readme: ---
  language: en
  datasets:
  - squad
  tags:
  - Question Generation
  widget:
   - text: "<answer> T5v1.1 <context> Cheng fine-tuned T5v1.1 on SQuAD for question generation."
     example_title: "Example 1"
   - text: "<answer> SQuAD <context> Cheng fine-tuned T5v1.1 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"
  ---
  
  # T5v1.1-Base Fine-Tuned on SQuAD for Question Generation
  
  ### Model in Action:
  
  ```python
  import torch
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  
  trained_model_path = 'ZhangCheng/T5v1.1-Base-Fine-Tuned-for-Question-Generation'
  trained_tokenizer_path = 'ZhangCheng/T5v1.1-Base-Fine-Tuned-for-Question-Generation'
  
  class QuestionGeneration:
  
      def __init__(self):
          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'].to(self.device)
          attention_mask = encoding['attention_mask'].to(self.device)
          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}
  
  if __name__ == "__main__":
      context = 'ZhangCheng fine-tuned T5v1.1 on SQuAD dataset for question generation.'
      answer = 'ZhangCheng'
      QG = QuestionGeneration()
      qa = QG.generate(answer, context)
      print(qa['question'])
      # Output: 
      # Who fine-tuned T5v1.1 on SQuAD?
  ```
  