readme: ---
  license: afl-3.0
  ---
  
  # Question generation using T5 transformer trained on SQuAD
  
  <h2> <i>Input format: context: "..." answer: "..." </i></h2>
  
  Import the pretrained model as well as tokenizer:
  ```
  from transformers import T5ForConditionalGeneration, T5Tokenizer
  
  model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-squad_v1') 
  tokenizer = T5Tokenizer.from_pretrained('AbhilashDatta/T5_qgen-squad_v1')
  ```
  
  Then use the tokenizer to encode/decode and model to generate: 
  
  ```
  input = "context: My name is Abhilash Datta. answer: Abhilash"
  batch = tokenizer(input, padding='longest', max_length=512, return_tensors='pt')
  inputs_batch = batch['input_ids'][0]
  inputs_batch = torch.unsqueeze(inputs_batch, 0)
  
  ques_id = model.generate(inputs_batch, max_length=100, early_stopping=True)
  ques_batch = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in ques_id]
  
  print(ques_batch)
  ```
  
  Output:
  ```
  ['what is my name']
  ```