readme: # T5 for abstractive question-answering
  This is T5-base model fine-tuned for abstractive QA using text-to-text approach
  
  ## Model training
  This model was trained on colab TPU with 35GB RAM for 2 epochs
  
  ## Model in Action 🚀
  ```
  from transformers import AutoModelWithLMHead, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("tuner007/t5_abs_qa")
  model = AutoModelWithLMHead.from_pretrained("tuner007/t5_abs_qa")
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model = model.to(device)
  
  def get_answer(question, context):
  input_text = "context: %s <question for context: %s </s>" % (context,question)
  features = tokenizer([input_text], return_tensors='pt')
  out = model.generate(input_ids=features['input_ids'].to(device), attention_mask=features['attention_mask'].to(device))
  return tokenizer.decode(out[0])
  ```
  #### Example 1: Answer available
  ```
  context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
  question = "What is Valhalla?"
  get_answer(question, context)
  # output: 'It is a hall of worship ruled by Odin.'
  ```
  #### Example 2: Answer not available 
  ```
  context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
  question = "What is Asgard?"
  get_answer(question, context)
  # output: 'No answer available in context.'
  ```
  
  
  > Created by Arpit Rajauria
  [![Twitter icon](https://cdn0.iconfinder.com/data/icons/shift-logotypes/32/Twitter-32.png)](https://twitter.com/arpit_rajauria)
  