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Documentation (#2989)

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* All Tokenizers

BertTokenizer + few fixes
RobertaTokenizer
OpenAIGPTTokenizer + Fixes
GPT2Tokenizer + fixes
TransfoXLTokenizer
Correct rst for TransformerXL
XLMTokenizer + fixes
XLNet Tokenizer + Style
DistilBERT + Fix XLNet RST
CTRLTokenizer
CamemBERT Tokenizer
FlaubertTokenizer
XLMRobertaTokenizer
cleanup

* cleanup
This commit is contained in:
Lysandre Debut
2020-02-25 18:43:36 -05:00
committed by GitHub
parent c913eb9c38
commit bb7c468520
30 changed files with 866 additions and 242 deletions
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109
src/transformers/tokenization_roberta.py
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@@ -16,6 +16,7 @@
import logging
from typing import List, Optional
from tokenizers.processors import RobertaProcessing
@@ -60,12 +61,59 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
class RobertaTokenizer(GPT2Tokenizer):
"""
RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities:
- Byte-level Byte-Pair-Encoding
- Requires a space to start the input string => the encoding methods should be called with the
``add_prefix_space`` flag set to ``True``.
Otherwise, this tokenizer ``encode`` and ``decode`` method will not conserve
the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"`
Constructs a RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities:
- Byte-level Byte-Pair-Encoding
- Requires a space to start the input string => the encoding methods should be called with the
``add_prefix_space`` flag set to ``True``.
Otherwise, this tokenizer ``encode`` and ``decode`` method will not conserve
the absence of a space at the beginning of a string:
::
tokenizer.decode(tokenizer.encode("Hello")) = " Hello"
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users
should refer to the superclass for more information regarding methods.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
merges_file (:obj:`str`):
Path to the merges file.
errors (:obj:`str`, `optional`, defaults to "replace"):
Paradigm to follow when decoding bytes to UTF-8. See `bytes.decode
<https://docs.python.org/3/library/stdtypes.html#bytes.decode>`__ for more information.
bos_token (:obj:`string`, `optional`, defaults to "<s>"):
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the beginning
of sequence. The token used is the :obj:`cls_token`.
eos_token (:obj:`string`, `optional`, defaults to "</s>"):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end
of sequence. The token used is the :obj:`sep_token`.
sep_token (:obj:`string`, `optional`, defaults to "</s>"):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences
for sequence classification or for a text and a question for question answering.
It is also used as the last token of a sequence built with special tokens.
cls_token (:obj:`string`, `optional`, defaults to "<s>"):
The classifier token which is used when doing sequence classification (classification of the whole
sequence instead of per-token classification). It is the first token of the sequence when built with
special tokens.
unk_token (:obj:`string`, `optional`, defaults to "<unk>"):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (:obj:`string`, `optional`, defaults to "<pad>"):
The token used for padding, for example when batching sequences of different lengths.
mask_token (:obj:`string`, `optional`, defaults to "<mask>"):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
"""
vocab_files_names = VOCAB_FILES_NAMES
@@ -102,13 +150,25 @@ class RobertaTokenizer(GPT2Tokenizer):
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 4 # take into account special tokens
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A RoBERTa sequence has the following format:
single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>
- single sequence: ``<s> X </s>``
- pair of sequences: ``<s> A </s></s> B </s>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
@@ -116,20 +176,23 @@ class RobertaTokenizer(GPT2Tokenizer):
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
:obj:`List[int]`: A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
@@ -143,12 +206,22 @@ class RobertaTokenizer(GPT2Tokenizer):
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
RoBERTa does not make use of token type ids, therefore a list of zeros is returned.
if token_ids_1 is None, only returns the first portion of the mask (0's).
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]