In the rapidly advancing field of AI and machine learning, data labeling is crucial for building high-quality models. However, manual labeling is often time-consuming and very expensive. Label Studio offers several methods to automate and streamline the labeling process, ensuring that your models receive high-quality labeled data more efficiently, while still keeping a human in the loop for more difficult or tricky items. In this post, we'll delve into three effective methods to automate your labeling using Label Studio Community Edition: bootstrapping labels, semi-automated labeling, and active learning. Each method is discussed in detail, followed by links to technical resources for implementation.
It is also important to note that each of these methods require the use of our Machine Learning Backend. Rather than including instructions for setting up the ML backend for each example, we’ll just include these instructions at the beginning of the post.
Now that you’ve got Label Studio installed and the ML Backend ready to go, we’ll take a deeper look at the ways in which Label Studio helps you save time and money by automating your labeling.
Bootstrapping labels involves using a DIY workflow in the community edition of Label Studio utilizing the interactive LLM connector. This method leverages large language models (LLMs) like GPT-4 to generate initial labels for your dataset, significantly reducing the initial manual effort while still providing the opportunity for humans to review and adjust the dataset as-needed.
Bootstrapping labels with Label Studio and GPT-4 involves several steps:
For a detailed tutorial on bootstrapping labels using GPT-4, refer to the step-by-step tutorial written by Jimmy Whitaker, one of our resident Data Scientists.
Semi-automated labeling is a powerful approach that combines automated pre-labeling with human verification. This method can significantly speed up the annotation process, especially for large datasets, by leveraging both custom models and popular pre-trained models like Segment Anything for image segmentation, GroundingDino for object detection, and SPaCy for named entity recognition (NER).
Semi-automated labeling with Label Studio involves several key steps:
To provide a practical example, let's walk through a semi-automated labeling workflow using the SPaCy
Setting Up Label Studio:
Create a new labeling project in Label Studio and define the labeling interface for NER.
Integrating SPaCy:
Create a custom Python script to connect SPaCy to Label Studio’s ML backend:
import spacy
from label_studio_ml.model import LabelStudioMLBase
class SpacyNER(LabelStudioMLBase):
def __init__(self, **kwargs):
super(SpacyNER, self).__init__(**kwargs)
self.nlp = spacy.load("en_core_web_sm")
def predict(self, tasks, **kwargs):
predictions = []
for task in tasks:
text = task['data']['text']
doc = self.nlp(text)
predictions.append({
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": ent.start_char,
"end": ent.end_char,
"labels": [ent.label_]
}
} for ent in doc.ents
]
})
return predictionsFor additional example code and tutorials, you can refer to the Label Studio ML guide and ML tutorials.
Active learning optimizes the labeling process by selecting the most informative samples for annotation. Instead of randomly selecting data points, active learning algorithms identify and prioritize samples that will have the greatest impact on model performance. This results in greater efficiency and improved label quality.
Active learning with Label Studio Community Edition involves a manual approach where you can sort tasks and retrieve predictions to simulate an active learning process. Here’s an overview of the workflow:
For a more detailed guide on setting up an active learning loop in the Community Edition, visit the active learning documentation. This resource provides in-depth instructions on configuring your model, integrating it with Label Studio, and optimizing the active learning process.
We’ve heard from many teams who have explored using LLMs for data labeling, but were not able to achieve the same level of accuracy and reliability required for production model performance as human-in-the-loop labeling. That’s why we developed a new Prompts interface in the HumanSignal Platform (formerly Label Studio Enterprise) to deliver the efficiency of auto-labeling, without sacrificing quality.
You’ll can enable subject matter experts to efficiently scale their knowledge using a natural language interface, rather than training a large team of annotators. And you can ensure high quality annotations with real-time metrics based on ground truth data, plus constrained generation and human-in-the-loop workflows built into the platform. But one of the biggest benefits is that you’ll be able to save time not bouncing between tools, creating custom scripts, or worrying about multiple import/export formats because the HumanSignal Platform gives you a streamlined workflow from data sourcing to ground truth creation to auto-labeling and optional human-in-the-loop review.
To get a sense for exactly how this could benefit your labeling efforts, see what our global manufacturing customer Geberit was able to accomplish using the new Prompts interface for auto-labeling:
Contact our sales team to learn more and schedule a demo.
Automating your labeling process with Label Studio Community Edition can significantly enhance efficiency and quality in data annotation. By leveraging methods such as bootstrapping labels, semi-automated labeling, and active learning, you can streamline your workflows and focus more on building and refining your models. Each method offers unique advantages and can be tailored to meet your specific needs.
By implementing these techniques, you can reduce the manual effort involved in data labeling, improve the consistency and quality of your labeled data, and ultimately build better-performing machine learning models. For more detailed instructions and technical resources, be sure to explore the linked resources for each method. Happy labeling!
Go through the entire fine-tuning process on OpenAI’s platform—from preparing recently-posted Wikipedia data to estimating costs and deploying your fine-tuned model.
Learn when you should use webhooks vs. the API in Label Studio, and see examples of what you can do with webhooks.
Learn how to use Ultralytics YOLOv8 object detection model with Label Studio.