README.md

metadata

language: en
datasets:
  - squad
tags:
  - Question Generation
widget:
  - text: >-
      <answer> T5 <context> Cheng fine-tuned T5 on SQuAD for question
      generation.
    example_title: Example 1
  - text: >-
      <answer> SQuAD <context> Cheng fine-tuned T5 on SQuAD dataset for question
      generation.
    example_title: Example 2
  - text: >-
      <answer> thousands <context> Transformers provides thousands of
      pre-trained models to perform tasks on different modalities such as text,
      vision, and audio.
    example_title: Example 3

T5-Base Fine-Tuned on SQuAD for Question Generation

Model in Action:

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?