text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.nn import init
  
  """
      u_embedding: Embedding for center word.
      v_embedding: Embedding for neighbor words.
  """
  
  
  class SkipGramModel(nn.Module):
  
      def __init__(self, emb_size, emb_dimension):
          super(SkipGramModel, self).__init__()
          self.emb_size = emb_size
          self.emb_dimension = emb_dimension
          self.u_embeddings = nn.Embedding(emb_size, emb_dimension, sparse=True)
          self.v_embeddings1 = nn.Embedding(emb_size, emb_dimension, sparse=True)
          self.v_embeddings2 = nn.Embedding(emb_size, emb_dimension, sparse=True)
  
          initrange = 1.0 / self.emb_dimension
          init.uniform_(self.u_embeddings.weight.data, -initrange, initrange)
          init.constant_(self.v_embeddings1.weight.data, 0)
          init.constant_(self.v_embeddings2.weight.data, 0)
  
      def forward(self, pos_u, pos_v, neg_v, task):
          emb_u = self.u_embeddings(pos_u)
          emb_v = self.v_embeddings1(pos_v) if task == 1 else self.v_embeddings2(pos_v)
          emb_neg_v = self.v_embeddings1(neg_v) if task == 1 else self.v_embeddings2(neg_v)
  
          score = torch.sum(torch.mul(emb_u, emb_v), dim=1)
          score = torch.clamp(score, max=10, min=-10)
          score = -F.logsigmoid(score)
  
          neg_score = torch.bmm(emb_neg_v, emb_u.unsqueeze(2)).squeeze()
          neg_score = torch.clamp(neg_score, max=10, min=-10)
          neg_score = -torch.sum(F.logsigmoid(-neg_score), dim=1)
  
          return torch.mean(score + neg_score)
  
      def save_embedding(self, id2word, file_name):
          embedding = self.u_embeddings.weight.cpu().data.numpy()
          with open(file_name, 'w') as f:
              f.write('%d %d\n' % (len(id2word), self.emb_dimension))
              for wid, w in id2word.items():
                  e = ' '.join(map(lambda x: str(x), embedding[wid]))
                  f.write('%s %s\n' % (w, e))
  