torch.cosine_similarity

torch.cosine_similarity 是 PyTorch 中的一个函数，用于计算两个张量之间的余弦相似度。这个函数接受两个参数，分别为两个张量，并返回它们之间的余弦相似度值，范围在 -1 到 1 之间。

相关问题

import jieba import torch from transformers import BertTokenizer, BertModel, BertConfig # 自定义词汇表路径 vocab_path = "output/user_vocab.txt" count = 0 with open(vocab_path, 'r', encoding='utf-8') as file: for line in file: count += 1 user_vocab = count print(user_vocab) # 种子词 seed_words = ['姓名'] # 加载微博文本数据 text_data = [] with open("output/weibo_data.txt", "r", encoding="utf-8") as f: for line in f: text_data.append(line.strip()) print(text_data) # 加载BERT分词器，并使用自定义词汇表 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', vocab_file=vocab_path) config = BertConfig.from_pretrained("bert-base-chinese", vocab_size=user_vocab) # 加载BERT模型 model = BertModel.from_pretrained('bert-base-chinese', config=config, ignore_mismatched_sizes=True) seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"] seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens) seed_segment_ids = [0] * len(seed_token_ids) # 转换为张量，调用BERT模型进行编码 seed_token_tensor = torch.tensor([seed_token_ids]) seed_segment_tensor = torch.tensor([seed_segment_ids]) model.eval() with torch.no_grad(): seed_outputs = model(seed_token_tensor, seed_segment_tensor) seed_encoded_layers = seed_outputs[0] jieba.load_userdict('data/user_dict.txt') # 构建隐私词库 privacy_words = set() privacy_words_sim = set() for text in text_data: words = jieba.lcut(text.strip()) tokens = ["[CLS]"] + words + ["[SEP]"] token_ids = tokenizer.convert_tokens_to_ids(tokens) segment_ids = [0] * len(token_ids) # 转换为张量，调用BERT模型进行编码 token_tensor = torch.tensor([token_ids]) segment_tensor = torch.tensor([segment_ids]) model.eval() with torch.no_grad(): outputs = model(token_tensor, segment_tensor) encoded_layers = outputs[0] # 对于每个词，计算它与种子词的余弦相似度 for i in range(1, len(tokens) - 1): word = tokens[i] if word in seed_words: continue if len(word) <= 1: continue sim_scores = [] for j in range(len(seed_encoded_layers)): sim_scores.append(torch.cosine_similarity(seed_encoded_layers[j][0], encoded_layers[j][i], dim=0).item()) cos_sim = sum(sim_scores) / len(sim_scores) print(cos_sim, word) if cos_sim >= 0.5: privacy_words.add(word) privacy_words_sim.add((word, cos_sim)) print(privacy_words) # 输出隐私词库 with open("output/privacy_words.txt", "w", encoding="utf-8") as f1: for word in privacy_words: f1.write(word + '\n') with open("output/privacy_words_sim.txt", "w", encoding="utf-8") as f2: for word, cos_sim in privacy_words_sim: f2.write(word + "\t" + str(cos_sim) + "\n") 详细解释上述代码，包括这行代码的作用以及为什么要这样做？

这段代码是一个隐私词提取的程序，其目的是从微博文本数据中提取出可能包含隐私信息的词汇，并将其输出到文件中。

代码分为以下几个部分：

1.导入必要的库和模型

import jieba
import torch
from transformers import BertTokenizer, BertModel, BertConfig

其中，jieba是一个中文分词库，torch是PyTorch深度学习框架，transformers是一个自然语言处理模型库。

2.加载自定义的词汇表

vocab_path = "output/user_vocab.txt"
count = 0
with open(vocab_path, 'r', encoding='utf-8') as file:
    for line in file:
        count += 1
user_vocab = count
print(user_vocab)

这里的自定义词汇表是一些特定领域的词汇，例如医学领域或法律领域的专业术语。这些词汇不在通用的词汇表中，需要单独加载。

3.加载微博文本数据

text_data = []
with open("output/weibo_data.txt", "r", encoding="utf-8") as f:
    for line in f:
        text_data.append(line.strip())
print(text_data)

这里的微博文本数据是程序要处理的输入数据。

4.加载BERT分词器，并使用自定义词汇表

tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', vocab_file=vocab_path)
config = BertConfig.from_pretrained("bert-base-chinese", vocab_size=user_vocab)

BERT分词器可以将中文文本转换为一系列的词汇编号，这里使用自定义词汇表来保证所有的词汇都可以被正确地转换。

5.加载BERT模型

model = BertModel.from_pretrained('bert-base-chinese', config=config, ignore_mismatched_sizes=True)

BERT模型是一个预训练的深度学习模型，可以将文本编码为向量表示。

6.构建种子词库

seed_words = ['姓名']
seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"]
seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens)
seed_segment_ids = [0] * len(seed_token_ids)
seed_token_tensor = torch.tensor([seed_token_ids])
seed_segment_tensor = torch.tensor([seed_segment_ids])
model.eval()
with torch.no_grad():
    seed_outputs = model(seed_token_tensor, seed_segment_tensor)
    seed_encoded_layers = seed_outputs[0]

种子词库是指一些已知的包含隐私信息的词汇，这里只有一个“姓名”。这部分代码将种子词转换为张量表示，并调用BERT模型进行编码。

7.构建隐私词库

privacy_words = set()
privacy_words_sim = set()
for text in text_data:
    words = jieba.lcut(text.strip())
    tokens = ["[CLS]"] + words + ["[SEP]"]
    token_ids = tokenizer.convert_tokens_to_ids(tokens)
    segment_ids = [0] * len(token_ids)
    token_tensor = torch.tensor([token_ids])
    segment_tensor = torch.tensor([segment_ids])
    model.eval()
    with torch.no_grad():
        outputs = model(token_tensor, segment_tensor)
        encoded_layers = outputs[0]
    for i in range(1, len(tokens) - 1):
        word = tokens[i]
        if word in seed_words:
            continue
        if len(word) <= 1:
            continue
        sim_scores = []
        for j in range(len(seed_encoded_layers)):
            sim_scores.append(torch.cosine_similarity(seed_encoded_layers[j][0], encoded_layers[j][i], dim=0).item())
        cos_sim = sum(sim_scores) / len(sim_scores)
        print(cos_sim, word)
        if cos_sim >= 0.5:
            privacy_words.add(word)
            privacy_words_sim.add((word, cos_sim))
print(privacy_words)

这部分代码是隐私词提取的核心部分，其流程如下：

1. 对每个文本进行分词。
2. 将分词后的词汇转换为张量表示，并调用BERT模型进行编码。
3. 对于每个词，计算它与种子词之间的余弦相似度。
4. 如果相似度大于等于0.5，则将该词添加到隐私词库中。

8.输出隐私词库

with open("output/privacy_words.txt", "w", encoding="utf-8") as f1:
    for word in privacy_words:
        f1.write(word + '\n')
with open("output/privacy_words_sim.txt", "w", encoding="utf-8") as f2:
    for word, cos_sim in privacy_words_sim:
        f2.write(word + "\t" + str(cos_sim) + "\n")

这部分代码将提取出的隐私词输出到文件中，包括词汇本身和与种子词的相似度值。

torch.nn.cosinesimilarity

`torch.nn.cosinesimilarity`是PyTorch库中的一个计算余弦相似度的函数。它会接受两个张量作为输入，并返回它们之间的余弦相似度。余弦相似度是一种衡量两个向量之间相似程度的方法，在计算图像分类和自然语言处理中广泛应用。