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Last active Jan 27, 2020
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#No. of trianable parameters
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters')
#Initialize the pretrained embedding
pretrained_embeddings = TEXT.vocab.vectors
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