Build classification models with advanced workflows for automated data annotation and model training using DataNeuron.
Why use DataNeuron Classification Flow?
DataNeuron's "recognize vs recall" approach greatly simplifies the validator's task, saving time and effort, and freeing up critical resources. Compared to manual human-in-loop (HITL) labeling, DataNeuron achieved a 90% reduction in the number of paragraphs validated, while achieving accuracy comparable to any state-of-the-art model.
Support for Multi-Class, Multi-Label, NER, Summarization, and Translation workflows. Scale Task-Specific LLMs, Traditional ML, and Generative AI. Using DataNeuron’s proprietary light-weight models (ensemble of unsupervised, semi-supervised) and DSEAL for annotation you can achieve comparable/ better accuracies to HITL and Pre-Trained LLMs
Using Dataneuron’s DSEAL covers maximum possible variation in information with only a limited subset of paragraphs which helps in capturing more information at a faster rate, resulting into quicker convergence to SOTA accuracy. With DSEAL, the validators are always challenged with most interesting data points keeping them fully engaged and involved.
DataNeuron is a seamless platform to move from data preparation to model customization and deployment. It supports both traditional ML models as well as LLMs. You can train a model from scratch, compare multiple model performance, fine-tune latest LLMs and deploy the model in your product for variety of LLM tasks, all this with zero-code development.