DSSM实战中文文本匹配任务_dssm 处理中文

引言

本文我们通过DSSM模型来完成中文文本匹配任务，其中包含了文本匹配任务的一般套路，后续只需要修改实现的模型。

数据准备

数据准备包括

• 构建词表(Vocabulary)
• 构建数据集(Dataset)

本次用的是LCQMC通用领域问题匹配数据集，它已经分好了训练、验证和测试集。

我们通过pandas来加载一下。

import pandas as pd

train_df = pd.read_csv(data_path.format("train"), sep="\t", header=None, names=["sentence1", "sentence2", "label"])

train_df.head()
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数据是长这样子的，有两个待匹配的句子，标签是它们是否相似。

下面用jieba来处理每个句子。

def tokenize(sentence):
    return list(jieba.cut(sentence))

train_df.sentence1 = train_df.sentence1.apply(tokenize)
train_df.sentence2 = train_df.sentence2.apply(tokenize)
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得到分好词的数据后，我们就可以得到整个训练语料库中的所有token：

train_sentences = train_df.sentence1.to_list() + train_df.sentence2.to_list()
train_sentences[0]
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['喜欢', '打篮球', '的', '男生', '喜欢', '什么样', '的', '女生']
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现在就可以来构建词表了，我们定义一个类：

UNK_TOKEN = "<UNK>"
PAD_TOKEN = "<PAD>"


class Vocabulary:
    """Class to process text and extract vocabulary for mapping"""

    def __init__(self, token_to_idx: dict = None, tokens: list[str] = None) -> None:
        """
        Args:
            token_to_idx (dict, optional): a pre-existing map of tokens to indices. Defaults to None.
            tokens (list[str], optional): a list of unique tokens with no duplicates. Defaults to None.
        """

        assert any(
            [tokens, token_to_idx]
        ), "At least one of these parameters should be set as not None."
        if token_to_idx:
            self._token_to_idx = token_to_idx
        else:
            self._token_to_idx = {}
            if PAD_TOKEN not in tokens:
                tokens = [PAD_TOKEN] + tokens

            for idx, token in enumerate(tokens):
                self._token_to_idx[token] = idx

        self._idx_to_token = {idx: token for token, idx in self._token_to_idx.items()}

        self.unk_index = self._token_to_idx[UNK_TOKEN]
        self.pad_index = self._token_to_idx[PAD_TOKEN]

    @classmethod
    def build(
        cls,
        sentences: list[list[str]],
        min_freq: int = 2,
        reserved_tokens: list[str] = None,
    ) -> "Vocabulary":
        """Construct the Vocabulary from sentences

        Args:
            sentences (list[list[str]]): a list of tokenized sequences
            min_freq (int, optional): the minimum word frequency to be saved. Defaults to 2.
            reserved_tokens (list[str], optional): the reserved tokens to add into the Vocabulary. Defaults to None.

        Returns:
            Vocabulary: a Vocubulary instane
        """

        token_freqs = defaultdict(int)
        for sentence in tqdm(sentences):
            for token in sentence:
                token_freqs[token] += 1

        unique_tokens = (reserved_tokens if reserved_tokens else []) + [UNK_TOKEN]
        unique_tokens += [
            token
            for token, freq in token_freqs.items()
            if freq >= min_freq and token != UNK_TOKEN
        ]
        return cls(tokens=unique_tokens)

    def __len__(self) -> int:
        return len(self._idx_to_token)

    def __getitem__(self, tokens: list[str] | str) -> list[int] | int:
        """Retrieve the indices associated with the tokens or the index with the single token

        Args:
            tokens (list[str] | str): a list of tokens or single token

        Returns:
            list[int] | int: the indices or the single index
        """
        if not isinstance(tokens, (list, tuple)):
            return self._token_to_idx.get(tokens, self.unk_index)
        return [self.__getitem__(token) for token in tokens]

    def lookup_token(self, indices: list[int] | int) -> list[str] | str:
        """Retrive the tokens associated with the indices or the token with the single index

        Args:
            indices (list[int] | int): a list of index or single index

        Returns:
            list[str] | str: the corresponding tokens (or token)
        """

        if not isinstance(indices, (list, tuple)):
            return self._idx_to_token[indices]

        return [self._idx_to_token[index] for index in indices]

    def to_serializable(self) -> dict:
        """Returns a dictionary that can be serialized"""
        return {"token_to_idx": self._token_to_idx}

    @classmethod
    def from_serializable(cls, contents: dict) -> "Vocabulary":
        """Instantiates the Vocabulary from a serialized dictionary


