Guides

Evaluating leading text-to-speech models

Discover how to employ a more comprehensive approach to evaluating leading text-to-speech models using both human preference ratings and automated evaluation techniques.

Using multimodal chat to enhance a customer’s online support experience

In this guide we’ll show how Labelbox can be used to collect training data for an ecommerce chatbot that responds to customer queries about online shopping.

AI foundations: Understanding embeddings

Learn how to utilize embeddings for data vector representations and discover key use cases at Labelbox, including uploading custom embeddings for optimized performance.

How to accelerate labeling projects using GPT–4V in Foundry

Learn 3 specific examples of how teams can use Labelbox to accelerate labeling projects by using multimodal models to create high-quality labels for various data types including images, HTML, and text. 

Detecting swimming pools with GPT4 Visual

Explore how Model Foundry enables teams to efficiently compare and select the right foundation model to kickstart LLM development, decreasing costs and accelerating time-to-value.

How to analyze customer reviews and improve customer care with NLP

Learn how to leverage Labelbox's data-centric AI platform to redefine customer care with AI and create solutions tailored to unique customer care challenges.

How to evaluate object detection models with Labelbox Model Foundry

Explore how Model Foundry enables teams to efficiently compare and select the right foundation model to kickstart computer vision development, decreasing costs and accelerating time-to-value.

Zero-Shot Learning vs. Few-Shot Learning vs. Fine-Tuning: A technical walkthrough using OpenAI's APIs & models

With large language models (LLMs) gaining popularity, new techniques have emerged for applying them to NLP tasks. Three techniques in particular — zero-shot learning, few-shot learning, and fine-tuning — take different approaches to leveraging LLMs. In this guide, we’ll walk through the key difference between these techniques and how to implement them.  We’ll walk through a case study of extracting airline names from tweets to compare the techniques. Using an entity extraction dataset, we’ll be

How to use the model Foundry for automated data labeling and enrichment

Learn how to harness the power of Model Foundry to automate and enrich data workflows in Labelbox.

How to fine-tune OpenAI’s GPT-3.5 Turbo using Labelbox

In this guide, we’ll cover how to leverage Open AI’s GPT-3.5 and Labelbox to simplify the fine-tuning process, allowing you to rapidly iterate and refine your models’ performance on specific data.