AI in 5: How to Train a Classifier using an LLM

The AI in 5 tutorial demonstrates a concise guide to building a text classification model using a large language model and Cohere AI's embedding model for semantic text understanding. Focusing on a subset of 5,000 questions from a Student Questions dataset of around 120,000 entries, the tutorial explains data preprocessing, including converting the dataset into a suitable format for classification, and splitting it into training and testing sets. Users are guided to use the Clarifai platform to create an application, adjust workflows, and upload data for model training. The tutorial covers training the model using transfer learning and evaluates its performance using metrics such as ROC/AUC, Precision, Recall, and F1 Score on both training and unseen test data, demonstrating high effectiveness despite the limited dataset. This process encapsulates the key steps of data preprocessing, model training, and performance evaluation within the context of building a text classification model.

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