How does loss over sigmoid works in skip gram model?

Here is the code I have been referring to
skip gram source code

class skipgram(nn.Module):
  def __init__(self, vocab_size, embedding_dim):
    super(skipgram, self).__init__()
    self.u_embeddings = nn.Embedding(vocab_size, embedding_dim, sparse=True)   
    self.v_embeddings = nn.Embedding(vocab_size, embedding_dim, sparse=True) 
    self.embedding_dim = embedding_dim
  def init_emb(self):
    initrange = 0.5 / self.embedding_dim, initrange), 0)
  def forward(self, u_pos, v_pos, v_neg, batch_size):

    embed_u = self.u_embeddings(u_pos)
    embed_v = self.v_embeddings(v_pos)

    score  = torch.mul(embed_u, embed_v)
    score = torch.sum(score, dim=1)
    log_target = F.logsigmoid(score).squeeze()
    neg_embed_v = self.v_embeddings(v_neg)
    neg_score = torch.bmm(neg_embed_v, embed_u.unsqueeze(2)).squeeze()
    neg_score = torch.sum(neg_score, dim=1)
    sum_log_sampled = F.logsigmoid(-1*neg_score).squeeze()

    loss = log_target + sum_log_sampled

    return -1*loss.sum()/batch_size

As we can see the author of the code has directly taken the sigmoid of the scores along with negative sigmoid adds them takes the mean and sends if off…
I could have understood if we would have a cross-entropy or NLLloss over here but there is nothing as such…

does anyone have an intuitive explanation on why this works (because it does)
thank you