def predict(self, X=None, return_logit=False, n_predictions=None):
""" Compute prediction of an answer to a question
Parameters
----------
X: str or list of strings
Sample (question) or list of samples to perform a prediction on
return_logit: boolean
Whether to return logit of best answer or not. Default: False
Returns
-------
If X is str
prediction: tuple (answer, title, paragraph)
If X is list os strings
predictions: list of tuples (answer, title, paragraph)
If return_logits is True, each prediction tuple will have the following
structure: (answer, title, paragraph, best logit)
"""
if isinstance(X, str):
closest_docs_indices = self.retriever.predict(
X, metadata=self.metadata)
squad_examples = generate_squad_examples(
question=X,
closest_docs_indices=closest_docs_indices,
metadata=self.metadata,
retrieve_by_doc=self.retrieve_by_doc,
)
examples, features = self.processor_predict.fit_transform(
X=squad_examples)
prediction = self.reader.predict((examples, features),
return_logit, n_predictions)
return prediction
elif isinstance(X, list):
predictions = []
for query in X:
closest_docs_indices = self.retriever.predict(
query, metadata=self.metadata)
squad_examples = generate_squad_examples(
question=query,
closest_docs_indices=closest_docs_indices,
metadata=self.metadata,
)
examples, features = self.processor_predict.fit_transform(
X=squad_examples)
pred = self.reader.predict((examples, features), return_logit,
n_predictions)
predictions.append(pred)
return predictions
else:
raise TypeError("The input is not a string or a list. \
Please provide a string or a list of strings as input"
)
def predict(self, X=None):
""" Compute prediction of an answer to a question
Parameters
----------
X: str or list of strings
Sample (question) or list of samples to perform a prediction on
Returns
-------
If X is str
prediction: tuple (answer, title, paragraph)
If X is list os strings
predictions: list of tuples (answer, title, paragraph)
"""
if (isinstance(X, str)):
closest_docs_indices = self.retriever.predict(
X, metadata=self.metadata)
squad_examples = generate_squad_examples(
question=X,
closest_docs_indices=closest_docs_indices,
metadata=self.metadata)
examples, features = self.processor_predict.fit_transform(
X=squad_examples)
prediction = self.reader.predict((examples, features))
return prediction
elif (isinstance(X, list)):
predictions = []
for query in X:
closest_docs_indices = self.retriever.predict(
query, metadata=self.metadata)
squad_examples = generate_squad_examples(
question=query,
closest_docs_indices=closest_docs_indices,
metadata=self.metadata)
examples, features = self.processor_predict.fit_transform(
X=squad_examples)
pred = self.reader.predict((examples, features))
predictions.append(pred)
return predictions
else:
raise TypeError("The input is not a string or a list. \
Please provide a string or a list of strings as input"
)
def predict(
self,
query: str = None,
n_predictions: int = None,
retriever_score_weight: float = 0.35,
return_all_preds: bool = False,
):
""" Compute prediction of an answer to a question
Parameters
----------
X: str
Sample (question) to perform a prediction on
n_predictions: int or None (default: None).
Number of returned predictions. If None, only one prediction is return
retriever_score_weight: float (default: 0.35).
The weight of retriever score in the final score used for prediction.
Given retriever score and reader average of start and end logits, the final score used for ranking is:
final_score = retriever_score_weight * retriever_score + (1 - retriever_score_weight) * (reader_avg_logit)
return_all_preds: boolean (default: False)
whether to return a list of all predictions done by the Reader or not
Returns
-------
if return_all_preds is False:
prediction: tuple (answer, title, paragraph, score/logit)
if return_all_preds is True:
List of dictionnaries with all metadada of all answers outputted by the Reader
given the question.
"""
if not isinstance(query, str):
raise TypeError(
"The input is not a string. Please provide a string as input.")
if not (isinstance(n_predictions, int) or n_predictions is None
or n_predictions < 1):
raise TypeError(
"n_predictions should be a positive Integer or None")
best_idx_scores = self.retriever.predict(query)
squad_examples = generate_squad_examples(
question=query,
best_idx_scores=best_idx_scores,
metadata=self.metadata,
retrieve_by_doc=self.retrieve_by_doc,
)
examples, features = self.processor_predict.fit_transform(
X=squad_examples)
prediction = self.reader.predict(
X=(examples, features),
n_predictions=n_predictions,
retriever_score_weight=retriever_score_weight,
return_all_preds=return_all_preds,
)
return prediction
def get_bert_predictions(self, query, retriever_result):
metadata = self.cdqa_pipeline._expand_paragraphs(
to_df(self.prediction_data))
indexed_top_scores = self.get_indexed_top_scores(retriever_result)
squad_examples = generate_squad_examples(
question=query,
best_idx_scores=indexed_top_scores,
metadata=metadata,
retrieve_by_doc=False)
examples, features = self.cdqa_pipeline.processor_predict.fit_transform(
X=squad_examples)
return self.cdqa_pipeline.reader.predict(
X=(examples, features),
n_predictions=None,
retriever_score_weight=self.retriever_score_weight,
return_all_preds=True)