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商品描述
Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models.
Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks.
You'll learn:
- Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful products
- How to develop an intuition about the Transformer architecture and the impact of each architectural decision
- Ways to adapt pretrained language models to your own domain and use cases
- How to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrum
- Effective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniques
- How to interface language models with external tools and integrate them into an existing software ecosystem
商品描述(中文翻譯)
Transformer 基礎的語言模型是解決各種語言任務的強大工具，並且代表了自然語言處理領域的一次階段性轉變。然而，從演示和原型到成熟應用的過渡一直很緩慢。通過本書，您將學習構建有用產品的工具、技術和操作手冊，這些產品融合了語言模型的強大功能。
經驗豐富的機器學習研究員 Suhas Pai 提供了實用的建議，以應對常見的失敗模式並克服當前最先進模型的限制。您將深入了解 Transformer 架構及其變體，並了解語言模型的分類法，這可以幫助您洞察哪些模型在特定任務上表現更佳。
您將學習到：
- 應對當前最先進語言模型失敗模式的巧妙方法，以及利用其優勢構建有用產品的方法
- 如何培養對 Transformer 架構的直覺，以及每個架構決策的影響
- 如何將預訓練的語言模型適應到您自己的領域和用例
- 如何從可用的選擇中為您的領域和任務選擇合適的語言模型，以及如何處理自建與購買的困境
- 有效的微調和參數高效微調，以及少量學習和零樣本學習技術
- 如何將語言模型與外部工具接口並整合到現有的軟體生態系統中
