parameters: (do_lower_case, init_checkpoint)
docstring: Checks whether the casing config is consistent with the checkpoint name.
docstring_summary: Checks whether the casing config is consistent with the checkpoint name.
docstring_tokens: Checks whether the casing config is consistent with the checkpoint name .
function: def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
    """Checks whether the casing config is consistent with the checkpoint name."""
  
    # The casing has to be passed in by the user and there is no explicit check
    # as to whether it matches the checkpoint. The casing information probably
    # should have been stored in the bert_config.json file, but it's not, so
    # we have to heuristically detect it to validate.
  
    if not init_checkpoint:
      return
  
    m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
    if m is None:
      return
  
    model_name = m.group(1)
  
    lower_models = [
        "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
        "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
    ]
  
    cased_models = [
        "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
        "multi_cased_L-12_H-768_A-12"
    ]
  
    is_bad_config = False
    if model_name in lower_models and not do_lower_case:
      is_bad_config = True
      actual_flag = "False"
      case_name = "lowercased"
      opposite_flag = "True"
  
    if model_name in cased_models and do_lower_case:
      is_bad_config = True
      actual_flag = "True"
      case_name = "cased"
      opposite_flag = "False"
  
    if is_bad_config:
      raise ValueError(
          "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
          "However, `%s` seems to be a %s model, so you "
          "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
          "how the model was pre-training. If this error is wrong, please "
          "just comment out this check." % (actual_flag, init_checkpoint,
                                            model_name, case_name, opposite_flag))
function_tokens: def validate_case_matches_checkpoint ( do_lower_case , init_checkpoint ) : # The casing has to be passed in by the user and there is no explicit check # as to whether it matches the checkpoint. The casing information probably # should have been stored in the bert_config.json file, but it's not, so # we have to heuristically detect it to validate. if not init_checkpoint : return m = re . match ( "^.*?([A-Za-z0-9_-]+)/bert_model.ckpt" , init_checkpoint ) if m is None : return model_name = m . group ( 1 ) lower_models = [ "uncased_L-24_H-1024_A-16" , "uncased_L-12_H-768_A-12" , "multilingual_L-12_H-768_A-12" , "chinese_L-12_H-768_A-12" ] cased_models = [ "cased_L-12_H-768_A-12" , "cased_L-24_H-1024_A-16" , "multi_cased_L-12_H-768_A-12" ] is_bad_config = False if model_name in lower_models and not do_lower_case : is_bad_config = True actual_flag = "False" case_name = "lowercased" opposite_flag = "True" if model_name in cased_models and do_lower_case : is_bad_config = True actual_flag = "True" case_name = "cased" opposite_flag = "False" if is_bad_config : raise ValueError ( "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. " "However, `%s` seems to be a %s model, so you " "should pass in `--do_lower_case=%s` so that the fine-tuning matches " "how the model was pre-training. If this error is wrong, please " "just comment out this check." % ( actual_flag , init_checkpoint , model_name , case_name , opposite_flag ) )