Text Classification - Instructor

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This tutorial showcases how to implement text classification tasks—specifically, single-label and multi-label classifications—using the OpenAI API, Python's enum module, and Pydantic models. Motivation Text classification is a common problem in many NLP applications, such as spam detection or support ticket categorization. The goal is to provide a systematic way to handle these cases using OpenAI's GPT models in combination with Python data structures. For single-label classification, we first define an enum for possible labels and a Pydantic model for the output. The function classify will perform the single-label classification. Let's run an example to see if it correctly identifies a spam message. For multi-label classification, we introduce a new enum class and a different Pydantic model to handle multiple labels. The function multi_classify is responsible for multi-label classification. Finally, we test the multi-label classification function using a sample support ticket.

Text Classification using OpenAI and Pydantic ¶ This tutorial showcases how to implement text classification tasks-specifically, single-label and multi-label classifications-using the OpenAI API and Pydantic models. For complete examples, check out our single classification and multi-label classification examples in the cookbook. Motivation Text classification is a common problem in many NLP applications, such as spam detection or support ticket categorization. The goal is to provide a systematic way to handle these cases using OpenAI's GPT models in combination with Python data structure

Text Classification - Instructor

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