tokens = jieba.lcut(text) tokens = [token for token in tokens if ((token not in stopwords) and len(token) > 1)] return tokens

这段代码使用了jieba库对文本进行分词，并过滤掉停用词和长度小于等于1的词汇，最后返回处理后的词汇列表。具体解释如下：

1. `jieba.lcut(text)`：使用jieba库的分词函数对文本进行分词，返回一个词汇列表。

2. `tokens = [token for token in tokens if ((token not in stopwords) and len(token) > 1)]`：通过列表解析式，筛选出不在停用词列表中且长度大于1的词汇，并保存在新的列表中。

3. `return tokens`：返回处理后的词汇列表。

相关问题

def tokenize_text(text): tokens = jieba.lcut(text) tokens = [token.strip() for token in tokens] return tokens

这是一个使用 jieba 分词对中文文本进行分词的函数，具体实现如下：

- 使用 jieba.lcut 对文本进行分词，得到分词结果。
- 对分词结果进行去除空格等操作，保证每个分词的格式一致。
- 返回分词结果。

该函数可以用于将中文文本转换为一系列有意义的词汇，方便进行后续的自然语言处理任务。

import jieba import torch from sklearn.metrics.pairwise import cosine_similarity from transformers import BertTokenizer, BertModel seed_words = ['姓名'] # 加载微博文本数据 text_data = [] with open("output/weibo1.txt", "r", encoding="utf-8") as f: for line in f: text_data.append(line.strip()) # 加载BERT模型和分词器 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') model = BertModel.from_pretrained('bert-base-chinese') 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]) with torch.no_grad(): seed_outputs = model(seed_token_tensor, seed_segment_tensor) seed_encoded_layers = seed_outputs[0] jieba.load_userdict('data/userdict.txt') # 构建隐私词库 privacy_words = 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]) with torch.no_grad(): outputs = model(token_tensor, segment_tensor) encoded_layers = outputs[0] # 对于每个词，计算它与种子词的相似度 for i in range(1, len(tokens)-1): word = tokens[i] if word in seed_words: continue word_tensor = encoded_layers[0][i].reshape(1, -1) seed_tensors =seed_encoded_layers[0][i].reshape(1, -1) # 计算当前微博词汇与种子词的相似度 sim = cosine_similarity(word_tensor, seed_tensors, dense_output=False)[0].max() print(sim, word) if sim > 0.5 and len(word) > 1: privacy_words.add(word) print(privacy_words) 上述代码运行之后有错误，报错信息为：Traceback (most recent call last): File "E:/PyCharm Community Edition 2020.2.2/Project/WordDict/newsim.py", line 397, in <module> seed_tensors =seed_encoded_layers[0][i].reshape(1, -1) IndexError: index 3 is out of bounds for dimension 0 with size 3. 请帮我修改

根据报错信息，问题出在获取seed_encoded_layers的第三个元素时，超过了张量的维度范围。可以通过打印输出seed_encoded_layers的形状，来确定张量的维度情况，看看是否与预期一致。

同时，可以检查一下seed_tokens和seed_token_ids的值，确保它们正确。

下面是修改后的代码：

import jieba 
import torch 
from sklearn.metrics.pairwise import cosine_similarity 
from transformers import BertTokenizer, BertModel 

seed_words = ['姓名'] 

# 加载微博文本数据 
text_data = [] 
with open("output/weibo1.txt", "r", encoding="utf-8") as f: 
    for line in f: 
        text_data.append(line.strip()) 

# 加载BERT模型和分词器 
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') 
model = BertModel.from_pretrained('bert-base-chinese') 

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]) 
with torch.no_grad(): 
    seed_outputs = model(seed_token_tensor, seed_segment_tensor) 
    seed_encoded_layers = seed_outputs[0] 

jieba.load_userdict('data/userdict.txt') 

# 构建隐私词库 
privacy_words = 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]) 
    with torch.no_grad(): 
        outputs = model(token_tensor, segment_tensor) 
        encoded_layers = outputs[0] 

    # 对于每个词，计算它与种子词的相似度 
    for i in range(1, len(tokens)-1): 
        word = tokens[i] 
        if word in seed_words: 
            continue 
        word_tensor = encoded_layers[0][i].reshape(1, -1) 
        seed_tensors = seed_encoded_layers[0][0].reshape(1, -1) # 修改此处，取第一个元素 
        # 计算当前微博词汇与种子词的相似度 
        sim = cosine_similarity(word_tensor, seed_tensors, dense_output=False)[0].max() 
        print(sim, word) 
        if sim > 0.5 and len(word) > 1: 
            privacy_words.add(word) 

print(privacy_words)