Explain in simple terms: RNN text classification model practical strategy

Table of Contents

1. Overview of RNN text classification model

2. Specific construction of RNN text classification model

1. Define model classes

2. Model forward propagation

3. Complete code display

3. Summary

1. Overview of RNN text classification model

The RNN text classification model is a model that uses a recurrent neural network (RNN) for text classification. RNN is a type of recurrent neural network capable of processing sequence data such as text. In text classification tasks, RNN models can convert text sequences into continuous vector representations and use these vectors for classification. It can be used for various text classification tasks, such as Sentiment Analysis, Topic Classification, Spam Detection, etc. Itsadvantage is that it can handle variable-length sequences and capture long-term dependencies and contextual information in the sequence. However, itsdisadvantagesare that the training process can be slower and requires proper preprocessing and feature engineering of the data.

2. Specific construction of RNN text classification model

1. Define model class

class Model(nn.Module):
    def __init__(self, embedding_pretrained,n_vocab,embed,num_classes):
        super(Model, self).__init__()
        if embedding_pretrained is not None:# 4761 pad tells the model, 4761
            self.embedding = nn.Embedding.from_pretrained(embedding_pretrained, padding_idx = n_vocab-1, freeze=False) #If you use pre-trained embedding, use this line
        else:
            self.embedding = nn.Embedding(n_vocab, embed, padding_idx = n_vocab - 1)#If new training embedding uses this line
        #padding_idx defaults to None. If specified, the parameter PAD corresponding to padding_idx will not affect the gradient, so the word embedding vector at padding_idx will not be updated during the training process.
        self.lstm = nn.LSTM(embed, 128, 3, bidirectional=True, batch_first=True, dropout=0.3)
        #128 is the number of neurons in each hidden state in each layer, 3 is the number of hidden layers, batch_first=True means that the input and output tensors will be ' (batch, seq, feature) ' instead of ' ( seq, batch, feature) 'Provide.
        self.fc = nn.Linear(128 * 2, num_classes)

Detailed explanation of some parameters:

def __init__(self, embedding_pretrained, n_vocab, embed, num_classes): This is the constructor of the model class, which accepts four parameters: pretrained word embeddings (embedding_pretrained), vocabulary size(n_vocab), word embedding dimension(embed code>), and the number of categories (num_classes).


self.embedding = nn.Embedding.from_pretrained(embedding_pretrained, padding_idx=n_vocab-1, freeze=False): embedding_pretrained is the pretrained word Embedding, use this pretrained word embedding model and set padding_idx to the index of the last word embedding (n_vocab-1) and freeze code> is set to False, which means that the weight of the word embedding is updated during the training process.
self.lstm = nn.LSTM(embed, 128, 3, bidirectional=True, batch_first=True, dropout=0.3): Create an LSTM layer with the number of input features. embed (the dimension of word embedding), the number of neurons in each hidden state is 128, the number of hidden layers is 3, bidirectional is True, batch priority is True, and the dropout rate is 0.3 (randomly turned off number of neurons to prevent overfitting).
self.fc = nn.Linear(128 * 2, num_classes): Create a fully connected layer (linear layer) with the number of input features 128 * 2(The number of output features of the LSTM layer), the number of output features is num_classes(the number of classification categories).


A PyTorch model is defined, which is mainly used for text classification tasks. The structure of the model includes word embedding layer, LSTM (long short-term memory) layer and fully connected layer. During the forward propagation process of the model, the input data first passes through the word embedding layer, then is passed to the LSTM layer, and finally the output is obtained through the fully connected layer. Among them, the word embedding layer converts the input text sequence into a continuous vector representation, the LSTM layer further processes the output of the word embedding layer and captures the long-term dependencies in the sequence, and the final fully connected layer processes the output of the LSTM layer. Classification.
2. Model forward propagation
 def forward(self, x): #([23,34,..,13],79)
        x, _ = x
        out = self.embedding(x) #
        out, _ = self.lstm(out)
        out = self.fc(out[:, -1, :]) # Hidden state at the last moment of the sentence
        return out
Among them, each parameter is explained in detail as follows:

x, _ = x: Separate the sequence length and batch size from the input tensor. Here we only care about the sequence length, so use the underscore "_" to ignore the batch size, which will not be described below. 
out = self.embedding(x): Pass the input tensor x to the word embedding layer and get a shape of (batch , sequence, embed) tensor. Here, the batch size is equal to the sequence length since we are processing only one sequence in each batch.
out, _ = self.lstm(out): Pass the output of the word embedding layer to the LSTM layer, and get a shape of (batch, sequence, 128 * 2) ’s tensor. The output of the LSTM layer is the hidden state at each moment and the last hidden state.

A forward propagation process of a neural network is defined, which first converts the text sequence into a continuous vector representation, and then performs classification through the LSTM layer and the fully connected layer. A neural network including word embedding layer, LSTM layer and fully connected layer is implemented.
3. Complete code display
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
importsys

class Model(nn.Module):
    def __init__(self, embedding_pretrained,n_vocab,embed,num_classes):
        super(Model, self).__init__()
        if embedding_pretrained is not None:# 4761 pad tells the model, 4761
            self.embedding = nn.Embedding.from_pretrained(embedding_pretrained, padding_idx = n_vocab-1, freeze=False) #If you use pre-trained embedding, use this line
        else:
            self.embedding = nn.Embedding(n_vocab, embed, padding_idx = n_vocab - 1)#If new training embedding uses this line
        #padding_idx defaults to None. If specified, the parameter PAD corresponding to padding_idx will not affect the gradient, so the word embedding vector at padding_idx will not be updated during the training process.
        self.lstm = nn.LSTM(embed, 128, 3, bidirectional=True, batch_first=True, dropout=0.3)
        #128 is the number of neurons in each hidden state in each layer, 3 is the number of hidden layers, batch_first=True means that the input and output tensors will be ' (batch, seq, feature) ' instead of ' ( seq, batch, feature) 'Provide.
        self.fc = nn.Linear(128 * 2, num_classes)

    def forward(self, x): #([23,34,..,13],79)
        x, _ = x
        out = self.embedding(x) #
        out, _ = self.lstm(out)
        out = self.fc(out[:, -1, :]) # Hidden state at the last moment of the sentence
        return out
In summary, a neural network model including a word embedding layer, an LSTM layer and a fully connected layer is defined, which can be used in the forward propagation process of text classification tasks, laying the foundation for the overall implementation of subsequent text classification.
3. Summary
The process of building an RNN text classification model includes multiple steps such as data preprocessing, model construction, parameter setting, training, evaluation and application. Through reasonable design and adjustment of these steps, an efficient and accurate RNN text classification model can be built.

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