May 2025
Intermediate to advanced
382 pages
10h 33m
Chinese
Content preview from 图解大模型 : 生成式AI 原理与实战
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132
｜
第
5
章
在
BERTopic
中，这类重排序模型被称为
表示模型
。这种方法的一个主要优势是，优化主
题表示的过程只需要循环执行与主题数量相等的次数。例如，我们有数百万个文档和一百
个主题，表示模块只需要对每个主题应用一次，而无须对每个文档都应用一次。
如图
5-21
所示，
BERTopic
设计了多种表示模块，允许你微调表示。表示模块甚至可以多
次堆叠，使用不同的方法来微调表示。
MMR
spaCy
MMR
KeyBERT
......
......
......
......
KeyBERT
......
选择
一个模块
用表示模块
微调主题
或堆叠
多个模块
KeyBERT
......
......
......
......
......
图 5-21：在应用 c-TF-IDF 权重后，主题可以通过各种表示模型进行微调，其中许多是 LLM
在探索如何使用这些表示模块之前，我们需要做两件事。第一，我们要保存原始的主题表
示，这样就更容易比较使用和不使用表示模型的结果：
# 保存原始表示
from copy import deepcopy
original_topics = deepcopy(topic_model.topic_representations_)
第二，我们创建一个简单的封装函数，用于快速可视化主题词的差异，以比较使用和不使
用表示模型的结果：
def topic_differences
(
model
,
original_topics
,
nr_topics=5
):
"""
显示两个模型之间主题表示的差异
"""
df = pd.DataFrame ...
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