def predict(self, Xs, context=None, **kwargs): """ Produces list of most likely class labels as determined by the fine-tuned model. :param \*Xs: lists of text inputs, shape [batch, n_fields] :returns: list of class labels. """ return BaseModel.predict(self, Xs, context=context, **kwargs)
def predict(self, pairs): """ Produces a list of most likely class labels as determined by the fine-tuned model. :param pairs: Array of text, shape [batch, 2] :returns: list of class labels. """ return BaseModel.predict(self, pairs)
def predict(self, Xs, max_length=None): """ Produces list of most likely class labels as determined by the fine-tuned model. :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 class labels. """ return BaseModel.predict(self, Xs, max_length=max_length)
def predict(self, questions, answers): """ Produces a list of most likely class labels as determined by the fine-tuned model. :param question: List or array of text, shape [batch] :param answers: List or array of text, shape [batch, n_answers] :returns: list of class labels. """ raw_ids = BaseModel.predict(self, list(zip(questions, answers))) return [ans[i] for ans, i in zip(answers, raw_ids)]
def predict(self, pairs, max_length=None): """ Produces a list of most likely class labels as determined by the fine-tuned model. :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 class labels. """ return BaseModel.predict(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(self, X1, X2, max_length=max_length)
def predict(self, questions, answers, max_length=None): """ Produces a list of most likely class labels as determined by the fine-tuned model. :param question: List or array of text, shape [batch] :param answers: List or array of text, shape [batch, n_answers] :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 class labels. """ raw_ids = BaseModel.predict(self, list(zip(questions, answers)), max_length=max_length) return [ans[i] for ans, i in zip(zip(*answers), raw_ids)]