title: PyTorch LSTM - using word embeddings instead of nn.Embedding()
body: <p>Is the nn.Embedding() essential for learning for an LSTM?</p>
  
  <p>I am using an LSTM in PyTorch to predict NER - example of a similar task is here - <a href="https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html" rel="nofollow noreferrer">https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html</a></p>
  
  <p>Code wise, I am using code almost identical to the code in the tutorial above.</p>
  
  <p>The only detail is -
  I am using word2Vec instead of nn.Embedding().</p>
  
  <p>So I remove the nn.Embedding() layer and provide the forward function the features from the word2Vec directly. The RNN does not learn.</p>
  
  <p>Hence, Is the nn.Embedding() essential for learning for an LSTM?</p>