Prompt Engineering 简明教程

Useful Libraries and Frameworks

在本章中，我们将探索一系列有用的库和框架，这些库和框架可以在提示工程师的提示工程项目中提供极大帮助。这些工具提供了必要的函数和资源，可以简化提示生成过程、微调和基于提示的语言模型的评估。

In this chapter, we will explore a selection of useful libraries and frameworks that can significantly aid prompt engineers in their prompt engineering projects. These tools provide essential functionalities and resources to streamline the prompt generation process, fine-tuning, and evaluation of prompt-based language models.

Hugging Face Transformers

Hugging Face Transformers 是一个流行的开源库，提供了预训练模型、标记器和实用工具，用于自然语言处理任务，包括提示工程。

Hugging Face Transformers is a popular open-source library that offers pre-trained models, tokenizers, and utilities for natural language processing tasks, including prompt engineering.

Key Features

  1. Pre-trained Models − Hugging Face Transformers provides access to a wide range of pre-trained language models, such as GPT-3, BERT, RoBERTa, and more, which can be fine-tuned for prompt engineering tasks.

  2. Tokenizers − The library offers tokenization tools that help convert text into input features suitable for language models.

  3. Pipelines − Hugging Face Transformers provides easy-to-use pipelines for various NLP tasks, including text generation, sentiment analysis, translation, and more.

OpenAI GPT-3 API

OpenAI GPT-3 API 允许开发者与强大的 GPT-3 语言模型进行交互，并创建基于提示的自定义应用程序。

The OpenAI GPT-3 API allows developers to interact with the powerful GPT-3 language model and create custom prompt-based applications.

Key Features

  1. GPT-3 Language Model − The API grants access to the GPT-3 language model, enabling prompt engineers to generate contextually relevant responses based on custom prompts.

  2. Chat Format − The API supports a chat-based format, allowing for interactive conversations with the language model by extending the prompt with user and model messages.

  3. Custom Prompt Engineering − Prompt engineers can leverage the API to fine-tune prompts for specific domains or tasks, making it a versatile tool for prompt engineering projects.

AllenNLP

AllenNLP 是一个基于 PyTorch 构建的自然语言处理库，为研究和生产应用程序提供了广泛的 NLP 功能。

AllenNLP is a natural language processing library built on PyTorch, offering a wide range of NLP functionalities for research and production applications.

Key Features

  1. Pre-trained Models − AllenNLP provides pre-trained models for various NLP tasks, which can be used as a starting point for prompt engineering projects.

  2. Custom Components − The library allows prompt engineers to define and integrate custom components, enabling tailored prompt-based model architectures.

  3. Flexibility and Extensibility − AllenNLP’s modular design and flexibility make it suitable for experimentation and customization in prompt engineering tasks.

TensorFlow Extended (TFX)

TFX 是一个端到端的平台，用于部署面向生产的机器学习管道，包括提示工程管道。

TFX is an end-to-end platform for deploying production-ready machine learning pipelines, including prompt engineering pipelines.

Key Features

  1. Scalable Pipelines − TFX allows prompt engineers to create scalable, reusable, and production-ready prompt engineering pipelines for fine-tuning and evaluation.

  2. TensorFlow Hub Integration − TFX integrates with TensorFlow Hub, providing access to various pre-trained models for prompt engineering projects.

  3. Model Versioning − TFX supports model versioning and management, making it easy to keep track of model iterations and improvements.

Sentence Transformers

Sentence Transformers 是一个专门为句子和文本嵌入而设计的库，为提示工程项目提供了有用的工具。

Sentence Transformers is a library specifically designed for sentence and text embeddings, offering useful tools for prompt engineering projects.

Key Features

  1. Sentence Embeddings − Sentence Transformers provides pre-trained models to generate high-quality embeddings for sentences or phrases, making them suitable for prompt representations.

  2. Cross-lingual Support − The library supports multilingual embeddings, allowing prompt engineers to create cross-lingual prompt-based models.

  3. Fine-tuning Support − Sentence Transformers models can be fine-tuned for specific tasks or domains, enhancing the model’s relevance and performance for prompt engineering.

Conclusion

在本章中，我们探索了提示工程师可以用来简化其提示工程项目的各种有用的库和框架。

In this chapter, we explored various useful libraries and frameworks that prompt engineers can use to streamline their prompt engineering projects.

Hugging Face Transformers 和 AllenNLP 提供了预训练的模型和标记化工具，而 OpenAI GPT-3 API 实现了与强大的 GPT-3 语言模型的交互。

Hugging Face Transformers and AllenNLP offer pre-trained models and tokenization tools, while OpenAI GPT-3 API enables interactions with the powerful GPT-3 language model.

TensorFlow Extended 提供了一个端到端平台，用于提示工程管道，而 Sentence Transformers 为提示表示提供了专门的句子嵌入。

TensorFlow Extended provides an end-to-end platform for prompt engineering pipelines, and Sentence Transformers offers specialized sentence embeddings for prompt representations.