readme: EconBERTa - RoBERTa further trained for 25k steps (T=512, batch_size = 256) on text sourced from economics books.
  
  Example usage for MLM: 
  
  ```python
  from transformers import RobertaTokenizer, RobertaForMaskedLM
  from transformers import pipeline
  
  tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
  model = RobertaForMaskedLM.from_pretrained('models').cpu()
  model.eval()
  mlm = pipeline('fill-mask', model = model, tokenizer  = tokenizer)
  test = "ECB - euro, FED - <mask>, BoJ - yen"
  print(mlm(test)[:2])
  
  [{'sequence': 'ECB - euro, FED - dollar, BoJ - yen',
    'score': 0.7342271208763123,
    'token': 1404,
    'token_str': ' dollar'},
   {'sequence': 'ECB - euro, FED - dollars, BoJ - yen',
    'score': 0.10828445851802826,
    'token': 1932,
    'token_str': ' dollars'}]
  ```
  
  