LSTM Model For Sentence Autocompletion
We will use a long-short-term memory network (LSTM) for our model. As we know LSTM network has an edge over simple RNNs since they have three extra gates which prevents gradient vanishing problem in the neural networks. Let us see the parameters and methods we are using in this model one by one.
- __init__(self, dataset): This is the initialization method of the class. It sets up the architecture and parameters of the LSTM model. Here are the key components that are being used within this method: self.lstm_size: It specifies the size (number of units) in the LSTM hidden state. self.embedding_dim: It Specifies the dimensionality of the word embeddings. self.num_layers: It specifies the number of layers in the LSTM. Since we don’t want our model to be computationally expensive we will use only 3 layers. self.embedding: It defines an embedding layer that converts input indices to dense word embeddings. self.lstm: This argument defines the LSTM layer with the specified input size, hidden size, number of layers, and dropout rate. self.fc: Here we are using a fully connected later at the end of the network Definesar layer that maps the LSTM output to the vocabulary size to generate logits.
- forward(self, x, prev_state): This method performs the forward pass of the model. It takes an input tensor x and the previous state of the LSTM prev_state as input. Here are the steps within this method: self.embedding(x): The embed argument passes the input tensor x through the embedding layer to get the word embeddings. self.lstm(embed, prev_state): The output state passes the embeddings and previous state through the LSTM layer to get the output and updated state from the network. self.fc(output): Whereas the fully connected layer produces the logits value (the model’s predictions) and state (the updated LSTM state) for each incomplete sentence.
- init_state(self, sequence_length): This method initializes the LSTM state with all zeros. It takes the length of the input sequence as input and returns an initial state tensor with zeros. The state tensor has a shape of (num_layers, sequence_length, lstm_size)
Python3
from torch import nn class LSTMModel(nn.Module): def __init__(self, dataset): super(LSTMModel, self).__init__() self.lstm_size = 128 self.embedding_dim = 128 self.num_layers = 3 n_vocab = len(dataset.unique_words) self.embedding = nn.Embedding( num_embeddings=n_vocab, embedding_dim=self.embedding_dim, ) self.lstm = nn.LSTM( input_size=self.embedding_dim, hidden_size=self.lstm_size, num_layers=self.num_layers, dropout=0.2, ) self.fc = nn.Linear(self.lstm_size, n_vocab) def forward(self, x, prev_state): embed = self.embedding(x) output, state = self.lstm(embed, prev_state) logits = self.fc(output) return logits, state def init_state(self, sequence_length): return ( torch.zeros(self.num_layers, \ sequence_length, self.lstm_size), torch.zeros(self.num_layers, \ sequence_length, self.lstm_size) ) |
Sentence Autocomplete Using Pytorch
Natural Language Processing(NLP) is one of the most flourishing parts of deep learning. Several applications of NLP are being used continuously in daily life. In this article, we are going to see how we can use NLP to autocomplete half-written sentences using deep learning methods. We will also see how we can generate clean data for training our NLP model. We will cover the following steps in this article
- Cleaning the text data for training the NLP model
- Loading the dataset using PyTorch
- Creating the LSTM model
- Training an NLP model
- Making inferences from the trained model
We have seen applications like google keyboard where Google recommends what to type next based on the words which we have already written in the chatbox draft. However, to recommend the next term application like Google has been trained on billions of written sentences. In our model, we will use Wikipedia sentences that are freely available on the internet to download and that we can use for training our model.