        Args:
            contents (dict): a dictionary generated by `to_serializable`

        Returns:
            Vocabulary: the Vocabulary instance
        """
        return cls(**contents)

    def __repr__(self):
        return f"<Vocabulary(size={len(self)})>"

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可以通过build方法传入所有分好词的语句，同时传入min_freq指定保存最少出现次数的单词。

这里实现了__getitem__来获取token对应的索引，如果传入的是单个token就返回单个索引，如果传入的是token列表，就返回索引列表。类似地，通过lookup_token来根据所以查找对应的token。

vocab = Vocabulary.build(train_sentences)
vocab
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100%|██████████| 477532/477532 [00:00<00:00, 651784.13it/s]
<Vocabulary(size=35925)>
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我们的词表有35925个token。

有了词表之后，我们就可以向量化句子了，这里也通过一个类来实现。

class TMVectorizer:
    """The Vectorizer which vectorizes the Vocabulary"""

    def __init__(self, vocab: Vocabulary, max_len: int) -> None:
        """
        Args:
            vocab (Vocabulary): maps characters to integers
            max_len (int): the max length of the sequence in the dataset
        """
        self.vocab = vocab
        self.max_len = max_len

    def _vectorize(
        self, indices: list[int], vector_length: int = -1, padding_index: int = 0
    ) -> np.ndarray:
        """Vectorize the provided indices

        Args:
            indices (list[int]): a list of integers that represent a sequence
            vector_length (int, optional): an arugment for forcing the length of index vector. Defaults to -1.
            padding_index (int, optional): the padding index to use. Defaults to 0.

        Returns:
            np.ndarray: the vectorized index array
        """

        if vector_length <= 0:
            vector_length = len(indices)

        vector = np.zeros(vector_length, dtype=np.int64)
        if len(indices) > vector_length:
            vector[:] = indices[:vector_length]
        else:
            vector[: len(indices)] = indices
            vector[len(indices) :] = padding_index

        return vector

    def _get_indices(self, sentence: list[str]) -> list[int]:
        """Return the vectorized sentence

        Args:
            sentence (list[str]): list of tokens
        Returns:
            indices (list[int]): list of integers representing the sentence
        """
        return [self.vocab[token] for token in sentence]

    def vectorize(
        self, sentence: list[str], use_dataset_max_length: bool = True
    ) -> np.ndarray:
        """
        Return the vectorized sequence

        Args:
            sentence (list[str]): raw sentence from the dataset
            use_dataset_max_length (bool): whether to use the global max vector length
        Returns:
            the vectorized sequence with padding
        """
        vector_length = -1
        if use_dataset_max_length:
            vector_length = self.max_len

        indices = self._get_indices(sentence)
        vector = self._vectorize(
            indices, vector_length=vector_length, padding_index=self.vocab.pad_index
        )

        return vector

    @classmethod
    def from_serializable(cls, contents: dict) -> "TMVectorizer":
        """Instantiates the TMVectorizer from a serialized dictionary

        Args:
            contents (dict): a dictionary generated by `to_serializable`

        Returns:
            TMVectorizer:
        """
        vocab = Vocabulary.from_serializable(contents["vocab"])
        max_len = contents["max_len"]
        return cls(vocab=vocab, max_len=max_len)

    def to_serializable(self) -> dict:
        """Returns a dictionary that can be serialized

        Returns:
            dict: a dict contains Vocabulary instance and max_len attribute
        """
        return {"vocab": self.vocab.to_serializable(), "max_len": self.max_len}

    def save_vectorizer(self, filepath: str) -> None:
        """Dump this TMVectorizer instance to file

        Args:
            filepath (str): the path to store the file
        """
        with open(filepath, "w") as f:
            json.dump(self.to_serializable(), f)

    @classmethod
    def load_vectorizer(cls, filepath: str) -> "TMVectorizer":
        """Load TMVectorizer from a file

        Args:
            filepath (str): the path stored the file

        Returns:
            TMVectorizer:
        """
        with open(filepath) as f:
            return TMVectorizer.from_serializable(json.load(f))
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命名为TMVectorizer表示是用于文本匹配(Text Matching)的专门类，调用vectorize方法一次传入一个分好词的句子就可以得到向量化的表示，支持填充Padding。

同时还支持保存功能，主要是用于保存相关的词表以及TMVectorizer所需的max_len字段。

在本小节的最后，通过继承Dataset来构建专门的数据集。

class TMDataset(Dataset):
    """Dataset for text matching"""

    def __init__(self, text_df: pd.DataFrame, vectorizer: TMVectorizer) -> None:
        """

