李沐70_bert微调——自学笔记

微调BERT

1.BERT滴哦每一个词元返回抽取了上下文信息的特征向量

2.不同的任务使用不同的特性

句子分类

将cls对应的向量输入到全连接层分类

命名实体识别

1.识别应该词元是不是命名实体，例如人名、机构、位置

2.将非特殊词元放进全连接层分类

问题回答

1.给定应该问题和描述文字，找出一个片段作为回答

2.对片段中的每个词元预测它是不是回答的开头或结束

总结

1.即使下游任务各有不同，使用BERT微调时君只需要增加输出层

2.但根据任务的不同，输入的表示，和使用Bert特征也会不一样

!pip install d2l==0.17.6  ### 很重要，不要下载错了，对于colab

Natural Language Inference and Dataset

斯坦福自然语言推断（SNLI）数据集

import os
import re
import torch
from torch import nn
from d2l import torch as d2ld2l.DATA_HUB['SNLI'] = ('https://nlp.stanford.edu/projects/snli/snli_1.0.zip','9fcde07509c7e87ec61c640c1b2753d9041758e4')data_dir = d2l.download_extract('SNLI')

读取数据集

定义函数read_snli以仅提取数据集的一部分，然后返回前提、假设及其标签的列表。*

def read_snli(data_dir, is_train):"""将SNLI数据集解析为前提、假设和标签"""def extract_text(s):# 删除我们不会使用的信息s = re.sub('\\(', '', s)s = re.sub('\\)', '', s)# 用一个空格替换两个或多个连续的空格s = re.sub('\\s{2,}', ' ', s)return s.strip()label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2}file_name = os.path.join(data_dir, 'snli_1.0_train.txt'if is_train else 'snli_1.0_test.txt')with open(file_name, 'r') as f:rows = [row.split('\t') for row in f.readlines()[1:]]premises = [extract_text(row[1]) for row in rows if row[0] in label_set]hypotheses = [extract_text(row[2]) for row in rows if row[0] \in label_set]labels = [label_set[row[0]] for row in rows if row[0] in label_set]return premises, hypotheses, labels

打印前3对前提和假设，以及它们的标签（“0”“1”和“2”分别对应于“蕴涵”“矛盾”和“中性”）。

train_data = read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):print('前提：', x0)print('假设：', x1)print('标签：', y)

前提： A person on a horse jumps over a broken down airplane .
假设： A person is training his horse for a competition .
标签： 2
前提： A person on a horse jumps over a broken down airplane .
假设： A person is at a diner , ordering an omelette .
标签： 1
前提： A person on a horse jumps over a broken down airplane .
假设： A person is outdoors , on a horse .
标签： 0

训练集约有550000对，测试集约有10000对。下面显示了训练集和测试集中的三个标签“蕴涵”“矛盾”和“中性”是平衡的。

test_data = read_snli(data_dir, is_train=False)
for data in [train_data, test_data]:print([[row for row in data[2]].count(i) for i in range(3)])

[183416, 183187, 182764]
[3368, 3237, 3219]

定义一个用于加载SNLI数据集的类。类构造函数中的变量num_steps指定文本序列的长度，使得每个小批量序列将具有相同的形状。

class SNLIDataset(torch.utils.data.Dataset):"""用于加载SNLI数据集的自定义数据集"""def __init__(self, dataset, num_steps, vocab=None):self.num_steps = num_stepsall_premise_tokens = d2l.tokenize(dataset[0])all_hypothesis_tokens = d2l.tokenize(dataset[1])if vocab is None:self.vocab = d2l.Vocab(all_premise_tokens + \all_hypothesis_tokens, min_freq=5, reserved_tokens=['<pad>'])else:self.vocab = vocabself.premises = self._pad(all_premise_tokens)self.hypotheses = self._pad(all_hypothesis_tokens)self.labels = torch.tensor(dataset[2])print('read ' + str(len(self.premises)) + ' examples')def _pad(self, lines):return torch.tensor([d2l.truncate_pad(self.vocab[line], self.num_steps, self.vocab['<pad>'])for line in lines])def __getitem__(self, idx):return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]def __len__(self):return len(self.premises)

