Back to Top
Up Arrow

Multiclass & Multilabel Text Classification

Build classification models with advanced workflows for automated data annotation and model training using DataNeuron.

Why use DataNeuron Classification Flow?

Perform Accurate Multilabel & Multiclass Classification using DataNeuron 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 Text Classification Flow.

  • Multiple file format support
  • Auto Parsing
  • Auto Pre-Processing
  • Seed paragraph support
  • Cloud Integration
Define Masterlist
  • Hierarchical Taxonomy
  • Data Coverage Metrics
  • Advanced Masterlist Suggestions
  • No Tag Class
  • Masterlist Summary
Validate Predictions
  • Fully Automated Annotation
  • Support for Multi-Label / Multi-Class
  • Proprietary DSEAL Approach
  • 5-10% data requires Validation
  • Auto Validation
Train Model & Deploy
  • Auto Model Training/ Deployment & APIs
  • Model Comparison
  • Control Hyperparameters
  • Support for Custom ML Models
  • Fine-Tune Multiple Task-Specific LLMs
Define Masterlist

DataNeuron Masterlist

Taxonomy Based Masterlist

Machine Learning is not binary so we don’t rely on rules or pre-defined functions, instead we rely on the simpler structure which is the Masterlist where we allow classes to have overlap. In contrast to other platforms that require the user to define multiple weak learner labeling functions. If a user lacks the labeling function heuristics, good results will be difficult to achieve.  Masterlist is comparatively easier to define than the labeling function required by existing Weak Supervision platforms. Further DataNeuron supports taxonomy or hierarchical ontologies on the Masterlist.

Advanced Masterlist & Coverage Metrics

Advanced Masterlist features to assist users in creating a more personalized Masterlist that is data specific rather than generic. It will help users define Masterlist that are accurate representations of the dataset. We analyze the Masterlist and provide an idea on where the classification could be good or bad. We also give the suggestions to improve the Masterlist.

DataNeuron Validation

Multiclass & Multilabel Classification

DataNeuron accelerates human-in-loop validations for automating data labelling, model creation, and end-to-end lifecycle management of Multiclass and Multilabel Text Classification ML models.

DataNeuron provides predictions for Multiclass classification use cases by mapping each paragraph to a single class, whereas for Multilabel classification each paragraph can be mapped to multiple classes (or none at all).

To reduce human bias and errors, DataNeuron employs a multi-user voting mechanism. In addition, the active learning approach is used to reduce time and effort by 95% while maintaining the quality of validated data.

Faster, Better & Easier Validation

While the entire data annotation process is automated, validation happens over two stages using an ensemble of unsupervised and semi-supervised models. User validation might be required for 5-10% of the total dataset corpus. DataNeuron uses a recommendation-driven process based on the Recognition vs Recall principle, which simplifies the validation process. This enables rapid programmatic data labelling, reducing validation time by almost 95%.

Additionally, the platform can perform Auto-Validation on paragraphs based on the Accuracy/ Confidence in Stage 2 Validation.

DataNeuron Model Training

Model Comparison & Hyperparameter Optimization

DataNeuron is the ideal combination of a no-code, fully automated platform while still providing enough levers for users to build a customized/desired model for their domain-specific tasks.
  • The platform enables users to easily compare various machine learning models and select the best algorithms for the task at hand.
  • DataNeuron also allows users to control the ranges and values of hyperparameters of these models to achieve faster convergence within the allowed tolerance level.

Model Training

DataNeuron has achieved comparable accuracy (within ~1-2 % margins) to state-of-the-art solutions with only 10% of the labeled data when compared to human-in-loop labeling. DataNeuron is an end-to-end NLP life cycle management platform with Model Training: it provides ready to consume API for the model.

DataNeuron’s workflow versioning helps user to update Masterlist classes even after the model deployment. Additionally iterative training can be performed at any point in time to mitigate the risk of data drift and model drift.

LLM Fine-Tuning

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.