ML Assisted Labeling

Overview

Datasaur’s ML-Assisted Labeling extension enhances efficiency and accuracy in data labeling for NLP projects. It integrates open-source models, Large Language Models (LLMs), and custom models, providing automatic labeling for both span-based and row-based tasks. This tool streamlines the data labeling process, automating your workflow to save time and improve data quality, allowing you to focus solely on reviewing.

Use Case

Finding out the sentiment of user reviews (Row-based)

You can use ML-assisted labeling features in many ways, one of the core ways you can use is sentiment analysis of reviews. Let’s run through a step-by-step guide on how this can be done.

1. Create Project: Follow the guide here to create a row-based project. Here’s what the data looks like below.

   
2. Enable ML-Assisted Labeling: Click 'Manage' and toggle on the ML-assisted labeling feature.

   
3. Select Sentiment Analysis: Click “Sentiment Analysis” on "Settings" and choose the Target Text and Target questions.

   
4. Predict and Review Labels: Click 'Predict Label'. After processing, review the labels and click 'Accept' or 'Reject'. Voila! Your sentiment analysis is complete.

   

Using spaCy to automatically label entire text data (Span-based)

You can also use service provider, spaCy, in ML-assisted feature to automatically label your data. Here’s how you can achieve this:

1. Create Project: Just like before, follow the guide here to create a span-based project. Here’s what the data looks like below.

   
2. Enable ML-Assisted Labeling: Click 'Manage' and toggle on the ML-assisted labeling feature.

   
3. Select spaCy: Click “spaCy” on “Settings”. To learn more about spaCy click here.

   
4. Predict and Review Labels: Click 'Predict Label'. After processing, review the labels and click 'Accept' or 'Reject' for individual labels. Voila! Your sentiment analysis is complete.\

   

For further details, please visit the Assisted Labeling - ML Assisted Labeling.