Guide
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Webhooks in Label Studio: When And How To Use Them
Learn when you should use webhooks vs. the API in Label Studio, and see examples of what you can do with webhooks.
Caitlin Wheeless
2024-10-01
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Learn how to use YOLO's object detection model with Label Studio.
HumanSignal Team
2024-09-26
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LLM Evaluations: Techniques, Challenges, and Best Practices
Explore the topic of evaluation for LLMs, its importance, and how we should approach it. Learn how integrating systematic evaluations can help teams iteratively refine their models to meet real-world needs.
Jimmy Whitaker
2024-08-29
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How to Build and Evaluate RAG Applications with Label Studio, OpenAI & Ragas
In this tutorial, we'll guide you through the process of setting up and using Label Studio in combination with Ragas (Retrieval-Augmented Generation Answer Scoring) and GPT-4 to build an optimized QA application.
Jo Booth
2024-08-22
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Key Considerations For Evaluating RAG-Based Systems
Implementing RAG-based systems comes with challenges to be aware of, particularly in assessing the quality of generated responses. This article will walk you through some of those challenges.
Jo Booth
2024-08-20
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3 Ways To Automate Your Labeling With Label Studio
Delve into three effective methods to automate your labeling using Label Studio, including examples and resources.
Nate Kartchner
2024-07-18
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LLM Evaluation: Comparing Four Methods to Automatically Detect Errors
An ongoing challenge for Large Language Models (LLMs) is their tendency to hallucinate. In this article, we explore four methods to automatically detect these errors.
Nikolai Liubimov
2024-05-30
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Do I Need to Build a Ground Truth Dataset?
The short answer is: it depends. Read on as we explore this topic further, uncovering the advantages and drawbacks of each approach to help you make an informed decision.
Label Studio Team
2024-05-21
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Tutorial: Importing Local YOLO Pre-Annotated Images to Label Studio
Today, we're diving into Label Studio Format Converter, a handy tool that helps you encode labels into formats compatible with your favorite machine learning libraries, enhancing workflow efficiency.
Label Studio Team
2024-05-15
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Fine-Tuning Generalist Models for Named Entity Recognition
Generalist models, like GLiNER, provide an excellent starting point for the tasks that they aim to solve. Fine-tuning these models offers us a way to improve their performance in the areas that we care about to solve business problems.
Micaela Kaplan
2024-05-08
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Strategies for Evaluating LLMs
Sure, benchmarks are cool, but they don’t give you the feel or the intuition of how a model actually works. To get that, you’ve got to hack around with the model and throw real-world prompts at it — like you’d do in day-to-day tasks.
Label Studio Team
2024-04-23
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A Step-by-Step Guide to Preparing Data for Labeling
In the world of machine learning, data labeling plays a central role in training accurate and reliable models, and properly preparing your data for labeling sets the foundation for the success of your machine learning projects.
Label Studio Team
2024-03-06
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Interactive Data Labeling with LLMs and Label Studio’s Prompt Interface
Learn how to build a prompt-centric workflow in Label Studio, which combines real-time prompt engineering with data labeling. The goal of this prompter is to make working with data and LLMs seamless and dynamic.
Jimmy Whitaker
2024-02-28
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5 Tips and Tricks for Label Studio’s API and SDK
These five tips for using Label Studio's API and SDK demonstrate these tools' powerful capabilities and flexibility for managing data labeling projects. From efficient project creation and task imports to advanced configurations and bulk data exports, Label Studio provides a comprehensive and streamlined approach suitable for beginners and advanced users.
Jimmy Whitaker
2024-01-23
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An Introduction to Retrieval-Augmented Generation (RAG)
Get a brief overview of RAG and how it relates to LLMs, learn when you might consider using RAG, and get a summary of some challenges based on current research you should be aware of should you choose to travel down this path.
Nate Kartchner
2024-01-11
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Labeling Your Computer Vision Data for Your Next Artificial Intelligence Project
With the rise of deep learning and machine learning techniques, computer vision has become essential in various applications. But how do you get that data into your AI model effectively?