        Args:
            text_df (pd.DataFrame): a DataFrame which contains the processed data examples
            vectorizer (TMVectorizer): a TMVectorizer instance
        """

        self.text_df = text_df
        self._vectorizer = vectorizer

    def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray, int]:
        row = self.text_df.iloc[index]
        return (
            self._vectorizer.vectorize(row.sentence1),
            self._vectorizer.vectorize(row.sentence2),
            row.label,
        )

    def get_vectorizer(self) -> TMVectorizer:
        return self._vectorizer

    def __len__(self) -> int:
        return len(self.text_df)

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构建函数所需的参数只有两个，分别是处理好的DataFrame和TMVectorizer实例。

实现__getitem__方法，因为这个方法会被DataLoader调用，在该方法中对语句进行向量化。

max_len = 50
vectorizer = TMVectorizer(vocab, max_len)

train_dataset = TMDataset(train_df, vectorizer)

batch_size = 128
train_data_loader = DataLoader(train_dataset, batch_size=batch_size,shuffle=True)

for setence1, setence12, label in train_data_loader:
    print(setence1)
    print(setence12)
    print(label)
    break
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构造模型

import torch.nn as nn
import torch


class DSSM(nn.Module):
    """The DSSM model implemention."""

    def __init__(
        self,
        vocab_size: int,
        embedding_size: int,
        activation: str = "relu",
        internal_hidden_sizes: list[int] = [256, 128, 64],
        dropout: float = 0.1,
    ):
        """

        Args:
            vocab_size (int): the size of the Vocabulary
            embedding_size (int): the size of each embedding vector
            activation (str, optional): the activate function. Defaults to "relu".
            internal_hidden_sizes (list[int], optional): the hidden size of inernal Linear Layer. Defaults to [256, 128, 64].
            dropout (float, optional): dropout ratio. Defaults to 0.1.
        """
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_size)

        assert activation.lower() in [
            "relu",
            "tanh",
        ], "activation only supports relu or tanh"

        if activation.lower() == "relu":
            activate_func = nn.ReLU()
        else:
            activate_func = nn.Tanh()

        self.dnn = nn.Sequential(
            nn.Dropout(dropout),
            nn.Linear(embedding_size, internal_hidden_sizes[0]),
            activate_func,
            nn.Dropout(dropout),
            nn.Linear(internal_hidden_sizes[0], internal_hidden_sizes[1]),
            activate_func,
            nn.Dropout(dropout),
            nn.Linear(internal_hidden_sizes[1], internal_hidden_sizes[2]),
            activate_func,
            nn.Dropout(dropout),
        )

        self._init_weights()

    def forward(self, sentence1: torch.Tensor, sentence2: torch.Tensor) -> torch.Tensor:
        """Using the same network to compute the representations of two sentences

        Args:
            sentence1 (torch.Tensor): shape (batch_size, seq_len)
            sentence2 (torch.Tensor): shape (batch_size, seq_len)

        Returns:
            torch.Tensor: the cosine similarity between sentence1 and sentence2
        """
        # shape (batch_size, seq_len) ->  (batch_size, seq_len, embedding_size) -> (batch_size, embedding_size)
        embed_1 = self.embedding(sentence1).sum(1)
        embed_2 = self.embedding(sentence2).sum(1)
        # (batch_size, embedding_size) -> (batch_size, internal_hidden_sizes[2])
        vector_1 = self.dnn(embed_1)
        vector_2 = self.dnn(embed_2)
        # (batch_size, internal_hidden_sizes[2]) -> (batch_size, )
        return torch.cosine_similarity(vector_1, vector_2, dim=1, eps=1e-8)

    def _init_weights(self) -> None:
        for layer in self.modules():
            if isinstance(layer, nn.Linear):
                nn.init.xavier_uniform_(layer.weight)

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模型实现并不完全符合DSSM论文中的设定，比如没有采用词哈希，感觉用Embedding效果应该差不多，可能还更好。同时激活函数支持ReLU和Tanh，经过实验发现这里用ReLU的效果更好。

内部设计和DSSM论文中差不多，除了每层之后增加了Dropout，对于每层的大小和词嵌入维度也进行了一些修改。

在forward方法中首先计算整个序列的词嵌入，然后在序列长度上求和，相当于没有考虑序列中token的顺序。然后让两个语句经过同一个模型得到不同的表示，最后简单地计算它们的余弦相似度。

