def read_emb(src_path: str, trg_path: str, pass_header: bool=True, top_tokens: int=50_000):
    """
    Read tokens of word2vec style embeddings to List[str].

    :param src_path str: path to word2vec-style source language embeddings
    :param trg_path str: path to word2vec-style target language embeddings
    :param top_tokens int: restrict read tokens to top 50_000 tokens
    """
    def get_tokens(path):
        word_list = []
        with open(path, 'r') as file:
            # pass header
            if pass_header:
                next(file)
            for i, line in enumerate(file):
                if i < top_tokens:
                    word = line.split(' ')[0]
                    word_list.append(word)
                else:
                    break
        assert len(word_list) == top_tokens
        return word_list
    src_words = get_tokens(src_path)
    trg_words = get_tokens(trg_path)
    return (src_words, trg_words)