Label Studio Team
2023-11-07
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Building Agents for Data Labeling
Exploring how LLMs and agents can be leveraged to transform the annotation workflow, aiming for greater data labeling efficiency, accuracy, and scalability.
Jimmy Whitaker
2023-10-18
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The Future of Data Labeling: Embracing Agents
With their dynamic capabilities, agents can potentially adapt to the nuances of different datasets and environments, ensuring that the labeling process remains efficient regardless of the data's nature.
Jimmy Whitaker
2023-10-04
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Fine-Tuning an LLM to Create a ChatBot with Enterprise-Specific Data
Our Data Scientist in Residence, Jimmy Whitaker, recently outlined how to fine-tune foundation models for AI applications. In case you missed it, here's a quick summary of the workshop.
Label Studio Team
2023-09-21
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Recap: Accelerate Your Image Annotation Workflow with Label Studio and Segment Anything
We recently held a workshop demonstrating the powerful integration between the Segment Anything Model (SAM) and Label Studio. If you missed it, you can get the recap right here.
Label Studio Team
2023-09-12
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Intro to Hugging Face: A Valuable Directory of AI Models and Tools
Hugging Face is a highly esteemed platform widely recognized as one of the most valuable resources for discovering and utilizing Language Model (LLM) technology.
Label Studio Team
2023-08-22
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From Foundation Models to Fine-Tuned Applications Using Label Studio
We explore how Label Studio can be employed to build a Question-Answering bot trained on domain-specific knowledge.
Jimmy Whitaker
2023-08-14
Nikolai Liubimov
2023-08-14
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Learning the Lingo of LLM Fine-Tuning
Learn the lingo around fine-tuning LLMs. We've compiled a comprehensive glossary of terms that you should know when embarking on your LLM tuning journey.
Label Studio Team
2023-08-03
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Data Labeling and Comparative Analysis of Fine-Tuning Methods
Out of the box, LLMs can handle general tasks fairly easily. But what fine-tuning methodology should you use when you need to complete tasks that require more specialized knowledge than what LLMs typically have?
Label Studio Team
2023-07-26
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Kickstart your Label Studio Annotation Project on Hugging Face Spaces
With just a few quick configuration changes, you can launch a compact Label Studio production environment for your annotation and labeling projects, all within Hugging Face.
Chris Hoge
2023-07-24
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Turbo Boost Your Data Labeling Solution and Accelerate your LLM Tuning
Machine learning models are the race cars of artificial intelligence, designed to deliver fast and accurate results. But just like a race car, they need the right fuel - in this case, high-quality data that has been appropriately prepared.
Label Studio Team
2023-07-18
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Fine-tuning plays a pivotal role in harnessing LLMs for specific tasks. By exploring the process of fine-tuning and understanding its importance in LLMs, you will gain the knowledge and practical skills needed to optimize LLMs.
Label Studio Team
2023-07-13
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Make Your Labeling Team More Efficient With Label Studio
Learn some advanced techniques to get the most out of Label Studio and drive more efficiencies into your labeling process.
Nate Kartchner
2023-07-11
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Five Large Language Models You Can Fine-Tune Today
Hundreds of thousands of open source LLMs are available, and choosing one is not easy. However, there are a small number of highly capable models that we think are excellent choices today.
Nate Kartchner
2023-07-06
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Understanding the Label Studio JSON format
Ever wonder how our JSON format is generated or how to structure your JSON files to import data, pre-annotate your datasets, or work with the ML backend? Wait no further! We've got you covered with this blog post.
Erin Mikail Staples
2023-06-28
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Using the Segment Anything Model Integration
Segment Anything is a popular model you can use to accelerate computer vision & image labeling. Learn how to use the Segment Anything integration with Label Studio.
Erin Mikail Staples
2023-05-25
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Create a High-Quality Dataset for Reinforcement Learning from Human Feedback
RLHF has enabled language models trained on a general corpus of text data to be aligned with complex human values. This article details how you can train a reward model for RLHF on your own data.
