Checkpoint saving: Every epoch (maximum 3 saved)
+  - Training device: GPU with mixed precision (fp16)
+
+## Model Results
+  
+### Evaluation Metrics  
+
+The model was evaluated using the BLEU score. The evaluation results before and after training are summarized in the table below:  
+
+| **Stage**         | **Eval Loss** | **BLEU Score** |  
+|--------------------|---------------|----------------|  
+| **Before Training** | 1.700         | 38.97          |  
+| **After Training**  | 0.796         | 54.96          |  
+
+
+### Training Loss
+
+The training loss decreased over the epochs, indicating that the model was learning effectively. The final training loss was approximately 0.710.
+
+
+## Model Usage
+
+
+```python
+from transformers import pipeline
+
+model_checkpoint = "Prikshit7766/marian-finetuned-kde4-en-to-fr"
+translator = pipeline("translation", model=model_checkpoint)
+translator("Default to expanded threads")
+```
+
+### Example Output
+
+```plaintext
+[{'translation_text': 'Par défaut, développer les fils de discussion'}]
+```