The continuous-bag-of-words-pytorch from smafjal

continuous-bag-of-words-pytorch's Introduction

continuous-bag-of-words(CBOW)-pytorch

This is one of the implementation of CBOW model in pytorch lib. CBOW is used for learning the word(getting the word probability) by looking at the context. A single window was define for words context learnig. One of the objectives of CBOW model is to find two words similirity. Nowadays CBOW is frequently used in many NLP deep learning task.

RUN

python3 cbow_model_pytorch.py

it will verbose all training loss. test_cbow function used to show the two words similarity after learning the corpus context.

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continuous-bag-of-words-pytorch's Issues

You may have made a mistake in your forward().

Your implementation:

def forward(self, inp):
        out = self.embeddings(inp).view(1, -1)
        out = out.view(1, -1)
        out = self.lin1(out)
        out = F.relu(out)
        out = self.lin2(out)
        out = F.log_softmax(out, dim=1)
        return out

However, you need to sum embeddings as the formula:
$ logp(w_i|C)= logSoftmax(A(\sum_{w∈C}q_w)+b)$

i.e

def forward(self, inp):
        out = torch.sum(self.embeddings(inp), dim=0).view(1, -1)
        .....

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