我们可以调用read_snli函数和SNLIDataset类来下载SNLI数据集，并返回训练集和测试集的DataLoader实例，以及训练集的词表。

def load_data_snli(batch_size, num_steps=50):"""下载SNLI数据集并返回数据迭代器和词表"""num_workers = d2l.get_dataloader_workers()data_dir = d2l.download_extract('SNLI')train_data = read_snli(data_dir, True)test_data = read_snli(data_dir, False)train_set = SNLIDataset(train_data, num_steps)test_set = SNLIDataset(test_data, num_steps, train_set.vocab)train_iter = torch.utils.data.DataLoader(train_set, batch_size,shuffle=True,num_workers=num_workers)test_iter = torch.utils.data.DataLoader(test_set, batch_size,shuffle=False,num_workers=num_workers)return train_iter, test_iter, train_set.vocab

在这里，我们将批量大小设置为128时，将序列长度设置为50，并调用load_data_snli函数来获取数据迭代器和词表。然后我们打印词表大小。

train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)

read 549367 examples
read 9824 examples/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.warnings.warn(_create_warning_msg(18678

现在我们打印第一个小批量的形状。与情感分析相反，我们有分别代表前提和假设的两个输入X[0]和X[1]。

for X, Y in train_iter:print(X[0].shape)print(X[1].shape)print(Y.shape)break

/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.self.pid = os.fork()torch.Size([128, 50])
torch.Size([128, 50])
torch.Size([128])

Fine-Tuning BERT

import json
import multiprocessing
import os
import torch
from torch import nn
from d2l import torch as d2l

加载预训练的BERT

提供了两个版本的预训练的BERT：“bert.base”与原始的BERT基础模型一样大，需要大量的计算资源才能进行微调，而“bert.small”是一个小版本，以便于演示。

d2l.DATA_HUB['bert.base'] = (d2l.DATA_URL + 'bert.base.torch.zip','225d66f04cae318b841a13d32af3acc165f253ac')
d2l.DATA_HUB['bert.small'] = (d2l.DATA_URL + 'bert.small.torch.zip','c72329e68a732bef0452e4b96a1c341c8910f81f')

两个预训练好的BERT模型都包含一个定义词表的“vocab.json”文件和一个预训练参数的“pretrained.params”文件。我们实现了以下load_pretrained_model函数来加载预先训练好的BERT参数。

def load_pretrained_model(pretrained_model, num_hiddens, ffn_num_hiddens,num_heads, num_layers, dropout, max_len, devices):data_dir = d2l.download_extract(pretrained_model)# 定义空词表以加载预定义词表vocab = d2l.Vocab()vocab.idx_to_token = json.load(open(os.path.join(data_dir,'vocab.json')))vocab.token_to_idx = {token: idx for idx, token in enumerate(vocab.idx_to_token)}bert = d2l.BERTModel(len(vocab), num_hiddens, norm_shape=[256],ffn_num_input=256, ffn_num_hiddens=ffn_num_hiddens,num_heads=4, num_layers=2, dropout=0.2,max_len=max_len, key_size=256, query_size=256,value_size=256, hid_in_features=256,mlm_in_features=256, nsp_in_features=256)# 加载预训练BERT参数bert.load_state_dict(torch.load(os.path.join(data_dir,'pretrained.params')))return bert, vocab

加载和微调经过预训练BERT的小版本（“bert.small”）

devices = d2l.try_all_gpus()
bert, vocab = load_pretrained_model('bert.small', num_hiddens=256, ffn_num_hiddens=512, num_heads=4,num_layers=2, dropout=0.1, max_len=512, devices=devices)

Downloading ../data/bert.small.torch.zip from http://d2l-data.s3-accelerate.amazonaws.com/bert.small.torch.zip...

