It provides annotation features for text classification, sequence labeling and sequence to sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours. You can try the annotation demo.

Okay, let's talk about the one thing which doesn't get that much attention in the data science realm: labeling your data. It's a painful process, and that may lead to its disregard in tutorials you found on the internet or bootcamps you joined. However, it's one of the most crucial components in the data pipeline, you know, garbage in garbage out. A bad label leads to a bad model and a bad production practice. A data-centric approach to machine learning recently has sparked this idea into a whole new research playground.