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README.md

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+Here’s a **README** template for your project, designed to highlight the models used, evaluation methodology, and key results. You can adapt this for Hugging Face or any similar platform.
+
+---
+
+# **English-to-Japanese Translation Project**
+
+## **Overview**
+This project focuses on building a robust system for English-to-Japanese translation using state-of-the-art multilingual models. Two models were used: **mT5** as the primary model and **mBART** as the secondary model. Together, they ensure high-quality translations and versatility in multilingual tasks.
+
+---
+
+## **Models Used**
+
+### **1. mT5 (Primary Model)**
+- **Reason for Selection**:  
+  - mT5 is highly versatile and trained on a broad multilingual dataset, making it suitable for translation and other tasks like summarization or answering questions.  
+  - It performs well without extensive fine-tuning, saving computational resources.  
+
+- **Strengths**:  
+  - Handles translation naturally with minimal training.  
+  - Can perform additional tasks beyond translation.  
+
+- **Limitations**:  
+  - Sometimes lacks precision in detailed translations.  
+
+---
+
+### **2. mBART (Secondary Model)**
+- **Reason for Selection**:  
+  - mBART specializes in multilingual translation tasks and provides highly accurate translations when fine-tuned.  
+
+- **Strengths**:  
+  - Optimized for translation accuracy, especially for long sentences and contextual consistency.  
+  - Handles grammatical and contextual errors well.  
+
+- **Limitations**:  
+  - Less flexible for tasks like summarization or question answering compared to mT5.  
+
+---
+
+## **Evaluation Strategy**
+
+To evaluate model performance, the following metrics were used:  
+
+1. **BLEU Score**:  
+   - Measures how close the model's output is to the correct translation.  
+   - Chosen because it is a standard for evaluating translation accuracy.  
+
+2. **Training Loss**:  
+   - Tracks how well the model is learning during training.  
+   - A lower loss shows better learning and fewer errors.  
+
+3. **Perplexity**:  
+   - Checks the confidence of the model’s predictions.  
+   - Lower perplexity means fewer mistakes and more fluent translations.  
+
+---
+
+## **Steps Taken**
+1. Fine-tuned both models using a dataset of English-Japanese text pairs to improve translation accuracy.  
+2. Tested the models on unseen data to measure their real-world performance.  
+3. Applied optimizations like **4-bit quantization** to reduce memory usage and make the models faster during evaluation.  
+
+---
+
+## **Results**
+- **mT5**:  
+  - Performed well in handling translations and additional tasks like summarization and answering questions.  
+  - Showed versatility but sometimes lacked detailed accuracy for translations.  
+
+- **mBART**:  
+  - Delivered precise and contextually accurate translations, especially for longer sentences.  
+  - Required fine-tuning but outperformed mT5 in translation-focused tasks.  
+
+- **Overall Conclusion**:  
+  mT5 is a flexible model for multilingual tasks, while mBART ensures high-quality translations. Together, they balance versatility and accuracy, making them ideal for English-to-Japanese translations.  
+
+---
+
+## **How to Use**
+1. Load the models from Hugging Face:  
+   - [mT5 Model on Hugging Face](https://huggingface.co/google/mt5-small)  
+   - [mBART Model on Hugging Face](https://huggingface.co/facebook/mbart-large-50)  
+
+2. Fine-tune the models for your dataset using English-Japanese text pairs.  
+3. Evaluate performance using BLEU Score, training loss, and perplexity.  
+
 ---
-title: FinalProject
-emoji: 🔥
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.8.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Translation
+
+## **Future Work**
+- Expand the dataset for better fine-tuning.  
+- Explore task-specific fine-tuning for mT5 to improve its translation accuracy.  
+- Optimize the models further for deployment in resource-constrained environments.  
+
+---
+
+## **References**
+- [mT5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/2010.11934)  
+- [mBART: Multilingual Denoising Pretraining for Neural Machine Translation](https://arxiv.org/abs/2001.08210)  
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference