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在RE2文本匹配实战的最后#xff0c;博主说过会结合词向量以及其他技巧来对效果进行调优#xff0c;本篇文章对整个过程进行详细记录。其他文本匹配系列实战后续也会进行类似的调优#xff0c;方法是一样的#xff0c;不再赘述。
本文所用到的词向量可以在Gensim训练…引言
在RE2文本匹配实战的最后博主说过会结合词向量以及其他技巧来对效果进行调优本篇文章对整个过程进行详细记录。其他文本匹配系列实战后续也会进行类似的调优方法是一样的不再赘述。
本文所用到的词向量可以在Gensim训练中文词向量实战文末找到免费提供下载。
完整代码在文末。
数据准备
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 pdtrain_df pd.read_csv(data_path.format(train), sep\t, headerNone, names[sentence1, sentence2, label])train_df.head()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)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][喜欢, 打篮球, 的, 男生, 喜欢, 什么样, 的, 女生]现在就可以来构建词表了我们沿用之前的代码
class Vocabulary:Class to process text and extract vocabulary for mappingdef __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_idxelse:self._token_to_idx {}if PAD_TOKEN not in tokens:tokens [PAD_TOKEN] tokensfor idx, token in enumerate(tokens):self._token_to_idx[token] idxself._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]classmethoddef build(cls,sentences: list[list[str]],min_freq: int 2,reserved_tokens: list[str] None,) - Vocabulary:Construct the Vocabulary from sentencesArgs:sentences (list[list[str]]): a list of tokenized sequencesmin_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 instanetoken_freqs defaultdict(int)for sentence in tqdm(sentences):for token in sentence:token_freqs[token] 1unique_tokens (reserved_tokens if reserved_tokens else []) [UNK_TOKEN]unique_tokens [tokenfor token, freq in token_freqs.items()if freq min_freq and token ! UNK_TOKEN]return cls(tokensunique_tokens)def __len__(self) - int:return len(self._idx_to_token)def __iter__(self):for idx, token in self._idx_to_token.items():yield idx, tokendef __getitem__(self, tokens: list[str] | str) - list[int] | int:Retrieve the indices associated with the tokens or the index with the single tokenArgs:tokens (list[str] | str): a list of tokens or single tokenReturns:list[int] | int: the indices or the single indexif 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 indexArgs:indices (list[int] | int): a list of index or single indexReturns: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 serializedreturn {token_to_idx: self._token_to_idx}classmethoddef from_serializable(cls, contents: dict) - Vocabulary:Instantiates the Vocabulary from a serialized dictionaryArgs:contents (dict): a dictionary generated by to_serializableReturns:Vocabulary: the Vocabulary instancereturn cls(**contents)def __repr__(self):return fVocabulary(size{len(self)})主要修改是增加
def __iter__(self):for idx, token in self._idx_to_token.items():yield idx, token使得这个词表是可迭代的其他代码参考完整代码。
模型实现
模型实现见RE2文本匹配实战没有任何修改。
模型训练
主要优化在模型训练过程中首先我们训练得更久——总epochs数设成50同时我们引入早停策略当模型不再优化则无需继续训练。
早停策略
class EarlyStopper:def __init__(self, patience: int 5, mode: str min) - None:self.patience patienceself.counter 0self.best_value 0.0if mode not in {min, max}:raise ValueError(fmode {mode} is unknown!)self.mode modedef step(self, value: float) - bool:if self.is_better(value):self.best_value valueself.counter 0else:self.counter 1if self.counter self.patience:return Truereturn Falsedef is_better(self, a: float) - bool:if self.mode min:return a self.best_valuereturn a self.best_value很简单如果调用step()返回True则触发了早停通过best_value保存训练过程中的最佳指标同时技术清零其中patience表示最多忍耐模型不再优化次数
学习率调度
当模型不再收敛时还可以尝试减少学习率。这里引入的ReduceLROnPlateau就可以完成这件事。
lr_scheduler ReduceLROnPlateau(optimizer, modemax, factor0.85, patience0)for epoch in range(args.num_epochs):train(train_data_loader, model, criterion, optimizer, args.grad_clipping)acc, p, r, f1 evaluate(dev_data_loader, model)# 当准确率不再下降则降低学习率lr_scheduler.step(acc)增加梯度裁剪值
梯度才才裁剪值增加到10.0。
载入预训练词向量
最重要的就是载入预训练词向量了
def load_embedings(vocab, embedding_path: str, embedding_dim: int 300, lower: bool True
) - list[list[float]]:word2vec KeyedVectors.load_word2vec_format(embedding_path)embedding np.random.randn(len(vocab), embedding_dim)load_count 0for i, word in vocab:if lower:word word.lower()if word in word2vec:embedding[i] word2vec[word]load_count 1print(floaded word count: {load_count})return embedding.tolist()首先加载word2vec文件接着随机初始化一个词表大小的词向量然后遍历(见上文)词表中的标记如果标记出现在word2vec中则使用word2vec的嵌入并且计数加1最后打印出工加载的标记数。
设定随机种子
def set_random_seed(seed: int 666) - None:np.random.seed(seed)torch.manual_seed(seed)torch.cuda.manual_seed_all(seed)为了让结果可复现还实现了设定随机种子 本文用的是 set_random_seed(seed47)最终能达到测试集上84.6%的准确率实验过程中碰到了85.0%的准确率但没有复现。
训练
训练参数
Arguments : Namespace(dataset_csvtext_matching/data/lcqmc/{}.txt, vectorizer_filevectorizer.json, model_state_filemodel.pth, pandas_filedataframe.{}.pkl, save_dir/home/yjw/workspace/nlp-in-action-public/text_matching/re2/model_storage, reload_modelFalse, cudaTrue, learning_rate0.0005, batch_size128, num_epochs50, max_len50, embedding_dim300, hidden_size150, encoder_layers2, num_blocks2, kernel_sizes[3], dropout0.2, min_freq2, project_funclinear, grad_clipping10.0, num_classes2)
主要训练代码
train_data_loader DataLoader(train_dataset, batch_sizeargs.batch_size, shuffleTrue
)
dev_data_loader DataLoader(dev_dataset, batch_sizeargs.batch_size)
test_data_loader DataLoader(test_dataset, batch_sizeargs.batch_size)optimizer torch.optim.AdamW(model.parameters(), lrargs.learning_rate)
criterion nn.CrossEntropyLoss()lr_scheduler ReduceLROnPlateau(optimizer, modemax, factor0.85, patience0)best_value 0.0early_stopper EarlyStopper(modemax)for epoch in range(args.num_epochs):train(train_data_loader, model, criterion, optimizer, args.grad_clipping)acc, p, r, f1 evaluate(dev_data_loader, model)lr_scheduler.step(acc)if acc best_value:best_value accprint(fSave model with best acc :{acc:4f})torch.save(model.state_dict(), model_save_path)if early_stopper.step(acc):print(fStop from early stopping.)breakacc, p, r, f1 evaluate(dev_data_loader, model)print(fEVALUATE [{epoch1}/{args.num_epochs}] accuracy{acc:.3f} precision{p:.3f} recal{r:.3f} f1 score{f1:.4f})model.eval()acc, p, r, f1 evaluate(test_data_loader, model)
print(fTEST 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(fTEST[best score] accuracy{acc:.3f} precision{p:.3f} recal{r:.3f} f1 score{f1:.4f}
)输出
Arguments : Namespace(dataset_csvtext_matching/data/lcqmc/{}.txt, vectorizer_filevectorizer.json, model_state_filemodel.pth, pandas_filedataframe.{}.pkl, save_dir/home/yjw/workspace/nlp-in-action-public/text_matching/re2/model_storage, reload_modelFalse, cudaTrue, learning_rate0.0005, batch_size128, num_epochs50, max_len50, embedding_dim300, hidden_size150, encoder_layers2, num_blocks2, kernel_sizes[3], dropout0.2, min_freq2, project_funclinear, grad_clipping10.0, num_classes2)
Using device: cuda:0.
Loads cached dataframes.
Loads vectorizer file.
set_count: 4789
Model: RE2((embedding): Embedding((embedding): Embedding(4827, 300, padding_idx0)(dropout): Dropout(p0.2, inplaceFalse))(connection): AugmentedResidualConnection()(blocks): ModuleList((0): ModuleDict((encoder): Encoder((encoders): ModuleList((0): Conv1d((model): ModuleList((0): Sequential((0): Conv1d(300, 150, kernel_size(3,), stride(1,), padding(1,))(1): GeLU())))(1): Conv1d((model): ModuleList((0): Sequential((0): Conv1d(150, 150, kernel_size(3,), stride(1,), padding(1,))(1): GeLU()))))(dropout): Dropout(p0.2, inplaceFalse))(alignment): Alignment((projection): Sequential((0): Dropout(p0.2, inplaceFalse)(1): Linear((model): Sequential((0): Linear(in_features450, out_features150, biasTrue)(1): GeLU()))))(fusion): Fusion((dropout): Dropout(p0.2, inplaceFalse)(fusion1): Linear((model): Sequential((0): Linear(in_features900, out_features150, biasTrue)(1): GeLU()))(fusion2): Linear((model): Sequential((0): Linear(in_features900, out_features150, biasTrue)(1): GeLU()))(fusion3): Linear((model): Sequential((0): Linear(in_features900, out_features150, biasTrue)(1): GeLU()))(fusion): Linear((model): Sequential((0): Linear(in_features450, out_features150, biasTrue)(1): GeLU()))))(1): ModuleDict((encoder): Encoder((encoders): ModuleList((0): Conv1d((model): ModuleList((0): Sequential((0): Conv1d(450, 150, kernel_size(3,), stride(1,), padding(1,))(1): GeLU())))(1): Conv1d((model): ModuleList((0): Sequential((0): Conv1d(150, 150, kernel_size(3,), stride(1,), padding(1,))(1): GeLU()))))(dropout): Dropout(p0.2, inplaceFalse))(alignment): Alignment((projection): Sequential((0): Dropout(p0.2, inplaceFalse)(1): Linear((model): Sequential((0): Linear(in_features600, out_features150, biasTrue)(1): GeLU()))))(fusion): Fusion((dropout): Dropout(p0.2, inplaceFalse)(fusion1): Linear((model): Sequential((0): Linear(in_features1200, out_features150, biasTrue)(1): GeLU()))(fusion2): Linear((model): Sequential((0): Linear(in_features1200, out_features150, biasTrue)(1): GeLU()))(fusion3): Linear((model): Sequential((0): Linear(in_features1200, out_features150, biasTrue)(1): GeLU()))(fusion): Linear((model): Sequential((0): Linear(in_features450, out_features150, biasTrue)(1): GeLU())))))(pooling): Pooling()(prediction): Prediction((dense): Sequential((0): Dropout(p0.2, inplaceFalse)(1): Linear((model): Sequential((0): Linear(in_features600, out_features150, biasTrue)(1): GeLU()))(2): Dropout(p0.2, inplaceFalse)(3): Linear((model): Sequential((0): Linear(in_features150, out_features2, biasTrue)(1): GeLU()))))
)
New modelTRAIN iter1866 loss0.436723: 100%|██████████| 1866/1866 [01:5500:00, 16.17it/s]
100%|██████████| 69/69 [00:0100:00, 40.39it/s]
Save model with best accuracy :0.771302
EVALUATE [2/50] accuracy0.771 precision0.800 recal0.723 f1 score0.7598TRAIN iter1866 loss0.403501: 100%|██████████| 1866/1866 [01:5700:00, 15.93it/s]
100%|██████████| 69/69 [00:0100:00, 45.32it/s]
Save model with best accuracy :0.779709
EVALUATE [3/50] accuracy0.780 precision0.785 recal0.770 f1 score0.7777TRAIN iter1866 loss0.392297: 100%|██████████| 1866/1866 [01:4500:00, 17.64it/s]
100%|██████████| 69/69 [00:0100:00, 43.32it/s]
Save model with best accuracy :0.810838
EVALUATE [4/50] accuracy0.811 precision0.804 recal0.822 f1 score0.8130TRAIN iter1866 loss0.383858: 100%|██████████| 1866/1866 [01:4600:00, 17.52it/s]
100%|██████████| 69/69 [00:0100:00, 42.72it/s]
EVALUATE [5/50] accuracy0.810 precision0.807 recal0.816 f1 score0.8113TRAIN iter1866 loss0.374672: 100%|██████████| 1866/1866 [01:4600:00, 17.55it/s]
100%|██████████| 69/69 [00:0100:00, 44.62it/s]
Save model with best accuracy :0.816746
EVALUATE [6/50] accuracy0.817 precision0.818 recal0.815 f1 score0.8164TRAIN iter1866 loss0.369444: 100%|██████████| 1866/1866 [01:4600:00, 17.52it/s]
100%|██████████| 69/69 [00:0100:00, 45.27it/s]
EVALUATE [7/50] accuracy0.815 precision0.800 recal0.842 f1 score0.8203TRAIN iter1866 loss0.361552: 100%|██████████| 1866/1866 [01:4700:00, 17.39it/s]
100%|██████████| 69/69 [00:0100:00, 42.68it/s]
Save model with best accuracy :0.824926
EVALUATE [8/50] accuracy0.825 precision0.820 recal0.832 f1 score0.8262TRAIN iter1866 loss0.358231: 100%|██████████| 1866/1866 [01:5000:00, 16.95it/s]
100%|██████████| 69/69 [00:0100:00, 42.80it/s]
Save model with best accuracy :0.827312
EVALUATE [9/50] accuracy0.827 precision0.841 recal0.808 f1 score0.8239TRAIN iter1866 loss0.354693: 100%|██████████| 1866/1866 [01:5500:00, 16.19it/s]
100%|██████████| 69/69 [00:0100:00, 36.67it/s]
Save model with best accuracy :0.830607
EVALUATE [10/50] accuracy0.831 precision0.818 recal0.851 f1 score0.8340TRAIN iter1866 loss0.351138: 100%|██████████| 1866/1866 [02:0200:00, 15.23it/s]
100%|██████████| 69/69 [00:0200:00, 32.18it/s]
Save model with best accuracy :0.837991
EVALUATE [11/50] accuracy0.838 precision0.840 recal0.836 f1 score0.8376TRAIN iter1866 loss0.348067: 100%|██████████| 1866/1866 [01:5200:00, 16.57it/s]
100%|██████████| 69/69 [00:0100:00, 42.16it/s]
EVALUATE [12/50] accuracy0.836 precision0.836 recal0.837 f1 score0.8365TRAIN iter1866 loss0.343886: 100%|██████████| 1866/1866 [02:0900:00, 14.43it/s]
100%|██████████| 69/69 [00:0200:00, 32.44it/s]
Save model with best accuracy :0.839127
EVALUATE [13/50] accuracy0.839 precision0.838 recal0.841 f1 score0.8395TRAIN iter1866 loss0.341275: 100%|██████████| 1866/1866 [02:1700:00, 13.60it/s]
100%|██████████| 69/69 [00:0200:00, 32.74it/s]
Save model with best accuracy :0.842649
EVALUATE [14/50] accuracy0.843 precision0.841 recal0.845 f1 score0.8431TRAIN iter1866 loss0.339279: 100%|██████████| 1866/1866 [02:1500:00, 13.74it/s]
100%|██████████| 69/69 [00:0100:00, 42.64it/s]
Save model with best accuracy :0.846399
EVALUATE [15/50] accuracy0.846 precision0.858 recal0.831 f1 score0.8440TRAIN iter1866 loss0.338046: 100%|██████████| 1866/1866 [01:4900:00, 17.00it/s]
100%|██████████| 69/69 [00:0100:00, 42.64it/s]
