What do you like best about the product?

It is intended to be adaptable and scalable to accommodate the requirements of various labeling tasks. Datasaur can scale up or down to handle big datasets, depending on the project's scope. The infrastructure provided by Datasaur enables group labeling by numerous users, which enhances the precision and consistency of labeled data. Since Datasaur interfaces with well-liked machine learning frameworks like TensorFlow and PyTorch, using the labeled data for model training is simple.

What do you dislike about the product?

I don't like the software's sporadic clunkiness and sluggish data processing. The process, labeling tools, and user interface may not be completely customizable to meet the demands of every user. For organizations that require multilingual labeling help, Datasaur's present language support is limited, which may be a downside.

What problems is the product solving and how is that benefiting you?

We needed something practical for data modeling and labeling for our NLP project, and Datasaur worked miracles for us. With the help of this fantastic tool, data annotation is now extremely simple. When conducting annotation tasks like object identification and picture segmentation saves a significant amount of time.