So I was trying to move away from torchtext and create my own custom dataloader, which i think I did. I tested it out with the IMDb dataset for sentiment analysis, but I forgot to add pretrained embeddings. So I have two questions for -
1-  nn.Embeddings basically creates a 2d matrix of random weights, if pretrained is not provided. And starts training them via grad descent, is this right? If so does this mean there are additional vocab_size*emb_dim parameters to train?
2- which brings to my follow up question, does the pretrained embeddings only function is to speed up training (by whatever factor, probably huge tho), I had the following graph of loss vs iterations without pretrained embeddings for 5 epochs. Do you think there might be any other issues in my code except embeddings? 
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