(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs)
return tokenizer
Instantiate a PreTrainedBertModel from a pre-trained model file.
        Download and cache the pre-trained model file if needed.
Instantiate a PreTrainedBertModel from a pre-trained model file.
        Download and cache the pre-trained model file if needed.
Instantiate a PreTrainedBertModel from a pre - trained model file . Download and cache the pre - trained model file if needed .
def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
        """
        Instantiate a PreTrainedBertModel from a pre-trained model file.
        Download and cache the pre-trained model file if needed.
        """
        if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
        else:
            vocab_file = pretrained_model_name
        if os.path.isdir(vocab_file):
            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
        # redirect to the cache, if necessary
        try:
            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
        except FileNotFoundError:
            logger.error(
                "Model name '{}' was not found in model name list ({}). "
                "We assumed '{}' was a path or url but couldn't find any file "
                "associated to this path or url.".format(
                    pretrained_model_name,
                    ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
                    vocab_file))
            return None
        if resolved_vocab_file == vocab_file:
            logger.info("loading vocabulary file {}".format(vocab_file))
        else:
            logger.info("loading vocabulary file {} from cache at {}".format(
                vocab_file, resolved_vocab_file))
        # Instantiate tokenizer.
        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
        return tokenizer
def from_pretrained ( cls , pretrained_model_name , cache_dir = None , * inputs , * * kwargs ) : if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP : vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP [ pretrained_model_name ] else : vocab_file = pretrained_model_name if os . path . isdir ( vocab_file ) : vocab_file = os . path . join ( vocab_file , VOCAB_NAME ) # redirect to the cache, if necessary try : resolved_vocab_file = cached_path ( vocab_file , cache_dir = cache_dir ) except FileNotFoundError : logger . error ( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find any file " "associated to this path or url." . format ( pretrained_model_name , ', ' . join ( PRETRAINED_VOCAB_ARCHIVE_MAP . keys ( ) ) , vocab_file ) ) return None if resolved_vocab_file == vocab_file : logger . info ( "loading vocabulary file {}" . format ( vocab_file ) ) else : logger . info ( "loading vocabulary file {} from cache at {}" . format ( vocab_file , resolved_vocab_file ) ) # Instantiate tokenizer. tokenizer = cls ( resolved_vocab_file , * inputs , * * kwargs ) return tokenizer