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

1. 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.

2. 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.

3. 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	Bounding Box Labeling
Sentiment Analysis	SpaCy	Custom Model
LLM Assisted Labeling	LLM Assisted Labeling
Amazon Comprehend	NLTK
Google Vertex AI	CoreNLP and SparkNLP NER
Amazon SageMaker	CoreNLP and SparkNLP POS
Datasaur LLM Labs	Datasaur LLM Labs
Azure	FewNERD
Hugging Face	Hugging Face
Custom Model	Custom Model

Quick Start Guide

So how do you set up the ML Assisted Labeling extension? It's as simple as three steps:

1. Go to Manage Extension and Enable ML Assisted Labeling.

2. The ML Assisted Labeling Extension should appear on the right side.

   

   Enabling ML Assisted Labeling
3. Select a service provider for assisted labeling.

4. Most of the providers don’t need any additional information.

5. Click Predict labels to apply labels to your document.

For the Row based project type, users have several additional options and features:

1. Select specific rows for prediction: Choose which rows to include in the prediction.

2. Target text: Pick which text to use as input for reference.

3. Target question: Decide which output column to predict.

4. Faster prediction speed: Toggle this option to run predictions faster via the backend.

Enforce ML Assisted Labeling Settings from Admin or Reviewer to Labeler

This feature ensures consistency by allowing Admins or Reviewers to enforce their ML Assisted Labeling settings for Labelers. When enabled, Labelers will follow the exact setup specified by the Admin or Reviewer, ensuring uniformity in results.

By clicking the three dots button next to the ML Assisted Labeling Extension, Admins or Reviewers can access the option to "Modify service provider settings." Selecting "All assignees" allows everyone in the project to modify their own ML Assisted Labeling settings. Choosing "Admin or reviewer only" enables the enforcement feature, restricting changes to the Admin or Reviewer.

Once activated, Labelers will not be able to switch to a different service provider. However, Admins or Reviewers retain the ability to modify the settings as needed.

If the "Admin or reviewer only" option is chosen in an ongoing project, please make sure the labeler refreshes their page to sync with the latest settings.

Enabling Admin or Reviewer ML Assisted Labeling Settings to Labeler