From b03b2a653d512e12c6af87fcb223c2dbb053d245 Mon Sep 17 00:00:00 2001 From: Sylvain Gugger Date: Mon, 26 Apr 2021 11:45:04 -0400 Subject: [PATCH] Style --- src/transformers/models/tapas/tokenization_tapas.py | 6 +++--- src/transformers/tokenization_utils.py | 6 +++--- src/transformers/tokenization_utils_base.py | 6 +++--- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/transformers/models/tapas/tokenization_tapas.py b/src/transformers/models/tapas/tokenization_tapas.py index 23d5d99d5c..6e9f439ea1 100644 --- a/src/transformers/models/tapas/tokenization_tapas.py +++ b/src/transformers/models/tapas/tokenization_tapas.py @@ -172,9 +172,9 @@ TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. is_split_into_words (:obj:`bool`, `optional`, defaults to :obj:`False`): - Whether or not the input is already pre-tokenized (e.g., split into words). If set to :obj:`True`, - the tokenizer assumes the input is already split into words (for instance, by splitting it on - whitespace) which it will tokenize. This is useful for NER or token classification. + Whether or not the input is already pre-tokenized (e.g., split into words). If set to :obj:`True`, the + tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) + which it will tokenize. This is useful for NER or token classification. pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). diff --git a/src/transformers/tokenization_utils.py b/src/transformers/tokenization_utils.py index 3ae7affdb3..b4e370803c 100644 --- a/src/transformers/tokenization_utils.py +++ b/src/transformers/tokenization_utils.py @@ -643,9 +643,9 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase): text (:obj:`str`): The text to prepare. is_split_into_words (:obj:`bool`, `optional`, defaults to :obj:`False`): - Whether or not the input is already pre-tokenized (e.g., split into words). If set to :obj:`True`, - the tokenizer assumes the input is already split into words (for instance, by splitting it on - whitespace) which it will tokenize. This is useful for NER or token classification. + Whether or not the input is already pre-tokenized (e.g., split into words). If set to :obj:`True`, the + tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) + which it will tokenize. This is useful for NER or token classification. kwargs: Keyword arguments to use for the tokenization. diff --git a/src/transformers/tokenization_utils_base.py b/src/transformers/tokenization_utils_base.py index cb0a99cd2f..2f160a881b 100644 --- a/src/transformers/tokenization_utils_base.py +++ b/src/transformers/tokenization_utils_base.py @@ -1286,9 +1286,9 @@ ENCODE_KWARGS_DOCSTRING = r""" returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. is_split_into_words (:obj:`bool`, `optional`, defaults to :obj:`False`): - Whether or not the input is already pre-tokenized (e.g., split into words). If set to :obj:`True`, - the tokenizer assumes the input is already split into words (for instance, by splitting it on - whitespace) which it will tokenize. This is useful for NER or token classification. + Whether or not the input is already pre-tokenized (e.g., split into words). If set to :obj:`True`, the + tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) + which it will tokenize. This is useful for NER or token classification. pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).