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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.
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