def featurize(self, pairs):
"""
Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
:param pairs: Array of text, shape [batch, 2]
:returns: np.array of features of shape (n_examples, embedding_size).
"""
return BaseModel.featurize(self, pairs)
def featurize(self, Xs, **kwargs):
"""
Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
:param \*Xs: lists of text inputs, shape [batch, n_fields]
:returns: np.array of features of shape (n_examples, embedding_size).
"""
return BaseModel.featurize(self, Xs, **kwargs)
def featurize(self, questions, answers):
"""
Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
:param questions: List or array of text, shape [batch]
:param answers: List or array of text, shape [n_answers, batch]
:returns: np.array of features of shape (n_examples, embedding_size).
"""
return BaseModel.featurize(self, zip(questions, answers))
def featurize(self, pairs, max_length=None):
"""
Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
: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: np.array of features of shape (n_examples, embedding_size).
"""
return BaseModel.featurize(self, pairs, max_length=max_length)
