def random_word(tokens, tokenizer):
    """
    Masking some random tokens for Language Model task with probabilities as in the original BERT paper.
    :param tokens: list of str, tokenized sentence.
    :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
    :return: (list of str, list of int), masked tokens and related labels for LM prediction
    """
    output_label = []

    for i, token in enumerate(tokens):
        prob = random.random()
        # mask token with 15% probability
        if prob < 0.15:
            prob /= 0.15

            # 80% randomly change token to mask token
            if prob < 0.8:
                tokens[i] = "[MASK]"

            # 10% randomly change token to random token
            elif prob < 0.9:
                tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]

            # -> rest 10% randomly keep current token

            # append current token to output (we will predict these later)
            try:
                output_label.append(tokenizer.vocab[token])
            except KeyError:
                # For unknown words (should not occur with BPE vocab)
                output_label.append(tokenizer.vocab["[UNK]"])
                logger.warning("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token))
        else:
            # no masking token (will be ignored by loss function later)
            output_label.append(-1)

    return tokens, output_label