readme: # StackOBERTflow-comments-small
  
  StackOBERTflow is a RoBERTa model trained on StackOverflow comments.
  A Byte-level BPE tokenizer with dropout was used (using the `tokenizers` package).
  
  The model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens. 
  The model was trained for 6 epochs on several GBs of comments from the StackOverflow corpus.
  
  ## Quick start: masked language modeling prediction
  
  ```python
  from transformers import pipeline
  from pprint import pprint
  
  COMMENT = "You really should not do it this way, I would use <mask> instead."
  
  fill_mask = pipeline(
      "fill-mask",
      model="giganticode/StackOBERTflow-comments-small-v1",
      tokenizer="giganticode/StackOBERTflow-comments-small-v1"
  )
  
  pprint(fill_mask(COMMENT))
  # [{'score': 0.019997311756014824,
  #   'sequence': '<s> You really should not do it this way, I would use jQuery instead.</s>',
  #   'token': 1738},
  #  {'score': 0.01693696901202202,
  #   'sequence': '<s> You really should not do it this way, I would use arrays instead.</s>',
  #   'token': 2844},
  #  {'score': 0.013411642983555794,
  #   'sequence': '<s> You really should not do it this way, I would use CSS instead.</s>',
  #   'token': 2254},
  #  {'score': 0.013224546797573566,
  #   'sequence': '<s> You really should not do it this way, I would use it instead.</s>',
  #   'token': 300},
  #  {'score': 0.011984303593635559,
  #   'sequence': '<s> You really should not do it this way, I would use classes instead.</s>',
  #   'token': 1779}]
  ```
  