Building Classifiers on Top of LLM

We'll show you how to leverage a pre-trained language model in Python to build a simple text classifier. From setting up your environment to training and deploying the model, we'll cover it all. Plus, learn how to use the classifier in real-time! Perfect for developers curious about large language models in action.
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In this tutorial, we’ll explore how to construct a classifier using a Hugging Face model in Python. We’ll start by setting up the Python environment and installing necessary libraries. We’ll also delve into efficient data management techniques, such as utilizing AWS S3 buckets for handling large datasets effectively.

Throughout the tutorial, we’ll cover essential steps like data manipulation using Pandas, data splitting for training and testing, and tokenization. We’ll then discuss key aspects of training, including parameter setup and the training process itself, while emphasizing the importance of data preprocessing and model evaluation.

Towards the end, we’ll demonstrate how to apply the trained model for classification tasks, including loading the model, tokenizing input text, and analyzing results. Finally, we’ll summarize the process and encourage any questions from the audience.