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Update tokenization_utils_base.py (#7696)

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Minor spelling corrections in docstrings. "information" is uncountable in English and has no plural.
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AndreaSottana 2020-10-12 11:09:20 +01:00 committed by GitHub
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src/transformers/tokenization_utils_base.py
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@ -159,9 +159,9 @@ class BatchEncoding(UserDict):
Dictionary of lists/arrays/tensors returned by the encode/batch_encode methods ('input_ids',
'attention_mask', etc.).
encoding (:obj:`tokenizers.Encoding` or :obj:`Sequence[tokenizers.Encoding]`, `optional`):
If the tokenizer is a fast tokenizer which outputs additional informations like mapping from word/character
space to token space the :obj:`tokenizers.Encoding` instance or list of instance (for batches) hold these
informations.
If the tokenizer is a fast tokenizer which outputs additional information like mapping from word/character
space to token space the :obj:`tokenizers.Encoding` instance or list of instance (for batches) hold this
information.
tensor_type (:obj:`Union[None, str, TensorType]`, `optional`):
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.
@ -1131,7 +1131,7 @@ ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
return_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return the lengths of the encoded inputs.
verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to print informations and warnings.
Whether or not to print more information and warnings.
**kwargs: passed to the :obj:`self.tokenize()` method
Return:
@ -2309,7 +2309,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to print informations and warnings.
Whether or not to print more information and warnings.
"""
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader