Summarization
Auto-summarize. Yes, that precise!
Why use DataNeuron Summarization Flow?
Create and validate condensed text with DataNeuron’s Summarization flow
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.
The process “under the hood” in DataNeuron’s Summarization Flow.
Validation on Predicted Summaries
DataNeuron provides extracted summary of a given text which can be directly accepted by the validators/SMEs with a single click. The users can also modify the suggested summary or write their own summary from scratch. The validated data can be exported in a JSON format which is ready to be consumed for fine-tuning all popular LLMs.
Model Training & Deployment
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.
