Markup: Document Annotation

An open-source annotation platform designed to convert unstructured documents into organized, machine-readable data.

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About Markup: Document Annotation

Markup is a versatile, open-source annotation platform that transforms unstructured text into structured data suitable for NLP and machine learning projects, including named-entity recognition. It learns from your annotations to predict and suggest complex labels, streamlining data preparation. Built with GPT-4, it accelerates the creation of high-quality datasets from free-text sources with ease.

How to Use

To get started, enable JavaScript in your browser. Begin by reviewing the documentation, then sign in or create an account. Use the interface to annotate text, helping generate structured datasets efficiently.

Features

Learns from annotations to improve prediction accuracy

Converts unstructured documents into organized formats

Supports NLP and machine learning tasks like named-entity recognition

Use Cases

Creating structured datasets from free-text data

Performing named-entity recognition for NLP projects

Automating data annotation workflows

Facilitating machine learning data preparation

Best For

Data annotatorsMachine learning engineersNLP researchersData scientistsAI developers

Pros

Converts unstructured data into organized formats

Leverages GPT-4 for smarter annotations

Predicts and suggests labels to speed up workflows

Open-source and customizable

Ideal for NLP and ML data preparation

Cons

May involve a learning curve for new users

Requires JavaScript to operate

Frequently Asked Questions

Find answers to common questions about Markup: Document Annotation

What is Markup?

Markup is an open-source annotation tool that converts unstructured text into structured data for NLP and machine learning applications.

How does Markup assist in data annotation?

It learns from your annotations to predict and suggest complex labels, making data annotation faster and more accurate.

Can I use Markup for named-entity recognition?

Yes, Markup supports tasks like named-entity recognition, helping to identify and label entities within text.

Is Markup suitable for machine learning projects?

Absolutely. It streamlines data preparation by transforming raw text into structured datasets for ML models.

What are the system requirements for using Markup?

You need to enable JavaScript in your browser to run the application effectively.