token = self.tokenizer(text, return_tensors="pt"),return_tensor="pt"是什么

意思？

return_tensors="pt"是一个参数，用于指示将令牌化后的输入文本返回为PyTorch张量（Tensor），而不仅仅是字符串形式的令牌序列。它使得文本数据可以直接输入到PyTorch模型中进行处理。

相关问题

import torch from transformers import BertTokenizer, BertForQuestionAnswering 加载BERT预训练模型和分词器 model_name = 'bert-base-chinese' # 中文预训练模型 tokenizer = BertTokenizer.from_pretrained(model_name) model = BertForQuestionAnswering.from_pretrained(model_name) 输入文本和问题 context = "我叫river，我在深圳上班。" question = "我是谁？" 对文本和问题进行分词和编码 inputs = tokenizer.encode_plus(question, context, add_special_tokens=True, return_tensors='pt') input_ids = inputs['input_ids'].tolist()[0] token_type_ids = inputs['token_type_ids'].tolist()[0] input_ids=torch.tensor([input_ids],dtype=torch.long) token_type_ids=torch.tensor([token_type_ids],dtype=torch.long) 使用BERT模型进行问答 start_scores, end_scores = model(input_ids=input_ids, token_type_ids=token_type_ids) 为什么strat_scores是str型的而不是tensor型的

根据您提供的代码，`start_scores` 应该是一个张量类型的对象，而不是字符串类型。如果您在打印 `start_scores` 的类型时得到了字符串类型的结果，可能是由于模型加载或处理的问题。

以下是一些可能导致 `start_scores` 类型不正确的原因和解决方法：

1. 检查模型是否成功加载：确保通过 `BertForQuestionAnswering.from_pretrained(model_name)` 成功加载了预训练模型。如果模型加载失败，可能会导致输出结果不正确。

2. 检查模型输入：确保将输入正确传递给模型。您可以使用 `print(input_ids.size(), token_type_ids.size())` 来检查输入张量的形状。确保 `input_ids` 和 `token_type_ids` 的形状符合预期。

3. 检查模型输出：在调用模型后，确保 `start_scores` 的类型是张量。您可以使用 `print(type(start_scores))` 来打印 `start_scores` 的类型。

如果问题仍然存在，请提供完整的错误信息和相关的代码细节，以便我能够更好地帮助您解决问题。

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)