Custom Classifier 2.0

The Custom Classifier 2.0 is used for text classification. Its base model is trained on a huge corpus of news articles to discover relationships between sentences and the labels they belong to. It uses the zero shot learning technique. Thus, it can even generalize on new unseen categories with datasets it has not been trained on.

Custom Classifier 2.0 is based on deep learning. The AI trains your data and automatically furnishes you with categories and labels associated with your datasets. It further performs data analysis to attribute confidence scores to these categories and labels.

Custom Classifier 2.0 can quickly analyze large sets of open-ended verbatim comments, surveys, feedback to find actionable insights that boost your business. It can be customized and works accurately in real-time. Its salient features include smart topic identification, automatic categorization and in-depth data analysis.

try our custom classifier 2.0 demo

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In the demo below, provide an input text and some categories (labels) in which you want to classify the text.

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Why our Custom Classifier 2.0 API ?

ACCURATE

Accurately classifies unstructured textual data

REAL-TIME

Uses cutting edge technology to provide results in real time.

CUSTOMIZABLE

Can be easily trained on custom datasets to enhance model performance

How Our Custom Classifier 2.0 API Works?

Custom Classifier 2.0 works on the Zero-Shot Learning technique. This means, our base model is able to classify text it has not been trained on. It is achieved through transfer learning, by which knowledge learned from one dataset can be applied on a different one . People have proposed multiple zero shot learning approaches for vision tasks, but not text classification. This makes Custom Classifier 2.0 one of the earliest examples of applying zero-shot learning to text classification.

Customer Classifier 2.0 uses an algorithm that is trained to learn relationships between sentences and the labels they can be associated with. It is trained on a large corpus of data sets. Based on the paradigm. " Train Once, Test Anywhere", it has the ability to even generalize to new categories and on new datasets.

It also uses multiple neural network algorithms that use a training methodology to get near accurate results on a variety of datasets. One of the best methodologies is to use a Long Short-Term Memory (LSTM) model for the task of learning relationships. Through constant feedback connections, the LSTM algorithm used in Custom Classifier 2.0 helps in establishing the concept of "belongingness" between sentences and classes in the model. This knowledge is now useful for unseen classes or even unseen datasets.

use cases

Analyze Verbatim Comments

Verbatim comments may pose a challenge at first but analyzing them can be made easy and simple with the help of Deep Learning. We have launched custom classifier 2.0 which is now SmartReader to allow anyone to analyze verbatim comments quickly and accurately without writing a single line of code. The algorithm deciphers the output of the data set by mining key phrases that are tracked to particular labels. Read more...

Customer Survey Analysis

There is a need to distinguish between positive and negative feedback while performing survey analysis. Negative feedback is a goldmine of potential product enhancements, and positive feedback tells you where you are adding the most value to your customers. Using the add-on makes it super easy to use AI and find out the underlying sentiment behind thousands of text pieces in one go. Read more...

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