def predict_proba(self, Xs, context=None, **kwargs):
"""
Produces probability distribution over classes for each example in X.
:param \*Xs: lists of text inputs, shape [batch, n_fields]
:returns: list of dictionaries. Each dictionary maps from X2 class label to its assigned class probability.
"""
return BaseModel.predict_proba(self, Xs, context=context, **kwargs)
def predict_proba(self, pairs):
"""
Produces a probability distribution over classes for each example in X.
:param pairs: Array of text, shape [batch, 2]
:returns: list of dictionaries. Each dictionary maps from a class label to its assigned class probability.
"""
return BaseModel.predict_proba(self, pairs)
def predict_proba(self, Xs, max_length=None):
"""
Produces probability distribution over classes for each example in X.
:param \*Xs: lists of text inputs, shape [batch, n_fields]
:param max_length: the number of tokens to be included in the document representation.
Providing more than `max_length` tokens as input will result in truncation.
:returns: list of dictionaries. Each dictionary maps from X2 class label to its assigned class probability.
"""
return BaseModel.predict_proba(self, Xs, max_length=max_length)
def predict_proba(self, pairs, max_length=None):
"""
Produces a probability distribution over classes for each example in X.
:param pairs: Array of text, shape [batch, 2]
:param max_length: the number of byte-pair encoded tokens to be included in the document representation.
Providing more than `max_length` tokens as input will result in truncation.
:returns: list of dictionaries. Each dictionary maps from a class label to its assigned class probability.
"""
return BaseModel.predict_proba(self, pairs, max_length=max_length)
def predict_proba(self, X1, X2, max_length=None):
"""
Produces a probability distribution over classes for each example in X.
:param X1: List or array of text, shape [batch]
:param X2: List or array of text, shape [batch]
:param max_length: the number of byte-pair encoded tokens to be included in the document representation.
Providing more than `max_length` tokens as input will result in truncation.
:returns: list of dictionaries. Each dictionary maps from a class label to its assigned class probability.
"""
return BaseModel.predict_proba(self, X1, X2, max_length=max_length)
