Knowledge Graph Technology Showcase (Winter 2022) E4: Datasaur.ai
Science & Technology
Introduction
Welcome to another edition of the Knowledge Graph Technology Showcase, where we explore innovative tools in the data labeling and annotation space. In this episode, we delve into Datasaur.ai, a no-code data labeling software platform designed to facilitate the labeling of text and audio data for both technical and non-technical users. Join us as we review its features, usability, and impact within the natural language processing (NLP) landscape.
Introduction to Datasaur.ai
My name is Ivan, and I am the CEO and founder of Datasaur.ai. Having worked in publishing, I've seen firsthand the challenges associated with data labeling—challenges that Datasaur.ai aims to address. Our platform allows users from various backgrounds, from high school students to highly educated professionals, to label data with ease, making it accessible without requiring a technical background.
User-Friendly Interface
Upon accessing Datasaur.ai, you will notice its intuitive interface, designed to simplify the data labeling process. Let’s take a look at a pre-loaded demo document about a fictional bank. Users can select text spans, apply labels, and even create overlapping labels, which enables granular and nuanced annotation. Moreover, users have the capability to draw connections between labels, allowing for more complex relationships to be established.
Datasaur.ai was built with the recognition that while many tools cater to computer vision, there was a significant gap in dedicated NLP solutions. The platform covers various aspects of NLP, including named entity recognition (NER), co-reference resolution, and dependency parsing.
Use Cases and Applications
Initially developed for machine learning teams, Datasaur.ai has expanded its use cases to include basic data extraction and entry. The primary goal remains creating labeled datasets that can be used to train machine learning models. Users can label an initial set of data and rely on automation to scale the labeling process effectively.
Custom Taxonomies and Data Formats
Datasaur.ai supports a range of customizable taxonomies. Users have the flexibility to create their own labels based on their unique business requirements—this includes hierarchical label structures. The platform accommodates various document formats, from basic text files to advanced OCR projects, ensuring all users can upload and annotate their data.
Collaborative Features
Collaboration is an essential aspect of the Datasaur.ai experience, with features that allow multiple users to work on labeling projects simultaneously. The platform includes roles and permissions, enabling teams to scale their workforce effectively. One significant advantage of using Datasaur.ai is its ability to measure inter-annotator agreement using Cohen's Kappa, allowing for the identification of biases and improving annotation quality.
Integration with NLP Libraries
A standout feature of Datasaur.ai is its integration with popular NLP libraries. It can leverage existing models to provide initial labeling suggestions, ensuring that users are alleviated of the more tedious aspects of the annotation process.
Exporting Data
When it comes to data export, Datasaur.ai offers various formats such as CSV, JSON, and TSV, ensuring compatibility with most machine learning frameworks. Users can also create custom export transformers, allowing for tailored outputs to match the requirements of specific models.
Global Impact and Accessibility
Recognizing the global need for diverse NLP solutions, Datasaur.ai supports all languages that can be rendered in a browser. The platform actively collaborates with organizations worldwide to enhance language models for underrepresented languages. Furthermore, Datasaur.ai offers a free tier and support for academic and non-profit organizations working on critical research projects.
Conclusion
Datasaur.ai is making valuable strides in the field of NLP by addressing the data labeling bottleneck. This tool not only streamlines the labeling process for users of all backgrounds but also encourages best practices in machine learning and data ethics. Whether for machine learning purposes or basic data entry, Datasaur.ai enables users to create high-quality datasets, essential for the success of any NLP application.
For more information about Datasaur.ai, feel free to reach out via email at demo@datasaur.ai.
Keywords
- Datasaur.ai
- Data labeling
- NLP
- Taxonomy
- Collaboration
- Machine learning
- Inter-annotator agreement
- Data extraction
- Customization
- Language support
FAQ
What is Datasaur.ai?
Datasaur.ai is a no-code data labeling platform designed to facilitate the annotation of text and audio data for users with varying levels of technical expertise.
What types of documents can be uploaded to Datasaur.ai?
Users can upload a range of document types, including TXT, CSV, PDF, Word documents, Excel files, JSON, and PowerPoint presentations. The platform also supports OCR for scanned images.
Can I use my own taxonomies in Datasaur.ai?
Yes, Datasaur.ai allows you to create and customize your own taxonomies and hierarchical label structures based on your unique business needs.
How does collaboration work within the platform?
Datasaur.ai enables multiple users to work simultaneously on labeling projects, with various roles and permissions to facilitate team collaboration.
What languages does Datasaur.ai support?
The platform supports all human languages that can be rendered in a browser, making it accessible to a global audience.
Are there export options available for labeled data?
Yes, users can export labeled data in CSV, JSON, and TSV formats, along with custom export transformers for tailored outputs.
