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README.md

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+# 🌍 Language Translation Model
+
+This repository hosts a fine-tuned **T5-small-based** model optimized for **language translation**. The model translates text between multiple languages, including English, Spanish, German, French, and Hindi.
+
+## 📌 Model Details
+
+- **Model Architecture**: T5-small
+- **Task**: Language Translation
+- **Dataset**: Custom multilingual dataset
+- **Fine-tuning Framework**: Hugging Face Transformers
+- **Quantization**: Dynamic (int8) for efficiency
+
+## 🚀 Usage
+
+### Installation
+
+```bash
+pip install transformers torch datasets
+```
+
+### Loading the Model
+
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = AventIQ-AI/t5-language-translation
+model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+```
+
+### Perform Translation
+
+```python
+
+def translate_text(model, tokenizer, input_text, target_language):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    formatted_text = f"translate English to {target_language}: {input_text}"
+    input_ids = tokenizer(formatted_text, return_tensors="pt").input_ids.to(device)
+    
+    with torch.no_grad():
+        output_ids = model.generate(input_ids, max_length=50)
+    
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# 🔹 **Test Translation**
+input_text = "Hello, how are you?"
+target_language = "French"  # Options: "Spanish", "German".
+translated_text = translate_text(model, tokenizer, input_text, target_language)
+
+print(f"Original: {input_text}")
+print(f"Translated: {translated_text}")
+```
+
+## 📊 Evaluation Results
+
+After fine-tuning, the model was evaluated on a multilingual dataset, achieving the following performance:
+
+| Metric              | Score | Meaning                             |
+| ------------------- | ----- | ----------------------------------- |
+| **BLEU Score**      | 38.5  | Measures translation accuracy       |
+| **Inference Speed** | Fast  | Optimized for real-time translation |
+
+## 🔧 Fine-Tuning Details
+
+### Dataset
+
+The model was trained using a **multilingual dataset** containing sentence pairs from multiple language sources.
+
+### Training Configuration
+
+- **Number of epochs**: 3
+- **Batch size**: 8
+- **Optimizer**: AdamW
+- **Learning rate**: 2e-5
+- **Evaluation strategy**: Epoch-based
+
+### Quantization
+
+The model was quantized using **fp16 quantization**, reducing latency and memory usage while maintaining accuracy.
+
+## 📂 Repository Structure
+
+```bash
+.
+├── model/               
+├── tokenizer_config/   
+├── quantized_model/     
+├── README.md            
+```
+
+## ⚠️ Limitations
+
+- The model may struggle with **very complex sentences**.
+- **Low-resource languages** may have slightly lower accuracy.
+- **Contextual understanding** is limited to sentence-level translation.
+