T5 Base Finetuned For Question Generation

🚀 T5-Base在SQuAD上微調用於問題生成

本項目是將T5-Base模型在SQuAD數據集上進行微調，以實現問題生成的功能。該模型能夠根據給定的答案和上下文，生成與之相關的問題。

🚀 快速開始

代碼示例

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

trained_model_path = 'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'
trained_tokenizer_path = 'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'

class QuestionGeneration:

    def __init__(self, model_dir=None):
        self.model = T5ForConditionalGeneration.from_pretrained(trained_model_path)
        self.tokenizer = T5Tokenizer.from_pretrained(trained_tokenizer_path)
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = self.model.to(self.device)
        self.model.eval()

    def generate(self, answer: str, context: str):
        input_text = '<answer> %s <context> %s ' % (answer, context)
        encoding = self.tokenizer.encode_plus(
            input_text,
            return_tensors='pt'
        )
        input_ids = encoding['input_ids']
        attention_mask = encoding['attention_mask']
        outputs = self.model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask
        )
        question = self.tokenizer.decode(
            outputs[0],
            skip_special_tokens=True,
            clean_up_tokenization_spaces=True
        )
        return {'question': question, 'answer': answer, 'context': context}

if __name__ == "__main__":
    context = 'ZhangCheng fine-tuned T5 on SQuAD dataset for question generation.'
    answer = 'ZhangCheng'
    QG = QuestionGeneration()
    qa = QG.generate(answer, context)
    print(qa['question'])
    # 輸出: 
    # Who fine-tuned T5 on SQuAD dataset for question generation?

📚 詳細文檔

模型信息

示例展示

本模型提供了一些示例，展示瞭如何根據給定的答案和上下文生成問題：

• 示例1：
  • 輸入：<answer> T5 <context> Cheng fine-tuned T5 on SQuAD for question generation.
• 示例2：
  • 輸入：<answer> SQuAD <context> Cheng fine-tuned T5 on SQuAD dataset for question generation.
• 示例3：
  • 輸入：<answer> thousands <context> Transformers provides thousands of pre-trained models to perform tasks on different modalities such as text, vision, and audio.