Correct docstring.

[Fraser-Greenlee]

Related issue: #8837

What does this PR do?

Updating the PreTrainedTokenizerBase.pad argument default value docstring to show the correct default value.

Current
docstring:

transformers/src/transformers/tokenization_utils_base.py

Lines 2469 to 2470 in d5b3e56

	padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`False`):
	Select a strategy to pad the returned sequences (according to the model's padding side and padding

arg:

transformers/src/transformers/tokenization_utils_base.py

Lines 2431 to 2472 in d5b3e56

	def pad(
	self,
	encoded_inputs: Union[
	BatchEncoding,
	List[BatchEncoding],
	Dict[str, EncodedInput],
	Dict[str, List[EncodedInput]],
	List[Dict[str, EncodedInput]],
	],
	padding: Union[bool, str, PaddingStrategy] = True,
	max_length: Optional[int] = None,
	pad_to_multiple_of: Optional[int] = None,
	return_attention_mask: Optional[bool] = None,
	return_tensors: Optional[Union[str, TensorType]] = None,
	verbose: bool = True,
	) -> BatchEncoding:
	"""
	Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
	in the batch.
	Padding side (left/right) padding token ids are defined at the tokenizer level (with ``self.padding_side``,
	``self.pad_token_id`` and ``self.pad_token_type_id``)
	.. note::
	If the ``encoded_inputs`` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
	result will use the same type unless you provide a different tensor type with ``return_tensors``. In the
	case of PyTorch tensors, you will lose the specific device of your tensors however.
	Args:
	encoded_inputs (:class:`~transformers.BatchEncoding`, list of :class:`~transformers.BatchEncoding`, :obj:`Dict[str, List[int]]`, :obj:`Dict[str, List[List[int]]` or :obj:`List[Dict[str, List[int]]]`):
	Tokenized inputs. Can represent one input (:class:`~transformers.BatchEncoding` or :obj:`Dict[str,
	List[int]]`) or a batch of tokenized inputs (list of :class:`~transformers.BatchEncoding`, `Dict[str,
	List[List[int]]]` or `List[Dict[str, List[int]]]`) so you can use this method during preprocessing as
	well as in a PyTorch Dataloader collate function.
	Instead of :obj:`List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
	see the note above for the return type.
	padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`False`):
	Select a strategy to pad the returned sequences (according to the model's padding side and padding
	index) among:

Fixes #8837 (issue)

I'm also curious why this method has default padding=True? Other methods (prepare_for_model, encode, call, encode_plus, batch_encode_plus) have padding=False.

Its default means the DataCollatorForLanguageModeling pads input examples which means it can't be simply switched with the default collator in the example script without breaking the attention mask.

transformers/src/transformers/data/data_collator.py

Line 253 in 610cb10

batch = self.tokenizer.pad(examples, return_tensors="pt")

@mfuntowicz
@LysandreJik
@sgugger

[sgugger]

Thanks for fixing!