Method from_pretrained

bert/tokenization_utils_base.py:1088–1140

r""" Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer. Args: pretrained_model_name_or_path: either: - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g

(cls, *inputs, **kwargs)

    @classmethod
    def from_pretrained(cls, *inputs, **kwargs):
        r"""
        Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.

        Args:
            pretrained_model_name_or_path: either:

                - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
                - a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
                - (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.

            cache_dir: (`optional`) string:
                Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.

            force_download: (`optional`) boolean, default False:
                Force to (re-)download the vocabulary files and override the cached versions if they exists.

            resume_download: (`optional`) boolean, default False:
                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.

            proxies: (`optional`) dict, default None:
                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                The proxies are used on each request.

            inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.

            kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.

        Examples::

            # We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer

            # Download vocabulary from S3 and cache.
            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

            # Download vocabulary from S3 (user-uploaded) and cache.
            tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')

            # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
            tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')

            # If the tokenizer uses a single vocabulary file, you can point directly to this file
            tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')

            # You can link tokens to special vocabulary when instantiating
            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
            # You should be sure '<unk>' is in the vocabulary when doing that.
            # Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
            assert tokenizer.unk_token == '<unk>'

        """
        return cls._from_pretrained(*inputs, **kwargs)

    @classmethod
    def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):