Jimmy Whitaker
2023-05-11
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Human Feedback in AI: The Essential Ingredient for Success
In this article we explore the profound impact of human feedback on AI and examine how powerful tools like Label Studio are catalyzing a paradigm shift in AI training methodologies.
Jimmy Whitaker
2023-05-09
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To help you get started with data labeling, we've created this guide that outlines the best practices to ensure you label your data accurately.
Label Studio Team
2023-05-04
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Integrity, Accuracy, Consistency: 3 Keys to Maintaining Data Quality in Machine Learning
How do you identify and maintain high-quality data when you’re knee-deep in a project? And what does “data quality” mean in the context of machine learning? Read more to find out!
Nikolai Liubimov
2023-04-20
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Getting started with Object Detection
In this article, we’ll explore what object detection is, the challenges involved in implementing it, and share a step-by-step breakdown of getting started with this exciting technique.
Label Studio Team
2023-04-06
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What will the Long-Lasting Impacts of Large Generative Models Be?
Evaluating GPT-4’s release through the lens of open source data science leads us to see the possibility and potential for long-term impact — but it also reiterates the underlying importance of dataset development and context provided by human feedback through processes such as reinforcement learning through human feedback (RLHF) or human-in-the-loop processes.
Erin Mikail Staples
2023-03-21
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Contribute to the New Label Studio Community Docs
The Label Studio docs have a new look; with cleaner information architecture, and more ways to contribute.
Chris Hoge
2023-03-07
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Labeling Audio Data With Label Studio
In this guide, you'll learn how to set up an audio labeling project in Label Studio. Then we'll walk you through importing and labeling your data. You'll also see the many different features in Label Studio's audio labeling interface.
Erin Mikail Staples
2023-02-14
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Machine Learning Bias: What Is It, Why Is It Important, and What Can You Do About It?
A machine learning model can send unintended, dangerous conversational responses. Train your models to avoid these outcomes by learning what machine learning bias is and how to minimize it.
Label Studio Team
2022-11-30
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Implementing Audio Classification for Machine Learning Projects Using Label Studio
With this guide, learn how audio classification works and how to implement it when building audio ML projects, so you can optimize your ML models and build a better overall product.
Label Studio Team
2022-11-23
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Understanding Audio Classification: Everything You Need to Know
This article takes a look at audio classification and discusses the various use cases that can benefit from this technique.
Label Studio Team
2022-11-16
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How To Choose an Open-source Audio Classification Tool and 6 Options To Use
When choosing an annotation tool for your audio classification project, you need to carefully study its unique features and ensure that it works well with the rest of your stack. Finding the right tool will give you the best value in your audio classification projects.
Label Studio Team
2022-11-09
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Getting Started with Image Classification
In this guide, we dig into image classification—what it means, how it works, and the main steps to help you get started.
Label Studio Team
2022-11-02
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The Building Blocks of an Efficient Data Labeling Process
Instituting an efficient data labeling process is the key to eliminating inaccuracies in the data fed to machine learning models. Here are some generally applicable principles that can improve the efficiency and accuracy of your data labeling process.
Label Studio Team
2022-10-26
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Open Source Tools for Sentiment Analysis
Some developers and data scientists just want to grab code, download a repo and go. If that’s your style, choosing a fully-featured open source sentiment tool might be right choice for you.
Label Studio Team
2022-10-13
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6 Costly Data Labeling Mistakes and How To Avoid Them
Learn six of the most common data labeling mistakes we see in ML projects and the fixes that can help you maintain consistent, accurate training data.
Label Studio Team
2022-09-09
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Understanding Sentiment Analysis
This blog covers the basics of sentiment analysis: key components, use cases, challenges, and solutions.
Label Studio Team
2022-07-18
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Data Labeling: The Unsung Hero Combating Data Drift
Learn how various types of data drift and how data drift impacts model performance, along with several examples of how data labeling can tackle data drift.
Label Studio Team
2021-10-21
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10 important considerations for NLP labeling
The top 10 important considerations for NLP labeling and functionality in labeling tools for natural language processing machine learning projects.
Label Studio Team
2021-06-17