Update README.md

README.md

@@ -30,46 +30,94 @@ model-index:
 <!-- 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
+# Marian Fine-Tuned KDE4 (English-to-French)
 
-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: 0.9620
-- Bleu: 50.5445
+This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/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
 
-## Model description
+---
+
+## Model Description
 
-More information needed
+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. 
 
-## Intended uses & limitations
+---
 
-More information needed
+## Intended Uses & Limitations
 
-## Training and evaluation data
+### Intended Uses
+- Translating English text into French.
+- High-quality translations in the context of software localization, especially related to KDE4.
 
-More information needed
+### Limitations
+- Performance may decline on texts outside the KDE4 domain.
+- Struggles with idiomatic expressions, specialized technical jargon, or ambiguous content.
 
-## Training procedure
+---
 
-### Training hyperparameters
+## 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.
+
+---
+
+## 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
+
+| Step  | Training Loss |
+|-------|---------------|
+| 500   | 1.2253        |
+| 1000  | 1.2165        |
+| 1500  | 1.1913        |
+| 2000  | 1.1404        |
+| 2500  | 1.1178        |
+| 3000  | 1.0900        |
+| 3500  | 1.0594        |
+| 4000  | 1.0512        |
+| 4500  | 1.0633        |
+| 5000  | 1.0405        |
+| 5500  | 1.0316        |
+
+---
+
+## Framework Versions
+- **Transformers**: 4.47.1  
+- **PyTorch**: 2.5.1+cu121  
+- **Datasets**: 3.2.0  
+- **Tokenizers**: 0.21.0  
+
+---
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 32
-- eval_batch_size: 64
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 1
-- mixed_precision_training: Native AMP
+## Example Usage
 
-### Training results
+```python
+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)
+```
 
-### Framework versions
+This script demonstrates how to use the model for English-to-French translation tasks.
 
-- Transformers 4.47.1
-- Pytorch 2.5.1+cu121
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+---