EVALUATE [16/50] accuracy0.844 precision0.844 recal0.843 f1 score0.8436TRAIN iter1866 loss0.334223: 100%|██████████| 1866/1866 [01:5900:00, 15.60it/s]
100%|██████████| 69/69 [00:0200:00, 32.00it/s]
EVALUATE [17/50] accuracy0.844 precision0.836 recal0.855 f1 score0.8455TRAIN iter1866 loss0.331690: 100%|██████████| 1866/1866 [02:0400:00, 15.01it/s]
100%|██████████| 69/69 [00:0100:00, 42.16it/s]
EVALUATE [18/50] accuracy0.844 precision0.834 recal0.860 f1 score0.8465TRAIN iter1866 loss0.328178: 100%|██████████| 1866/1866 [01:4900:00, 16.98it/s]
100%|██████████| 69/69 [00:0100:00, 42.50it/s]
EVALUATE [19/50] accuracy0.845 precision0.842 recal0.849 f1 score0.8454TRAIN iter1866 loss0.326720: 100%|██████████| 1866/1866 [01:4800:00, 17.12it/s]
100%|██████████| 69/69 [00:0100:00, 41.95it/s]
Save model with best accuracy :0.847421
EVALUATE [20/50] accuracy0.847 precision0.844 recal0.853 f1 score0.8482TRAIN iter1866 loss0.324938: 100%|██████████| 1866/1866 [01:4900:00, 16.99it/s]
100%|██████████| 69/69 [00:0100:00, 43.29it/s]
EVALUATE [21/50] accuracy0.845 precision0.842 recal0.848 f1 score0.8452TRAIN iter1866 loss0.322923: 100%|██████████| 1866/1866 [01:4800:00, 17.24it/s]
100%|██████████| 69/69 [00:0100:00, 43.47it/s]
EVALUATE [22/50] accuracy0.847 precision0.844 recal0.852 f1 score0.8480TRAIN iter1866 loss0.322150: 100%|██████████| 1866/1866 [01:4600:00, 17.51it/s]
100%|██████████| 69/69 [00:0100:00, 42.77it/s]
Save model with best accuracy :0.849920
EVALUATE [23/50] accuracy0.850 precision0.839 recal0.866 f1 score0.8523TRAIN iter1866 loss0.320312: 100%|██████████| 1866/1866 [01:4900:00, 17.06it/s]
100%|██████████| 69/69 [00:0100:00, 41.91it/s]
EVALUATE [24/50] accuracy0.847 precision0.843 recal0.853 f1 score0.8479TRAIN iter1866 loss0.319144: 100%|██████████| 1866/1866 [01:4900:00, 17.00it/s]
100%|██████████| 69/69 [00:0100:00, 42.76it/s]
EVALUATE [25/50] accuracy0.849 precision0.841 recal0.861 f1 score0.8511TRAIN iter1866 loss0.318375: 100%|██████████| 1866/1866 [01:4800:00, 17.20it/s]
100%|██████████| 69/69 [00:0100:00, 43.52it/s]
EVALUATE [26/50] accuracy0.850 precision0.843 recal0.859 f1 score0.8512TRAIN iter1866 loss0.317125: 100%|██████████| 1866/1866 [01:4800:00, 17.17it/s]
100%|██████████| 69/69 [00:0100:00, 42.54it/s]
EVALUATE [27/50] accuracy0.848 precision0.841 recal0.857 f1 score0.8490TRAIN iter1866 loss0.316708: 100%|██████████| 1866/1866 [01:4900:00, 17.03it/s]
100%|██████████| 69/69 [00:0100:00, 42.04it/s]
Stop from early stopping.
100%|██████████| 98/98 [00:0200:00, 38.74it/s]
TEST accuracy0.846 precision0.792 recal0.938 f1 score0.8587
100%|██████████| 98/98 [00:0200:00, 39.47it/s]
TEST[best f1] accuracy0.846 precision0.793 recal0.939 f1 score0.8594
一些结论 采用字向量而不是词向量经实验比较自训练的词向量和字向量后者效果更好 有38个标记没有被word2vec词向量覆盖 准确率达到84.6 超过了网上常见的84.0 训练了近30轮 词向量word2vec仅训练了5轮未调参显然不是最优的但也够用 RE2模型应该还能继续优化但没必要花太多时间调参
从RE2模型开始后续就进入预训练模型像Sentence-BERT、SimCSE等。
但在此之前计划先巩固下预训练模型的知识因此文本匹配系列暂时不更新等预训练模型更新差不多之后再更新。
完整代码
https://github.com/nlp-greyfoss/nlp-in-action-public/tree/master/text_matching/re2
