Build Named Entity Recognition (NER) models to identify standard or custom entities with automated predictions using DataNeuron.
Why use DataNeuron's NER 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.
DataNeuron supports creating domain specific entities for targeted Information Extraction. It also provides support to modify default model predictions for pre-defined entities.
With an intuitively designed UI, DataNeuron makes it easier to do NER validation by a simple select of a phrase or rejection of a highlighted text. Contrasting colors of the entities makes it even easier for the user to understand the mapping without reading the full text every time. DataNeuron’s Active Learning approach continuously improves the model accuracy resulting into fewer and fewer modifications from the user’s side in every new batch. Overall it requires less than 10% of the data to get validated before observing comparable accuracy to state-of-the-art models.
DataNeuron automates pre-processing, model comparison and parameter selection, model training/fine-tuning, and model deployment. Once the model training is complete, the platform generates summary report on the training accuracy for every single attribute in the Masterlist. Prediction service provides highly accurate context-based predictions on the ingested data in near real time without writing any code. Further Masterlist Suggestions can be used to prepare better training data. The Masterlist can be continuously managed and tweaked based on new attributes in the same dataset. Prediction Service can also be integrated with various applications through the supporting APIs.