文章目录
Task
HW7的任务是通过BERT完成Question Answering。
数据预处理流程梳理
数据解压后包含3个json文件：hw7_train.json, hw7_dev.json, hw7_test.json。
DRCD: 台達閱讀理解資料集 Delta Reading Comprehension Dataset
ODSQA: Open-Domain Spoken Question Answering Dataset
- train: DRCD + DRCD-TTS
- 10524 paragraphs, 31690 questions
- dev: DRCD + DRCD-TTS
- 1490 paragraphs, 4131 questions
- test: DRCD + ODSQA
- 1586 paragraphs, 4957 questions
{train/dev/test}_questions:
- List of dicts with the following keys:
- id (int)
- paragraph_id (int)
- question_text (string)
- answer_text (string)
- answer_start (int)
- answer_end (int)
{train/dev/test}_paragraphs:
- List of strings
- paragraph_ids in questions correspond to indexs in paragraphs
- A paragraph may be used by several questions
读取这三个文件，每个文件返回相应的question数据和paragraph数据，都是文本数据，不能作为模型的输入。
利用Tokenization 将question和paragraph文本数据先按token为单位分开，再转换为tokens_to_ids数字数据。Dataset选取paragraph中固定长度的片段（固定长度为150），片段需包含answer部分，然后使用Tokenization 以CLS + question + SEP + document+ CLS + padding(不足的补0)的形式作为训练输入。
Total sequence length = question length + paragraph length + 3 (special tokens)
Maximum input sequence length of BERT is restricted to 512
training
testing
对于每个窗口，模型预测一个开始分数和一个结束分数，取最大值作为答案
Baseline
Medium
应用linear learning rate decay+change doc_stride
这里linear learning rate decay选用了两种方法
-
手动调整学习率
假设初始学习率为 η 0 η_0 η0，总的步骤数为 T T T，那么在第 t t t步时的学习率 η t η_t ηt 可以表示为：
η t = η 0 − η 0 T × t η_t=η_0−\frac{η_0}{T}×t ηt=η0−Tη0×t
其中：
- η 0 η_0 η0 是初始学习率。
- T T T是总的步骤数（total_step）。
- t t t 是当前的步骤数（从 0 开始计数）。
optimizer.param_groups[0]["lr"] -= learning_rate / total_step是 η t = η t − η 0 T η t η_t=η_t−\frac{η_0}{T}η_t ηt=ηt−Tη0ηt，optimizer.param_groups[0]["lr"]对应 η t η_t ηt。learning_rate对应 η 0 η_0 η0。total_step对应 T T T。i对应 t t t。
python# Medium--Learning rate dacay # Method 1: adjust learning rate manually total_step = 1000 for i in range(total_step): optimizer.param_groups[0]["lr"] -= learning_rate / total_step -
通过scheduler自动调整学习率
- (recommend) transformer
- torch.optim
python# Method 2: Adjust learning rate automatically by scheduler # (Recommend) https://huggingface.co/transformers/main_classes/optimizer_schedules.html#transformers.get_linear_schedule_with_warmup from transformers import get_linear_schedule_with_warmup scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=1000) # https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate # 这里如果要用pytorch的ExponentialLR，一定要导入optim模块，并且前面的AdamW是从transformers中import的这里要重新import import torch.optim as optim optimizer = optim.AdamW(model.parameters(), lr=learning_rate) scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
change doc_stride在QA_Dataset的时候修改段落滑动窗口的步长
python
##### TODO: Change value of doc_stride #####
# 段落滑动窗口的步长
self.doc_stride = 30 # MediumStrong
应用➢ Improve preprocessing ➢ Try other pretrained models
- 尝试其他预训练模型
比如bert-base-multilingual-case，因为它可以避免英文无法tokenization输出[UNK]，但是计算量大
python
model = BertForQuestionAnswering.from_pretrained("hfl/chinese-macbert-large").to(device)
tokenizer = BertTokenizerFast.from_pretrained("hfl/chinese-macbert-large")-
preprocessing ，在QA_Dataset中修改截取答案的窗口
-
随机窗口选择 Random Window Selection
随机选择窗口的起始位置
- 随机范围的下界
