Question Decomposer T5

🚀 Question decomposer Based t5 and Seq2seq

A text2text - generation model that decomposes complex questions into simpler sub - questions using T5 and Seq2seq architecture.

🚀 Quick Start

This model is designed to decompose complex questions into simpler ones. For example, the question "What is the capital of France and when it entablish ?" can be decomposed into:

• What is the capital of France ?
• When was the capital of France entablish ?

Check out my demo here 👉🏻 demo

✨ Features

💻 Usage Examples

Basic Usage

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Set device
device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"

# Load model and tokenizer
model_path = "thenHung/question_decomposer_t5"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
model.to(device)
model.eval()

# Decompose question
question = "Who is taller between John and Mary?"
input_text = f"decompose question: {question}"
input_ids = tokenizer(
    input_text,
    max_length=128,
    padding="max_length",
    truncation=True,
    return_tensors="pt"
).input_ids.to(device)

with torch.no_grad():
    outputs = model.generate(
        input_ids,
        max_length=128,
        num_beams=4,
        early_stopping=True
    )

# Decode output
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
sub_questions = decoded_output.split(" [SEP] ")

# Print sub-questions
print(sub_questions)
# ['What is the height of John?', 'What is the height of Mary?']