August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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文本数据的情感分析
｜
341
户。在上述情况下，我们可能并没有太多数据来训练模型，但是高准确率非常重要。
我们知道，情感会受到单词所在上下文的影响，而且使用预训练模型可以提高情感
预测。这样，我们就能够突破数据集的限制，从语言的常见用法中学习知识。
在这个案例中，我们将使用
Transformers
库，它提供了非常易用的功能，而且广泛
支持多种预训练模型。有关这个话题的讨论请参见下面的介绍。
Transformers
库一
直在不断更新，这要归功于多位研究人员的贡献。
选择
Transformers
库
虽然很多研究小组都推出了优秀的深度学习模型，但是目前它们都各成一派，
不同框架之间并不兼容。例如，BERT 模型主要是谷歌研究小组在 TensorFlow
上开发出来的，无法在 PyTorch 上运行。因此，如果有人想使用 PyTorch，
则必须移植或重写所有的代码。此外，这些深度学习模型并没有标准的输入
格式以及其他命名约定，因此很难实现标准化的方法。为此，我们可以考虑
Hugging Face 提供的 Transformers 库（
https://oreil.ly/F0Vy7
）。使用这个库有
两个主要的优势：
• 在 Transformers 库的帮助下，选择不同的预训练模型只需修改变量值即可。
该库实现并提供了最近两年开发的大多数标杆模型，如 BERT、GPT-2 等。
• 该库可以在 PyTorch 和 TensorFlow 中运行。这是到 2020 年为止最流行的
两大深度学习框架，如果使用 Transformers ...
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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.