title: ValueError in lables
body: <p>Error:-</p>
  <p>label = tags.index(tag)<br />
  ValueError: 'greeting' is not in list</p>
  <p>My Code:-</p>
  <pre><code>import numpy as np
  import json
  import torch
  import torch.nn as nn
  from torch.utils.data import Dataset, DataLoader
  from NeuralNetwork import bag_of_words, tokenize, stem
  from brain import NeuralNet
  import nltk
  nltk.download('punkt')
  
  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']:
          print(pattern)
          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(tag))
  
  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 = 1000
  batch_size = 8
  learning_rate = 0.001
  input_size = len(x_train[0])
  hidden_size = 8
  output_size = len(tags)
  
  print(&quot;Training the Model...&quot;)
  
  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}')
  
  data = {
  &quot;model_state&quot;:model.state_dict(),
  &quot;input_size&quot;:input_size,
  &quot;hidden_size&quot;:hidden_size,
  &quot;output_size&quot;:output_size,
  &quot;all_words&quot;:all_words,
  &quot;tags&quot;:tags
  }
  
  FILE = &quot;TrainData.pth&quot;
  torch.save(data,FILE)
  
  print(f&quot;Training Complete, File Saved to {FILE}&quot;)
  
  </code></pre>
  <p>I have been trying to solve it, but I can not come up with a solution.<br />
  Can you guys please help me out? <br />
  It is having error at line 40</p>
  <p>qeht efgr get ugergeg
  iuh9oiuhjruiwhgreuhgiuqhoeiugrhiuehroiuhqiurhtieruhiurhiuhtiurehyiuhriuhqiuhry</p>