@@ -43,21 +43,21 @@ class PreTrainedTokenizer(object):
Parameters:
- ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token``
- ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token`` and ``self.bos_token_id``
- ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token``
- ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token`` and ``self.eos_token_id``
- ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token``
- ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token`` and ``self.unk_token_id``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token`` and ``self.sep_token_id``
- ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token``
- ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token`` and ``self.pad_token_id``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token`` and ``self.cls_token_id``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
"""
vocab_files_names={}
pretrained_vocab_files_map={}
...
...
@@ -494,6 +494,13 @@ class PreTrainedTokenizer(object):
to class attributes. If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Using `add_special_tokens` will ensure your special tokens can be used in several ways:
- special tokens are carefully handled by the tokenizer (they are never split)
- you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
