The seq2seq (sequence to sequence) model is a type of encoder-decoder deep learning model commonly employed in natural language processing that uses recurrent neural networks like LSTM to generate output. seq2seq can generate output token by token or character by character. In machine translation, seq2seq networks have an encoder accepting language as input and outputting state vectors and a decoder accepting the encoder’s final state and outputting possible translations.
In natural language processing, one-hot vectors are a way to represent a given word in a set of words wherein a 1 indicates the current word and 0s indicate every other word.
# a one-hot vector of the word "squid"# in the sentence "The squid jumped out of the suitcase."[0, 1, 0, 0, 0, 0, 0]
For text generation, the neural seq2seq model needs to keep track of the current word being processed by its encoder or decoder. It does so with timesteps; each one indicates what token in a given document (sentence) the model is currently processing.
seq2seq machine translation often employs a technique known as teacher forcing during training in which an input token from the previous timestep helps train the model for the current timestep’s target token.
Deep learning algorithms can be implemented in Python using the
TensorFlow library, which is commonly used for machine learning applications such as neural networks. These can be created using
TensorFlow with the
To import the library:
from tensorflow import keras
model modules of
Keras are used when implementing a deep learning model:
from keras.layers import Input, LSTM, Densefrom keras.models import Model
It is possible to improve seq2seq results by adjusting the model’s quantity of training data, the dimensionality of hidden layers, the number of training epochs, and the training batch size.