Try Label Studio Enterprise with new example projects and free trial
Label Studio Team
You can now try the most flexible data labeling platform, Label Studio Enterprise, using sample projects or by connecting your own data sources with a new 14-day free trial. You can also add team members, including managers, reviewers and annotators, to explore project management capabilities, collaborative team workflows, quality review and reporting.
The Heartex team has put together example projects with sample datasets to help you explore the possible use cases and user experience without having to connect your own data. The example projects cover a variety of popular machine learning and AI use cases, including:
Images
Optical Character Recognition (OCR) - Performing accurate optical character recognition (OCR) on images and PDFs is a challenging task, but one with many business applications, like transcribing receipts and invoices, or even for digitizing archival documents for record-keeping and research. With Label Studio, you can import the output from OCR engines and use the Label Studio UI to correct the recognized text and produce a clean OCR dataset that you can use for model training or other data analysis.
Object Detection - Label Studio offers several options for creating labeled datasets for use in object detection. One of which is to draw rectangular bounding boxes to annotate specific objects your model aims to recognize.
Image Classification - Are you training a model to recognize the type of content in images, for example for use in content moderation? Use this template to perform image classification with checkboxes.
NLP
Sentiment Analysis - Classify the sentiment of text using this template. For example, if you want to classify the sentiment of an email text or online review
Named Entity (NER) - Named Entity Recognition(NER) is a task of categorizing the entities in a text into categories like names of persons, locations, organizations, etc. This template supports overlapping text spans and very large documents.
Word Relation - To train a natural language processing model to perform relationship extraction tasks, use this template to create a dataset. This template prompts an annotator to label text spans and identify relationships between the spans. For example, identifying people and organizations, and adding relation arrows and labels to identify who founded an organization.
Audio
Speech Transcription - Audio transcription quality is important for accessibility, but also to ensure the quality of a service that you provide, such as customer support, or the outcomes of a meeting. Leverage Label Studio to produce high quality annotations for audio transcription.
Sound Recognition - For cases when you need to improve sound event detection, use this template to play an audio clip and label specific audio regions according to which event sound is audible from. As an interesting twist, this dataset contains bird songs.
Sentiment Analysis - If you want to perform audio classification tasks, such as intent or sentiment classification, use this template to listen to an audio file and classify.
Time Series
Forecasting - Train a machine learning model to perform forecasting on time series data, create a dataset using this template. This template prompts annotators to highlight predictable region spans in the time series channels and label them as “Regions”, then identify the trend forecast for a specific region.
Outliers and Anomaly Detection - Train a machine learning model to detect outliers and anomalies in time series data, use this template to label suspicious regions and classify those regions of the time series channels as outliers or anomalies. In this example we are looking at Stock open and close prices, volumes, as well as their highs and lows.
Basic Time Series Example - There are so many prediction problems that involve a time. Use this very simple template to familiarize yourself with basic time-series labeling.
Each example project comes with layout templates and sample datasets to get started quickly. Once you sign up for the trial, you’ll find the option to “explore example projects” which will allow you to choose a use case and create the project.
You can also test the Label Studio Enterprise features during your 14-day free trial, and connect with our experts if you want to dig into your business requirements and structure a Proof of Concept:
Project management & collaborative workflows: Control how Label Studio works for you and your annotation team. Label Studio Workspaces makes it easy to organize and isolate projects and manage your teams. You can define rules and criteria for automatic assignment or manually assigning tasks to annotators.
Data management: Use Label Studio to explore and filter your datasets. Managers, domain experts, and data scientists monitor, review, and verify annotations.
Role-based access control: Label Studio Enterprise includes predefined roles, such as Administrator, Manager, and Annotator (among others), configured to provide users with access to the right set of features and data for their job function. Leverage our SSO and SCIM integration to centralize access management.
Quality analytics & reporting: Label Studio Enterprise provides the metrics and reports you need to monitor label quality for an entire project, diagnose problematic samples, and to take corrective action.
Integrations: Connect to your relevant data sources, including cloud storage and ML backends. Leverage our webhooks, Python SDK and APIs to integrate Label Studio into your ML/AI pipeline.
Audit log: Label Studio Enterprise automatically maintains an audit log that tracks every action taken in Label Studio and the associated user.
Customize the interface: Design your own layouts with our easy to use HTML-like tags, or get started with popular templates. Customize your interface based on your use case, data types, or labeling process.
Get started with the 14-day free trial of Label Studio Enterprise, or contact our sales team to schedule a demo and feature overview tailored to your business and use cases.
Large Language Models (LLMs) are revolutionizing how we tackle data annotation, offering speed and efficiency that were previously unattainable. With Label Studio's Prompts, you can now handle multiple natural language processing (NLP) annotation types for a single data task in a unified Label Studio workflow.
Annotator Performance Dashboards make it easer to manage labeling teams at scale and orchestrate all the internal and external resources needed to improve data quality, without sacrificing speed.
In this post, we’ll guide you through the process of using Prompts in Label Studio Enterprise to pre-annotate data for Named Entity Recognition (NER) tasks.