训练模型

定义指标：

def metrics(y: torch.Tensor, y_pred: torch.Tensor) -> Tuple[float, float, float, float]:
    TP = ((y_pred == 1) & (y == 1)).sum().float()  # True Positive
    TN = ((y_pred == 0) & (y == 0)).sum().float()  # True Negative
    FN = ((y_pred == 0) & (y == 1)).sum().float()  # False Negatvie
    FP = ((y_pred == 1) & (y == 0)).sum().float()  # False Positive
    p = TP / (TP + FP).clamp(min=1e-8)  # Precision
    r = TP / (TP + FN).clamp(min=1e-8)  # Recall
    F1 = 2 * r * p / (r + p).clamp(min=1e-8)  # F1 score
    acc = (TP + TN) / (TP + TN + FP + FN).clamp(min=1e-8)  # Accurary
    return acc, p, r, F1
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定义参数：

args = Namespace(
        dataset_csv="text_matching/data/lcqmc/{}.txt",
        vectorizer_file="vectorizer.json",
        model_state_file="model.pth",
        save_dir=f"text_matching/dssm/model_storage",
        reload_model=False,
        cuda=True,
        learning_rate=5e-4,
        batch_size=128,
        num_epochs=10,
        max_len=50,
        embedding_dim=512,
        activation="relu",
        dropout=0.1,
        internal_hidden_sizes=[256, 256, 128],
        min_freq=2,
        print_every=500,
        verbose=True,
    )
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把参数统一管理，也可以保存到json中以便后续使用。

然后创建数据集

 if args.cuda:
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    else:
        device = torch.device("cpu")

print(f"Using device: {device}.")

vectorizer_path = os.path.join(args.save_dir, args.vectorizer_file)

train_df = build_dataframe_from_csv(args.dataset_csv.format("train"))
test_df = build_dataframe_from_csv(args.dataset_csv.format("test"))
dev_df = build_dataframe_from_csv(args.dataset_csv.format("dev"))

if os.path.exists(vectorizer_path):
    print("Loading vectorizer file.")
    vectorizer = TMVectorizer.load_vectorizer(vectorizer_path)
else:
    print("Creating a new Vectorizer.")

    train_sentences = train_df.sentence1.to_list() + train_df.sentence2.to_list()

    vocab = Vocabulary.build(train_sentences, args.min_freq)

    print(f"Builds vocabulary : {vocab}")

    vectorizer = TMVectorizer(vocab, args.max_len)

    vectorizer.save_vectorizer(vectorizer_path)

train_dataset = TMDataset(train_df, vectorizer)
test_dataset = TMDataset(test_df, vectorizer)
dev_dataset = TMDataset(dev_df, vectorizer)

  
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定义模型：

model = DSSM(
    vocab_size=len(vectorizer.vocab),
    embedding_size=args.embedding_dim,
    activation=args.activation,
    internal_hidden_sizes=args.internal_hidden_sizes,
    dropout=args.dropout,
)

print(f"Model: {model}")

model_saved_path = os.path.join(args.save_dir, args.model_state_file)
if args.reload_model and os.path.exists(model_saved_path):
    model.load_state_dict(torch.load(args.model_saved_path))
    print("Reloaded model")
else:
    print("New model")

model = model.to(device)

model_save_path = os.path.join(
    args.save_dir, f"{datetime.now().strftime('%Y%m%d%H%M%S')}-model.pth"
)
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加载数据集、定义优化器、损失函数：

train_data_loader = DataLoader(
    train_dataset, batch_size=args.batch_size, shuffle=True
)
dev_data_loader = DataLoader(dev_dataset)
test_data_loader = DataLoader(test_dataset)

optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
criterion = nn.CrossEntropyLoss()
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相似标签为1或0，这里当成二分类任务，因为模型的输出是余弦相似度，余弦相似度取值为-1到1，但一般0.4左右的值就可以认为是相似度不大了。所以采用CrossEntropyLoss而不是BCELoss；前者期望有2个输出特征，后者期望一个输出特征，通过是否大于0来判断类别，一般需要是sigmoid的输出。