微调BERT数据集

定义了一个定制的数据集类SNLIBERTDataset。在每个样本中，前提和假设形成一对文本序列，并被打包成一个BERT输入序列，如图15.6.2所示。回想 14.8.4节，片段索引用于区分BERT输入序列中的前提和假设。利用预定义的BERT输入序列的最大长度（max_len），持续移除输入文本对中较长文本的最后一个标记，直到满足max_len。为了加速生成用于微调BERT的SNLI数据集，我们使用4个工作进程并行生成训练或测试样本。

class SNLIBERTDataset(torch.utils.data.Dataset):def __init__(self, dataset, max_len, vocab=None):all_premise_hypothesis_tokens = [[p_tokens, h_tokens] for p_tokens, h_tokens in zip(*[d2l.tokenize([s.lower() for s in sentences])for sentences in dataset[:2]])]self.labels = torch.tensor(dataset[2])self.vocab = vocabself.max_len = max_len(self.all_token_ids, self.all_segments,self.valid_lens) = self._preprocess(all_premise_hypothesis_tokens)print('read ' + str(len(self.all_token_ids)) + ' examples')def _preprocess(self, all_premise_hypothesis_tokens):pool = multiprocessing.Pool(4)  # 使用4个进程out = pool.map(self._mp_worker, all_premise_hypothesis_tokens)all_token_ids = [token_ids for token_ids, segments, valid_len in out]all_segments = [segments for token_ids, segments, valid_len in out]valid_lens = [valid_len for token_ids, segments, valid_len in out]return (torch.tensor(all_token_ids, dtype=torch.long),torch.tensor(all_segments, dtype=torch.long),torch.tensor(valid_lens))def _mp_worker(self, premise_hypothesis_tokens):p_tokens, h_tokens = premise_hypothesis_tokensself._truncate_pair_of_tokens(p_tokens, h_tokens)tokens, segments = d2l.get_tokens_and_segments(p_tokens, h_tokens)token_ids = self.vocab[tokens] + [self.vocab['<pad>']] \* (self.max_len - len(tokens))segments = segments + [0] * (self.max_len - len(segments))valid_len = len(tokens)return token_ids, segments, valid_lendef _truncate_pair_of_tokens(self, p_tokens, h_tokens):# 为BERT输入中的'<CLS>'、'<SEP>'和'<SEP>'词元保留位置while len(p_tokens) + len(h_tokens) > self.max_len - 3:if len(p_tokens) > len(h_tokens):p_tokens.pop()else:h_tokens.pop()def __getitem__(self, idx):return (self.all_token_ids[idx], self.all_segments[idx],self.valid_lens[idx]), self.labels[idx]def __len__(self):return len(self.all_token_ids)

下载完SNLI数据集后，我们通过实例化SNLIBERTDataset类来生成训练和测试样本。这些样本将在自然语言推断的训练和测试期间进行小批量读取。

# 如果出现显存不足错误，请减少“batch_size”。在原始的BERT模型中，max_len=512
batch_size, max_len, num_workers = 512, 128, d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_set = SNLIBERTDataset(d2l.read_snli(data_dir, True), max_len, vocab)
test_set = SNLIBERTDataset(d2l.read_snli(data_dir, False), max_len, vocab)
train_iter = torch.utils.data.DataLoader(train_set, batch_size, shuffle=True,num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(test_set, batch_size,num_workers=num_workers)

read 549367 examples
read 9824 examples

微调BERT

用于自然语言推断的微调BERT只需要一个额外的多层感知机，该多层感知机由两个全连接层组成（请参见下面BERTClassifier类中的self.hidden和self.output）。这个多层感知机将特殊的“”词元的BERT表示进行了转换，该词元同时编码前提和假设的信息为自然语言推断的三个输出：蕴涵、矛盾和中性。

class BERTClassifier(nn.Module):def __init__(self, bert):super(BERTClassifier, self).__init__()self.encoder = bert.encoderself.hidden = bert.hiddenself.output = nn.Linear(256, 3)def forward(self, inputs):tokens_X, segments_X, valid_lens_x = inputsencoded_X = self.encoder(tokens_X, segments_X, valid_lens_x)return self.output(self.hidden(encoded_X[:, 0, :]))

预训练的BERT模型bert被送到用于下游应用的BERTClassifier实例net中。

net = BERTClassifier(bert)

为了允许具有陈旧梯度的参数，标志ignore_stale_grad=True在step函数d2l.train_batch_ch13中被设置。我们通过该函数使用SNLI的训练集（train_iter）和测试集（test_iter）对net模型进行训练和评估。

lr, num_epochs = 1e-4, 5
trainer = torch.optim.Adam(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction='none')
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,devices)

loss 0.520, train acc 0.791, test acc 0.780
2536.5 examples/sec on [device(type='cuda', index=0)]

在这里插入图片描述