ML Assisted Labeling
ML Assisted Labeling extension enables you to call open source models, LLMs, or your own model to automatically return labels and help you labeling!
Introduction
Datasaur's ML Assisted Labeling extension enhances data labeling efficiency and accuracy for NLP projects. It integrates open-source models, Large Language Models (LLMs), and custom models, providing automatic labeling for Span-based and Row-based projects. This tool streamlines the data labeling process, automating your labeling workflow to save time and improve data quality.
Key Features
Labeling multiple labels at once: Datasaur ML Assisted allows you to label multiple items within a label set at once, eliminating the need to label each item individually. Streamline your span-based projects with ease.
Integration with Popular Models: Seamlessly integrates with widely-used models like SpaCy, NER, POS, and Sentiment Analysis. Additionally, it supports integration with various LLMs, Hugging Face, and other model platforms.
Time-Saving Efficiency: Save valuable time and resources by automating the labeling process with Datasaur ML Assisted Labeling. Focus on critical tasks while quickly reviewing the automated labels and making sure you deliver a good quality data.
Supported Library
| Row Based | Span Based |
|---|---|
Quick Start Guide
So how do you set up the ML Assisted Labeling extension? It's as simple as three steps:
Select a service provider for assisted labeling;
Most of the providers don’t need any additional information;
Click Predict labels to apply labels to your document;
For the Row based project type, users have several additional options and features:
Select specific rows for prediction: Choose which rows to include in the prediction.
Target text: Pick which text to use as input for reference.
Target question: Decide which output column to predict.
Faster prediction speed: Toggle this option to run predictions faster via the backend.
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