T5 Base Finetuned For Question Generation

🚀 T5-Base Fine-Tuned on SQuAD for Question Generation

This project presents a T5-base model fine-tuned on the SQuAD dataset for question generation, offering an effective solution for generating relevant questions given an answer and context.

🚀 Quick Start

Prerequisites

Ensure you have torch and transformers libraries installed in your Python environment.

Code Example

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'])
    # Output: 
    # Who fine-tuned T5 on SQuAD dataset for question generation?

✨ Features

• Fine-Tuned on SQuAD: The model is fine-tuned on the SQuAD dataset, which enhances its performance in question generation.
• Easy to Use: With a simple Python class, you can easily generate questions based on given answers and contexts.

📦 Installation

Since the code mainly depends on torch and transformers, you can install them using the following commands:

pip install torch
pip install transformers

💻 Usage Examples

Basic Usage

# The basic usage code is shown above in the quick start section.

Advanced Usage

You can modify the generate method in the QuestionGeneration class to adjust the generation parameters, such as max_length, num_beams, etc., to get different generation results.

# Example of advanced usage
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, max_length=50, num_beams=4):
        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,
            max_length=max_length,
            num_beams=num_beams
        )
        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, max_length=60, num_beams=5)
    print(qa['question'])

📚 Documentation

Model Details

• Model Type: T5-Base fine-tuned for question generation.
• Training Data: SQuAD dataset.

Input and Output

• Input: An answer and a context string.
• Output: A dictionary containing the generated question, the original answer, and the context.

📄 License

No license information provided in the original document.