# 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 a project**: Follow the guide [here](/data-studio-projects/creating-a-project.md) to create a row labeling project.
2. **Enable ML-assisted labeling**: Click the gear icon from the extension panel on the right to open the **Manage extensions** dialog, and enable the **ML-assisted labeling** feature.

   <figure><img src="/files/9KrQiRV1too1I67Qilqd" alt=""><figcaption><p>Manage Extensions Pop Up</p></figcaption></figure>
3. **Select Sentiment Analysis**: Click **Sentiment Analysis** for the service provider and choose the **Target text** and **Target question**.

   <figure><img src="/files/rR4ygzFLEGDbAeZ6ydaE" alt=""><figcaption><p>ML Assisted Labeling Settings - Sentiment Analysis</p></figcaption></figure>
4. **Predict and review labels**: Click **Predict labels**. After processing, review the labels and click **Accept** or **Reject**. Voila! Your sentiment analysis is complete.

   <figure><img src="/files/8fcm2xSYaIluIUDmGB2v" alt=""><figcaption><p>Prediction Result</p></figcaption></figure>

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

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

1. **Create project**: Just like before, follow the guide [here](/data-studio-projects/creating-a-project.md) to create a span-based project. Here’s what the data looks like below.

   <figure><img src="/files/rQFFRQgriIzpQH2jQErC" alt=""><figcaption><p>Span Based Project</p></figcaption></figure>
2. **Enable ML-assisted labeling**: Open the **Manage extensions** dialog and turn on the **ML-assisted labeling** extension.

   <figure><img src="/files/9KrQiRV1too1I67Qilqd" alt=""><figcaption><p>Manage Extensions Pop Up</p></figcaption></figure>
3. **Select spaCy**: Click **spaCy** for the service provider. To learn more about spaCy click [here](/assisted-labeling/ml-assisted-labeling/spacy.md).

   <figure><img src="/files/sML60IzBG0yL64WuhaZ1" alt=""><figcaption><p>ML Assisted Labeling Settings - spaCy</p></figcaption></figure>
4. **Predict and review labels:** Click **Predict labels**. After processing, review the labels and click **Accept** or **Reject** for individual labels. Voila! The labels are applied.

   <figure><img src="/files/sULJ0bE8R9pnbeqBNQLr" alt=""><figcaption><p>Prediction Result</p></figcaption></figure>

For further details, please visit the [Assisted Labeling - ML Assisted Labeling](/assisted-labeling/ml-assisted-labeling.md).


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