DataNeuron is the only platform that excels both in data curation and model personalization.
Why use DataNeuron?
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.
LLMs have recently been at the center of the NLP universe, and utilizing LLM's full potential for any domain-specific task requires good expertise in fine-tuning/prompt engineering. This entails creating an optimized dataset in order to achieve the goal faster and with fewer-shot learning. DataNeuron's DSEAL efficiently helps users in creating such datasets with 95% less effort. More importantly, strategic data sampling in DataNeuron achieves higher accuracy in a fine-tuned model when compared to a fine-tuned model with a sequentially/ randomly sampled dataset.
Additionally, DataNeuron provides a no-code interface for personalizing these LLMs for a variety of domain-specific tasks. Using DataNeuron's prediction API, a fine-tuned/ customized model can be easily accessed and integrated into a product.
Divisive Sampling and Ensemble Active Learning (DSEAL) is a proprietary algorithm developed by DataNeuron scientists to achieve state-of-the-art accuracy (SOTA) with the smallest data sample size possible while utilizing the full potential of active-learning methods. DSEAL integrated seamlessly with Traditional ML models and LLMs. DSEAL has achieved SOTA accuracy within a 1-2% margin across multiple experiments in various domains with less as 4-5% of total dataset.
DataNeuron Stage 1 performed better than many pre-trained LLMs across multiple datasets pertaining to different NLP tasks.
DataNeuron Stage 1 model does not require any sample paragraphs to train on implying that Stage 1 models can automatically annotate with high accuracy without any prior domain knowledge.
Since DataNeuron models are light-weight it scales much better for the large data annotation workflows when compared to LLMs. At the same time DataNeuron is able to achieve comparable/better accuracies with the proprietary Unsupervised models and DSEAL algorithms when compared pre-trained LLMs at lesser cost/time.