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Why DataNeuron?

DataNeuron is the only platform that excels both in data curation and model personalization.

Why use DataNeuron?

Data Annotation, Model Evaluation, LLM Fine-Tuning, Model Deployment - DataNeuron has all you need in NLP

Fully Automated Data Labeling with 5-10% effort towards Validation

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.

Target complete NLP Landscape and NLP Model Lifecycle

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

Comparable Annotation Accuracy to pre-trained LLMs and HITL

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.

Advanced Model Training/ Fine-Tuning workflows and Model Deployment

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 LLM Workflow

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.


DataNeuron DSEAL

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.

Validation of 5-10% data with 100% automated data labeling


DataNeuron vs LLM for Data Labeling

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.


DataNeuron vs Human-In-The-Loop

ROI Calculator

HITL ROI Calculator

There are several reasons to prefer DataNeuron's annotation capability over Human-in-the-Loop annotations.
These are some examples:

  • HITL requires nearly 100% of the data to be annotated to start matching the accuracy achieved by DataNeuron with only 5% of the data annotated.
  • HITL annotation quality is generally inferior to DataNeuron due to a lack of SME expertise and human bias. DataNeuron mitigates these issues to a large extent with its "Recall vs Recognise" and "Multi User voting" mechanisms.
  • Cost of annotation and validation in DataNeuron is 95% less than the cost in the HITL approach.