AventIQ-AI/t5-text-translator

Text-to-Text Transfer Transformer (T5) Quantized Model for Text Translation

This repository hosts a quantized version of the T5 model, fine-tuned for text translation tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

Model Details

• Model Architecture: T5
• Task: Text Translation
• Dataset: Hugging Face's opus100
• Quantization: Float16
• Supporting Languages: English to French
• Fine-tuning Framework: Hugging Face Transformers

Usage

Installation

pip install transformers torch

Loading the Model

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/t5-text-translator"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)

def translate_text(model, text, src_lang, tgt_lang):
    input_text = f"translate {src_lang} to {tgt_lang}: {text}"
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)

    # Generate translation
    output_ids = model.generate(input_ids, max_length=50)
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Test Example
test_sentences = {"en-fr": "Hello, what is your name?"}

for lang_pair, sentence in test_sentences.items():
    src, tgt = lang_pair.split("-")
    print(f"{src} → {tgt}: {translate_text(model, sentence, src, tgt)}")

📊 ROUGE Evaluation Results

After fine-tuning the T5-Small model for text translation, we obtained the following ROUGE scores:

Metric	Score	Meaning
ROUGE-1	0.4673 (~46%)	Measures overlap of unigrams (single words) between the reference and generated text.
ROUGE-2	0.2486 (~24%)	Measures overlap of bigrams (two-word phrases), indicating coherence and fluency.
ROUGE-L	0.4595 (~45%)	Measures longest matching word sequences, testing sentence structure preservation.
ROUGE-Lsum	0.4620 (~46%)	Similar to ROUGE-L but optimized for summarization tasks.

Fine-Tuning Details

Dataset

The Hugging Face's opus100 dataset was used, containing different types of translations of languages.

Training

• Number of epochs: 3
• Batch size: 8
• Evaluation strategy: epoch

Quantization

Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation

Limitations

• The model may not generalize well to domains outside the fine-tuning dataset.
• Currently, it only supports English to French translations.
• Quantization may result in minor accuracy degradation compared to full-precision models.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.