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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文本分类
｜
105
考虑到我们完全没有使用任何标注数据，
0.78
的
F1
分数已经相当令人印象深刻了！这恰
恰说明了嵌入向量的多功能性和实用性，特别是当你能够创造性地使用它们时。
让我们来测试一下这种创造性。我们之前将标签命名为“一条负面
/
正面影
评”，但其实可以进一步优化。针对我们的电影评论数据集，可以使用“一
条非常负面
/
正面的影评”这类更具体的表述。这样，嵌入将捕捉到这是关
于电影的评论，并更加关注两个标签的极端性。你可以自己试试看这会如何
影响结果。
4.6
使用生成模型进行文本分类
使用生成模型（如
OpenAI
的
GPT
模型）进行分类的方式，与我们之前所做的有所不同。
生成模型接收文本输入并生成文本，因此有时称其为序列到序列模型是很恰当的。这与特
定任务模型形成鲜明对比，后者输出的是类别，如图
4-17
所示。
生成模型
目标：生成文本
目标：执行分类任务
1
is 1 + 1 ?What
输入 输出
词元序列
answer is 2The
movie ever !Best
词元序列
数值
词元序列
特定任务模型
序列到数值模型
序列到序列模型
图 4-17：特定任务模型从词元序列生成数值，而生成模型从词元序列生成词元序列
生成模型通常在多种多样的任务上进行训练，但往往无法直接满足你的场景需求。例如，
我们给生成模型一条没有任何上下文的电影评论，它根本不知道该如何处理。
我们需要帮助它理解上下文，并引导它得出我们想要的答案。如图
4-18
所示，这种引导过
程主要通过给模型输入指令或
提示词
来完成。迭代改进提示词以获得期望的输出的过程被 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.