Other labeling tools don’t offer the customizability or scalability needed for expanding NLP projects, teams, and needs. You need a robust tool that’s built for NLP and will grow as you do.
Unlock AI’s full business impact with a tool built specifically for NLP labeling, ready to be customized for your team’s requirements. All while retaining ease of use.
“Garbage in, garbage out.” Poor data can slow down model production, waste engineer time in debugging, and lead to errors in production that have negative business consequences.
Take advantage of QA capabilities that allow for high-level and granular reviews of labels and labelers to ensure data quality. Accelerate ideation to output, with 10X improved project times.
Deadlines can be difficult to hit when a majority of engineering time is spent cleaning and labeling data. Projects can be canceled if desired outputs aren’t reached in time.
Increase your team’s time by automating monotonous labeling tasks. Let them focus on building better models instead. Automate the bulk of the labeling workflow, from project setup and export to labeling itself.
Get a feel for how easy labeling can be with this example of NER token-based labeling in the Datasaur Playground.
We [Consensus] had a very complex and specific set of annotation needs. Datasaur was able to address those needs efficiently and effectively.
Information labeling tasks has been reduced by 80% which has allowed us to optimize our workflow much more, allowing us to focus on other areas that are also priorities for us.
"We looked at Prodigy, LightTag, LabelBox, Scale and more. You really can't beat Datasaur for their suite of features and price point."
We are excited to announce the launch of our new bounding box labeling feature—now available in beta!
What happened in NLP and AI news in November? Read all about “safety AI”, predictions for AI in 2023, and more.