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typo fixes in HF Transformers example #3307

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Sep 9, 2024
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typo fixes in HF Transformers example #3307

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12 changes: 6 additions & 6 deletions examples/Huggingface_Transformers/Transformer_handler_generalized.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ def initialize(self, ctx):
self.initialized = True

def preprocess(self, requests):
"""Basic text preprocessing, based on the user's chocie of application mode.
"""Basic text preprocessing, based on the user's choice of application mode.
Args:
requests (str): The Input data in the form of text is passed on to the preprocess
function.
Expand Down Expand Up @@ -193,14 +193,14 @@ def preprocess(self, requests):

# preprocessing text for question_answering.
elif self.setup_config["mode"] == "question_answering":
# TODO Reading the context from a pickeled file or other fromats that
# TODO Reading the context from a pickled file or other formats that
# fits the requirements of the task in hand. If this is done then need to
# modify the following preprocessing accordingly.

# the sample text for question_answering in the current version
# should be formated as dictionary with question and text as keys
# should be formatted as dictionary with question and text as keys
# and related text as values.
# we use this format here seperate question and text for encoding.
# we use this format here separate question and text for encoding.

question_context = ast.literal_eval(input_text)
question = question_context["question"]
Expand All @@ -215,7 +215,7 @@ def preprocess(self, requests):
)
input_ids = inputs["input_ids"].to(self.device)
attention_mask = inputs["attention_mask"].to(self.device)
# making a batch out of the recieved requests
# making a batch out of the received requests
# attention masks are passed for cases where input tokens are padded.
if input_ids.shape is not None:
if input_ids_batch is None:
Expand Down Expand Up @@ -486,7 +486,7 @@ def captum_sequence_forward(inputs, attention_mask=None, position=0, model=None)


def summarize_attributions(attributions):
"""Summarises the attribution across multiple runs
"""Summarizes the attribution across multiple runs
Args:
attributions ([list): attributions from the Layer Integrated Gradients
Returns:
Expand Down
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