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  1. import torch
  2. import torch.nn as nn
  3. import numpy as np
  4.  
  5. class CBOW(torch.nn.Module):
  6.  
  7.     def __init__(self, inp_size , vocab_size, embedding_dim=100):
  8.         super(CBOW, self).__init__()
  9.         self.embeddings = nn.Embedding(vocab_size, embedding_dim)
  10.         self.linear1 = nn.Linear(embedding_dim, 100)
  11.         self.activation_function1 = nn.ReLU()        
  12.         self.linear2 = nn.Linear(100, vocab_size)
  13.         self.activation_function2 = nn.LogSoftmax(dim = -1)
  14.        
  15.  
  16.     def forward(self, inputs):
  17.         embeds = sum(self.embeddings(torch.from_numpy(inputs).long())).view(1,-1)
  18.         out = self.linear1(embeds)
  19.         out = self.activation_function1(out)
  20.         out = self.linear2(out)
  21.         out = self.activation_function2(out)
  22.         return out
  23.    
  24. model = CBOW(window_size*2,vocab_size)
  25.  
  26. loss_function = nn.NLLLoss()
  27. optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
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