(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))
		if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
			# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
			# than the number of positional embeddings
			max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
			kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
		# 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 ) ) if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP : # if we're using a pretrained model, ensure the tokenizer wont index sequences longer # than the number of positional embeddings max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP [ pretrained_model_name ] kwargs [ 'max_len' ] = min ( kwargs . get ( 'max_len' , int ( 1e12 ) ) , max_len ) # Instantiate tokenizer. tokenizer = cls ( resolved_vocab_file , * inputs , * * kwargs ) return tokenizer