One-shot information extraction.
Customizable extraction model with as few as one example.
Universality and flexibility
Magi's learning material is widely spread among text of every industry in various quality, which nourishes the model's universality and flexibility, such that the model can be tuned to suit a specific task with as little data as only one sample.
The cost problem of NLP
It has long been a major obstacle for numerous industries to apply AI that the training cost is expensive, especially the process of preparing training datasets. In the cases of fields with high labor costs (medical industry, finance, etc.), the preparation of training datasets would typically require professionals to manually construct examples, which is both costly and relatively unreliable. Such obstacle would continue to affect maintenance and update as well. It could be challenging for organizations in certain fields to even verify the application value of NLP technology in their operations.
A chance to have a taste of NLP
Magi One could empower most industries with a convenient platform to apply the latest NLP technologies. Depending on Magi's general model from its continuous learning of nearly a decade, Magi One can train, in a short time with a petite dataset (containing as few as only one sample), a deployable model targeting a certain scenario for you to assess the applicability of NLP technology. With the growing size of training dataset and the expanding time of deployment, Magi One's performance would keep advancing.