def finetune(self, Xs, Y=None, batch_size=None): """ :param \*Xs: lists of text inputs, shape [batch, n_fields] :param Y: floating point targets :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number corresponds to the number of training examples provided to each GPU. """ return BaseModel.finetune(self, Xs, Y=Y, batch_size=batch_size)
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_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 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 finetune(self, Xs, Y=None, batch_size=None): """ :param \*Xs: lists of text inputs, shape [batch, n_fields] :param Y: integer or string-valued class labels. It is necessary for the items of Y to be sortable. :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number corresponds to the number of training examples provided to each GPU. """ return BaseModel.finetune(self, Xs, Y=Y, batch_size=batch_size)
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, 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 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_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(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 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)
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(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, 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(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_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)
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)]