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
Sentiment Analysis	SpaCy
OpenAI	OpenAI
Amazon Comprehend	NLTK
Google Vertex AI	CoreNLP and SparkNLP NER
Amazon SageMaker	CoreNLP and SparkNLP POS
Datasaur LLM Labs	FewNERD
Hugging Face	Hugging Face
Custom Model	Custom Model
Azure

Quick Start Guide

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

1. Select a service provider for assisted labeling;

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

3. Click Predict labels to apply labels to your document;

For the Row-Based project type, users can select the rows they want to be answered automatically and then click Predict Labels.