Fit the model for a fixed number of epochs.

        Args:
            x_train: list of training data.
            y_train: list of training target (label) data.
            x_valid: list of validation data.
            y_valid: list of validation target (label) data.
            batch_size: Integer.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch). `shuffle` will default to True.

def fit(self, x_train, y_train, x_valid=None, y_valid=None,
            epochs=1, batch_size=32, verbose=1, callbacks=None, shuffle=True):
        """Fit the model for a fixed number of epochs.

        Args:
            x_train: list of training data.
            y_train: list of training target (label) data.
            x_valid: list of validation data.
            y_valid: list of validation target (label) data.
            batch_size: Integer.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch). `shuffle` will default to True.
        """
        p = IndexTransformer(initial_vocab=self.initial_vocab, use_char=self.use_char)
        p.fit(x_train, y_train)
        embeddings = filter_embeddings(self.embeddings, p._word_vocab.vocab, self.word_embedding_dim)

        model = BiLSTMCRF(char_vocab_size=p.char_vocab_size,
                          word_vocab_size=p.word_vocab_size,
                          num_labels=p.label_size,
                          word_embedding_dim=self.word_embedding_dim,
                          char_embedding_dim=self.char_embedding_dim,
                          word_lstm_size=self.word_lstm_size,
                          char_lstm_size=self.char_lstm_size,
                          fc_dim=self.fc_dim,
                          dropout=self.dropout,
                          embeddings=embeddings,
                          use_char=self.use_char,
                          use_crf=self.use_crf)
        model, loss = model.build()
        model.compile(loss=loss, optimizer=self.optimizer)

        trainer = Trainer(model, preprocessor=p)
        trainer.train(x_train, y_train, x_valid, y_valid,
                      epochs=epochs, batch_size=batch_size,
                      verbose=verbose, callbacks=callbacks,
                      shuffle=shuffle)

        self.p = p
        self.model = model