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Datasaur was founded on the premise that building the right tools can further the adoption and democratization of artificial intelligence.
Companies both large and small have re-invented the wheel in building their own ad hoc data labeling tools.
We strive to combine all the industry’s best practices and create a machine learning platform so our clients can focus on doing what they do best.
Datasaur is a labeling platform that specializes in NLP. Within the first three years of the company’s growth, Datasaur has created audio, text, and OCR feature functions. Label Studio provides the ability to label audio, photos, text, HTML, and time series.
Labelbox and Datasaur both offer annotation software for organizations seeking to train their AI model. Although the two companies have many similarities, they do have some meaningful differences. Labelbox offers clients the ability to annotate images, text, audio, and video. Whereas Datasaur is focused on NLP data labeling, optimizing their platform for all things audio and text.
ML is a “garbage in, garbage out” technology. The effectiveness of the resulting model is directly tied to the input data; data labeling is therefore a critical step in training ML algorithms. Indeed, increasing the quantity and quality of training data can be the most efficient way to improve an algorithm. And with ML’s growing popularity the labeling task is here to stay. As you approach setting up or revisiting your own labeling process, review the following guide.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field.
Nartural Language Processing (NLP_ is beginning to add “order” to the legal field.
"Most comprehensive labelling tool in the market. Datasaur has saved us countless hours in building our own solution. My team lead never wants to go back to spreadsheets!"
"Operating in an industry where we have to be privacy- and security-conscientious with our data, Datasaur was the only acceptable solution for us. We recommend them for both feature set and support responsiveness."
"...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..."
