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)