Question Decomposer T5

🚀 基於T5和Seq2seq的問題分解器

本項目是一個基於T5和Seq2seq的問題分解器，能夠將複雜問題分解為多個簡單子問題，提升問題處理效率。

模型信息

🚀 快速開始

下面是一個示例問題及其分解結果： 示例：What is the capital of France and when it entablish ?

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

點擊下面的鏈接查看演示 👉🏻 demo

💻 使用示例

基礎用法

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?']