Function build_dataset

utils_fasttext.py:31–87

(config, ues_word)

def build_dataset(config, ues_word):
    if ues_word:
        tokenizer = lambda x: x.split(' ')  # 以空格隔开，word-level
    else:
        tokenizer = lambda x: [y for y in x]  # char-level
    if os.path.exists(config.vocab_path):
        vocab = pkl.load(open(config.vocab_path, 'rb'))
    else:
        vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
        pkl.dump(vocab, open(config.vocab_path, 'wb'))
    print(f"Vocab size: {len(vocab)}")

    def biGramHash(sequence, t, buckets):
        t1 = sequence[t - 1] if t - 1 >= 0 else 0
        return (t1 * 14918087) % buckets

    def triGramHash(sequence, t, buckets):
        t1 = sequence[t - 1] if t - 1 >= 0 else 0
        t2 = sequence[t - 2] if t - 2 >= 0 else 0
        return (t2 * 14918087 * 18408749 + t1 * 14918087) % buckets

    def load_dataset(path, pad_size=32):
        contents = []
        with open(path, 'r', encoding='UTF-8') as f:
            for line in tqdm(f):
                lin = line.strip()
                if not lin:
                    continue
                content, label = lin.split('\t')
                words_line = []
                token = tokenizer(content)
                seq_len = len(token)
                if pad_size:
                    if len(token) < pad_size:
                        token.extend([PAD] * (pad_size - len(token)))
                    else:
                        token = token[:pad_size]
                        seq_len = pad_size
                # word to id
                for word in token:
                    words_line.append(vocab.get(word, vocab.get(UNK)))

                # fasttext ngram
                buckets = config.n_gram_vocab
                bigram = []
                trigram = []
                # ------ngram------
                for i in range(pad_size):
                    bigram.append(biGramHash(words_line, i, buckets))
                    trigram.append(triGramHash(words_line, i, buckets))
                # -----------------
                contents.append((words_line, int(label), seq_len, bigram, trigram))
        return contents  # [([...], 0), ([...], 1), ...]
    train = load_dataset(config.train_path, config.pad_size)
    dev = load_dataset(config.dev_path, config.pad_size)
    test = load_dataset(config.test_path, config.pad_size)
    return vocab, train, dev, test