How to find the accuracy of module that uses pytorch Dataloader and chatDataset

Question

I am trying to make a virtual assistant using NLP, for that, I have made neural_network using ReLU with 3 layers on it, I want to find the accuracy of the module that uses NLP, PyTorch, and Data Loader. I don't have any idea how to find the accuracy. I am mentioning the whole training module below.

import numpy as np
import json
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from neural_network import bag_of_words, tokenize, stem
from brain import NeuralNet

with open('intents.json', 'r') as f:
    intents = json.load(f)

all_words = []
tags = []
xy = []

for intent in intents['intents']:
    tag = intent['tag']
    tags.append(tag)

    for pattern in intent['patterns']:
        w = tokenize(pattern)
        all_words.extend(w)
        xy.append((w, tag))

ignore_words = [",", "?", "/", ".", "!","'","-"]
all_words = [stem(w) for w in all_words if w not in ignore_words]
all_words = sorted(set(all_words))
tags = sorted(set(tags))

x_train = []
y_train = []

for (pattern_sentence, tag) in xy:
    bag = bag_of_words(pattern_sentence, all_words)
    x_train.append(bag)

    label = tags.index(tag)
    y_train.append(label)

x_train = np.array(x_train)
y_train = np.array(y_train)

num_epochs = 400
batch_size = 8
learning_rate = 0.001
input_size = len(x_train[0])
hidden_size = 8
output_size = len(tags)

print("Training the model...")


class chatDataset(Dataset):
    def __init__(self):
        self.n_samples = len(x_train)
        self.x_data = x_train
        self.y_data = y_train

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.n_samples


dataset = chatDataset()

train_loader = DataLoader(
    dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=0)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet(input_size, hidden_size, output_size).to(device=device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    for (words, labels) in train_loader:
        words = words.to(device)
        labels = labels.to(dtype=torch.long).to(device)
        outputs = model(words)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    if (epoch+1) % 100 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

print(f'Final loss: {loss.item():.4f}')

with Final loss, I need to print the accuracy of each epoch and If you have any suggestions about increasing the accuracy please give them!