when training the masked LM, the unmasked words (have label 0) were trained together with masked words?

According to the code

    def random_word(self, sentence):
        tokens = sentence.split()
        output_label = []

        for i, token in enumerate(tokens):
            prob = random.random()
            if prob < 0.15:
                # 80% randomly change token to make token
                if prob < prob * 0.8:
                    tokens[i] = self.vocab.mask_index

                # 10% randomly change token to random token
                elif prob * 0.8 <= prob < prob * 0.9:
                    tokens[i] = random.randrange(len(self.vocab))

                # 10% randomly change token to current token
                elif prob >= prob * 0.9:
                    tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)

                output_label.append(self.vocab.stoi.get(token, self.vocab.unk_index))

            else:
                tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)
                output_label.append(0)

        return tokens, output_label

Do we need to exclude the unmasked words when training the LM?

[codertimo]

@coddinglxf that’s what I thought at first, but can’t implement it efficiently as much as GPU computation time. If you have any idea please implement and pull request plez 😃 It would be really cool to do it 👍

[codertimo]

@leon-cas yes #36 it’s solved with your question