实现一个代码：采用python、bert、transformers，基于隐私相关种子词和大量微博用户博文文本数据，计算余弦相似度，构建隐私词库。微调bert来训练自己的微博用户文本数据后获得词向量来计算相似度结果是不是更准确？是的话请帮我实现带有微调功能的代码

好的，下面是基于BERT微调的代码实现：

import torch
import random
import numpy as np
import pandas as pd
import jieba
from transformers import BertTokenizer, BertForSequenceClassification, AdamW
from sklearn.metrics.pairwise import cosine_similarity

# 设置随机数种子
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

# 加载微博文本数据
text_data = []
with open("output/weibo2.txt", "r", encoding="utf-8") as f:
    for line in f:
        text_data.append(line.strip())

# 加载种子词
seed_words = ['教育经历']

# 加载BERT模型和分词器
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=1)

# 对微博文本进行分词和编码
def encode_text(text):
    words = jieba.lcut(text.strip())
    tokens = ["[CLS]"] + words + ["[SEP]"]
    token_ids = tokenizer.convert_tokens_to_ids(tokens)
    segment_ids = [0] * len(token_ids)
    return token_ids, segment_ids

# 构建数据集
data = []
for text in text_data:
    token_ids, segment_ids = encode_text(text)
    data.append((token_ids, segment_ids))

# 定义微调函数
def fine_tune(model, data, epochs=5, batch_size=32, lr=2e-5):
    optimizer = AdamW(model.parameters(), lr=lr)
    model.train()
    for epoch in range(epochs):
        np.random.shuffle(data)
        for i in range(0, len(data), batch_size):
            batch = data[i:i+batch_size]
            batch_token_ids = torch.tensor([x[0] for x in batch])
            batch_segment_ids = torch.tensor([x[1] for x in batch])
            batch_labels = torch.zeros(len(batch))
            optimizer.zero_grad()
            loss, _ = model(batch_token_ids, batch_segment_ids, labels=batch_labels)
            loss.backward()
            optimizer.step()

# 微调模型
fine_tune(model, data)

# 获取微调后的词向量
def get_word_embeddings(model, token_ids, segment_ids):
    model.eval()
    with torch.no_grad():
        outputs = model(token_ids.unsqueeze(0), segment_ids.unsqueeze(0))
        embeddings = outputs[0][0]
    return embeddings

# 构建隐私词库
privacy_words = set()
seed_embeddings = get_word_embeddings(model, *encode_text(" ".join(seed_words)))
for text in text_data:
    token_ids, segment_ids = encode_text(text)
    for i in range(1, len(token_ids) - 1):
        word = tokenizer.convert_ids_to_tokens([token_ids[i]])[0]
        if word in seed_words:
            continue
        embeddings = get_word_embeddings(model, token_ids[i], segment_ids[i])
        sim = cosine_similarity(embeddings.reshape(1, -1), seed_embeddings.reshape(1, -1))[0][0]
        if sim > 0.8 and len(word) > 1:
            privacy_words.add(word)

print(privacy_words)

这个代码首先对微博文本数据进行分词和编码，并使用BERT微调模型来训练这些数据，以获得微博文本数据的词向量。随后，使用这些词向量来计算每个词与种子词的相似度，并构建隐私词库。