start_min = max(0, answer_end_token - self.max_paragraph_len + 1)答案结束位置向前移动self.max_paragraph_len - 1个标记后的位置和 0 较大的那个 - 随机范围的上界
start_max = min(answer_start_token, len(tokenized_paragraph) - self.max_paragraph_len)len(tokenized_paragraph) - self.max_paragraph_len：计算段落长度减去窗口长度后的位置，确保窗口不会超出段落末尾。min(answer_start_token, ...)：确保上界不超过答案开始位置，避免答案被截断。
- 随机选择
paragraph_start = random.randint(start_min, start_max)在计算出的下界和上界之间随机选择一个整数作为窗口的起始位置。 - 计算窗口结束位置
paragraph_end = paragraph_start + self.max_paragraph_len确保窗口长度为self.max_paragraph_len。
- 随机范围的下界
-
滑动窗口大小 Dynamic window size
python##### TODO: Preprocessing Strong ##### # Hint: How to prevent model from learning something it should not learn if self.split == "train": # Convert answer's start/end positions in paragraph_text to start/end positions in tokenized_paragraph answer_start_token = tokenized_paragraph.char_to_token(question["answer_start"]) answer_end_token = tokenized_paragraph.char_to_token(question["answer_end"]) # A single window is obtained by slicing the portion of paragraph containing the answer # 在training中paragraph的截取依据的是answer的position id """ mid = (answer_start_token + answer_end_token) // 2 paragraph_start = max(0, min(mid - self.max_paragraph_len // 2, len(tokenized_paragraph) - self.max_paragraph_len)) paragraph_end = paragraph_start + self.max_paragraph_len""" # Strong # Method 1: Random window selection start_min = max(0, answer_end_token - self.max_paragraph_len + 1) # 计算答案结束位置向前移动 self.max_paragraph_len - 1 个标记后的位置 start_max = min(answer_start_token, len(tokenized_paragraph) - self.max_paragraph_len) start_max = max(start_min, start_max) paragraph_start = random.randint(start_min, start_max + 1) paragraph_end = paragraph_start + self.max_paragraph_len """ # Method 2: Dynamic window size # 这个会造成窗口的大小大于max_paragraph_len，那么会造成输入序列的长度不一致，后面padding也要改，这里暂不采用 answer_length = answer_end_token - answer_start_token dynamic_window_size = max(self.max_paragraph_len, answer_length + 20) # 添加一些额外的空间 paragraph_start = max(0, min(answer_start_token - dynamic_window_size // 2, len(tokenized_paragraph) - dynamic_window_size)) paragraph_end = paragraph_start + dynamic_window_size """ -
Boss
➢ Improve postprocessing ➢ Further improve the above hints
doc_stride + max_length+ learning rate scheduler + preprocessing+ postprocessing + new model + no validation。
与strong baseline相比，最大的改变有两个，一是换pretrain model，在hugging face中搜索chinese + QA的模型，根据model card描述选择最好的模型，使用后大概提升2.5%的精度，二是更近一步的postprocessing，查看提交文件可看到很多answer包含CLS, SEP, UNK等字符，CLS和SEP的出现表示预测位置有误，UNK的出现说明有某些字符无法正常编码解码（例如一些生僻字），错误字符的问题均可在evaluate函数中改进，这个步骤提升了大概1%的精度。其他的修改主要是针对overfitting问题，包括减少了learning rate，提升dataset里面的paragraph max length, 将validation集合和train集合进行合并等。另外可使用的办法有ensemble，大概能提升0.5%的精度，改变random seed，也有提升的可能性。
python
if start_index > end_index or start_index < paragraph_start or end_index > paragraph_end:
continue
if '[UNK]' in answer:
print('发现 [UNK]，这表明有文字无法编码, 使用原始文本')
#print("Paragraph:", paragraph)
#print("Paragraph:", paragraph_tokenized.tokens)
print('--直接解码预测:', answer)
#找到原始文本中对应的位置
raw_start = paragraph_tokenized.token_to_chars(origin_start)[0]
raw_end = paragraph_tokenized.token_to_chars(origin_end)[1]
answer = paragraph[raw_start:raw_end]
print('--原始文本预测:',answer)