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Unverified Commit fdf893c4 authored by Santiago Castro's avatar Santiago Castro Committed by GitHub
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Fix typo: indinces -> indices (#8159) 

* Fix typo: indinces -> indices

* Fix some more

* Fix some more

* Fix some more

* Fix CI
parent c83cec44
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Showing with 14 additions and 16 deletions
src/transformers/pipelines.py
View file @ fdf893c4
......@@ -122,7 +122,7 @@ def get_default_model(targeted_task: Dict, framework: Optional[str], task_option
Args:
targeted_task (:obj:`Dict` ):
Dictionnary representing the given task, that should contain default models
Dictionary representing the given task, that should contain default models
framework (:obj:`str`, None)
"pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet.
......@@ -150,9 +150,7 @@ def get_default_model(targeted_task: Dict, framework: Optional[str], task_option
else:
# XXX This error message needs to be updated to be more generic if more tasks are going to become
# parametrized
raise ValueError(
'The task defaults can\'t be correctly selectionned. You probably meant "translation_XX_to_YY"'
)
raise ValueError('The task defaults can\'t be correctly selected. You probably meant "translation_XX_to_YY"')
if framework is None:
framework = "pt"
......@@ -695,7 +693,7 @@ class Pipeline(_ScikitCompat):
Internal framework specific forward dispatching
Args:
inputs: dict holding all the keyworded arguments for required by the model forward method.
inputs: dict holding all the keyword arguments for required by the model forward method.
return_tensors: Whether to return native framework (pt/tf) tensors rather than numpy array
Returns:
......@@ -874,7 +872,7 @@ class TextGenerationPipeline(Pipeline):
args (:obj:`str` or :obj:`List[str]`):
One or several prompts (or one list of prompts) to complete.
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indinces) in the outputs.
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
......@@ -1710,7 +1708,7 @@ class QuestionAnsweringPipeline(Pipeline):
question (:obj:`str` or :obj:`List[str]`):
One or several question(s) (must be used in conjunction with the :obj:`context` argument).
context (:obj:`str` or :obj:`List[str]`):
One or several context(s) associated with the qustion(s) (must be used in conjunction with the
One or several context(s) associated with the question(s) (must be used in conjunction with the
:obj:`question` argument).
topk (:obj:`int`, `optional`, defaults to 1):
The number of answers to return (will be chosen by order of likelihood).
......@@ -1959,7 +1957,7 @@ class SummarizationPipeline(Pipeline):
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indinces) in the outputs.
Whether or not to include the tensors of predictions (as token indices) in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
......@@ -2077,7 +2075,7 @@ class TranslationPipeline(Pipeline):
args (:obj:`str` or :obj:`List[str]`):
Texts to be translated.
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indinces) in the outputs.
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
......@@ -2188,7 +2186,7 @@ class Text2TextGenerationPipeline(Pipeline):
args (:obj:`str` or :obj:`List[str]`):
Input text for the encoder.
return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to include the tensors of predictions (as token indinces) in the outputs.
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
......@@ -2253,8 +2251,8 @@ class Conversation:
:class:`~transformers.ConversationalPipeline`. The conversation contains a number of utility function to manage the
addition of new user input and generated model responses. A conversation needs to contain an unprocessed user input
before being passed to the :class:`~transformers.ConversationalPipeline`. This user input is either created when
the class is instantiated, or by calling :obj:`conversional_pipeline.append_response("input")` after a conversation
turn.
the class is instantiated, or by calling :obj:`conversational_pipeline.append_response("input")` after a
conversation turn.
Arguments:
text (:obj:`str`, `optional`):
......@@ -2671,8 +2669,8 @@ def check_task(task: str) -> Tuple[Dict, Any]:
- :obj:`"conversational"`
Returns:
(task_defaults:obj:`dict`, task_options: (:obj:`tuple`, None)) The actual dictionnary required to initialize
the pipeline and some extra task options for parametrized tasks like "translation_XX_to_YY"
(task_defaults:obj:`dict`, task_options: (:obj:`tuple`, None)) The actual dictionary required to initialize the
pipeline and some extra task options for parametrized tasks like "translation_XX_to_YY"
"""
......
src/transformers/tokenization_t5.py
View file @ fdf893c4
......@@ -89,7 +89,7 @@ class T5Tokenizer(PreTrainedTokenizer):
extra_ids (:obj:`int`, `optional`, defaults to 100):
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginnning ("<extra_id_0>" is the last token in the vocabulary
indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
like in T5 preprocessing see `here
<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
additional_special_tokens (:obj:`List[str]`, `optional`):
......
src/transformers/tokenization_t5_fast.py
View file @ fdf893c4
......@@ -100,7 +100,7 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
extra_ids (:obj:`int`, `optional`, defaults to 100):
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginnning ("<extra_id_0>" is the last token in the vocabulary
indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
like in T5 preprocessing see `here
<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
additional_special_tokens (:obj:`List[str]`, `optional`):
......
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