readme: 
  ## Overview
  ```
  Language model: Pegasus-xsum
  Language: English
  Downstream-task: Question-Answering Generation
  Training data: SQuAD 2.0, NewsQA
  Eval data: SQuAD 2.0, NewsQA
  Infrastructure: Nvidia Tesla K80 12Gb RAM
  ```
  
  ## Hyperparameters
  ```
  per_device_train_batch_size = 2
  per_device_eval_batch_size = 2
  num_train_epochs = 3
  base_LM_model = "pegasus-xsum"
  source_max_token_len = 256
  target_max_token_len = 64
  learning_rate = 5e-5
  lr_schedule = LinearWarmup
  warmup_steps = 150
  ```
  
  ## Usage
  ```python
  import transformers
  from transformers import PegasusForConditionalGeneration, PegasusTokenizerFast
  
  model_name = 'nloc2578/QAG_Pegasus_3ep_eval'
  tokenizer = PegasusTokenizerFast.from_pretrained(model_name)
  model = PegasusForConditionalGeneration.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id)
  
  text = '''The primary goal of distractor generation is generating answer
  options that are plausibly answers to the question, and might appear
  correct to a user who does know the correct answer. Distractors
  should also be clearly distinct from the key and each other and
  they should not be correct answers to the question (for questions
  that might have multiple correct answers).'''
  
  input_id = tokenizer(text, return_tensors='pt')
  output = model.generate(input_id['input_ids'])
  result = tokenizer.decode(output[0])
  
  print(result)
  ```