Module 2: Text Representation

Hello! Welcome to Module 2 of LLM University! We are Meor and Jay, your instructors for this module.

In this module, you'll go through some practical labs that will teach you how to use Cohere’s endpoints to build language applications for many text representation tasks. Through the use of many codelabs and exercises, you’ll be able to write code to call the Cohere API for several different endpoints such as embed and classify.

Here are the chapters and topics that we'll cover in this module:

• Introduction to Text Embedding: Learn how to use embeddings and Cohere's Embed endpoint to explore and get insights on a dataset of sentences.
• Introduction to Semantic Search: Learn how to use text embeddings to search for the answer to a given query among the sentences in a dataset.
• Clustering with Embeddings: Leverage embeddings and K-means clustering to split a text dataset into different clusters with semantically similar sentences.
• Topic Modeling: Learn how to map a large dataset of 10,000 Hacker News posts using the Embed endpoint. You'll also be able to cluster the posts and extract keywords from each cluster.
• Text Classification: Learn about different applications for classification models, along with how to evaluate their performance.
• Few-Shot Classification: Learn how to classify a small dataset of sentences by their sentiment (positive, negative, or neutral), using Cohere's Classify endpoint.
• Fine-Tuning for Classification: Learn how to fine-tune the Classify endpoint model on custom datasets, enhancing its performance on specific tasks.
• Multilingual Sentiment Analysis: Build a sentiment analysis application that can classify sentiments in text from multiple languages.
• Conclusion - Text Representation: Recap what you have learned and explore suggested topics to continue your learning.

This course assumes a basic understanding of natural language processing, large language models, and their applications. If you need to learn these topics, or could use a refresher, check out Module 1.

There is another very important task in machine learning, which is generation. Generation is a very large topic, so we've dedicated the entire Module 3 of the course to it.

Ready? Let's learn about text representation!