定义评估函数和训练函数，如上所述，这里通过torch.stack构造了两个输出特征。

def evaluate(data_iter: DataLoader, model: DSSM) -> Tuple[float, float, float, float]:
    y_list, y_pred_list = [], []
    model.eval()
    for x1, x2, y in tqdm(data_iter):
        x1 = x1.to(device).long()
        x2 = x2.to(device).long()
        y = torch.LongTensor(y).to(device)

        similarity = model(x1, x2)
        disparity = 1 - similarity

        output = torch.stack([disparity, similarity], 1).to(device)

        pred = torch.max(output, 1)[1]

        y_pred_list.append(pred)
        y_list.append(y)

    y_pred = torch.cat(y_pred_list, 0)
    y = torch.cat(y_list, 0)
    acc, p, r, f1 = metrics(y, y_pred)
    return acc, p, r, f1


def train(
    data_iter: DataLoader,
    model: DSSM,
    criterion: nn.CrossEntropyLoss,
    optimizer: torch.optim.Optimizer,
    print_every: int = 500,
    verbose=True,
) -> None:
    model.train()

    for step, (x1, x2, y) in enumerate(tqdm(data_iter)):
        x1 = x1.to(device).long()
        x2 = x2.to(device).long()
        y = torch.LongTensor(y).to(device)
        # the similarity between x1 and x2
        similarity = model(x1, x2)
        # the disparity between x1 and x2
        disparity = 1 - similarity
        # CrossEntropyLoss requires two class result
        output = torch.stack([disparity, similarity], 1).to(device)

        # output (batch_size, num_classes=2)
        # y (batch_size, )
        loss = criterion(output, y)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if verbose and (step + 1) % print_every == 0:
            # `torch.max` Returns a namedtuple (values, indices) where values is the maximum value of each row of the input tensor in the given dimension dim.
            # And indices is the index location of each maximum value found (argmax).
            # get the indices
            pred = torch.max(output, 1)[1]
            acc, p, r, f1 = metrics(y, pred)

            print(
                f" TRAIN iter={step+1} loss={loss.item():.6f} accuracy={acc:.3f} precision={p:.3f} recal={r:.3f} f1 score={f1:.4f}"
            )

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同时用torch.max返回的最大值对应的索引来表示预测的类别，0表示负类，1表示正类，所以torch.stack([disparity, similarity], 1)必须把表示不相似的disparity放在0的位置，不要搞错了。

开始训练：

best_f1 = 0.0

for epoch in range(args.num_epochs):
    train(
        train_data_loader,
        model,
        criterion,
        optimizer,
        print_every=args.print_every,
        verbose=args.verbose,
    )
    print("Begin evalute on dev set.")
    with torch.no_grad():
        acc, p, r, f1 = evaluate(dev_data_loader, model)
        if best_f1 < f1:
            best_f1 = f1
            torch.save(model.state_dict(), model_save_path)

        print(
            f"EVALUATE [{epoch+1}/{args.num_epochs}]  accuracy={acc:.3f} precision={p:.3f} recal={r:.3f} f1 score={f1:.4f} best f1: {best_f1:.4f}"
        )



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每训练完一个轮次后在验证集上进行验证，确保验证集效果是逐步变好的。

最后在测试集上验证，这里分别用训练完的模型和f1 score最好的模型在测试集上测试：

model.eval()

acc, p, r, f1 = evaluate(test_data_loader, model)
print(f"TEST accuracy={acc:.3f} precision={p:.3f} recal={r:.3f} f1 score={f1:.4f}")

model.load_state_dict(torch.load(model_save_path))
model.to(device)
acc, p, r, f1 = evaluate(test_data_loader, model)
print(f"TEST accuracy={acc:.3f} precision={p:.3f} recal={r:.3f} f1 score={f1:.4f}")
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...
TRAIN iter=1000 loss=0.532778 accuracy=0.789 precision=0.820 recal=0.869 f1 score=0.8439
 80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 1493/1866 [00:25<00:06, 60.49it/s] 
TRAIN iter=1500 loss=0.533075 accuracy=0.797 precision=0.795 recal=0.880 f1 score=0.8354
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1866/1866 [00:31<00:00, 59.61it/s]
Begin evalute on dev set.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8802/8802 [00:11<00:00, 755.99it/s]
EVALUATE [10/10]  accuracy=0.647 precision=0.639 recal=0.676 f1 score=0.6570 best f1: 0.6668
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12500/12500 [00:18<00:00, 662.33it/s]
TEST accuracy=0.715 precision=0.660 recal=0.887 f1 score=0.7571
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12500/12500 [00:19<00:00, 654.56it/s]
TEST accuracy=0.500 precision=0.500 recal=1.000 f1 score=0.6667
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从结果可以看到，最后在测试集上的准确率为71.7%，效果还行，因为模型比较简单。后续我们尝试不同的模型来优化这个效果。

同时可以看到验证集上f1 score最好的模型效果反而不比最后训练好的模型。

完整代码

点此