Best way to handle OOV words when using pretrained embeddings in PyTorch
I am using word2vec pretrained embedding in PyTorch (following code here). However, it does not seem to handle unseen words. Is there any good way to solve it?

FastText builds character ngram vectors as part of model training. When it finds an OOV word, it sums the character ngram vectors in the word to produce a vector for the word. You can find more detail here. 
