CUSTOM CLASSIFIER

We have built this classifier for text classification which relies on Zero-Shot learning technique called as Custom Classifier. Our base model is trained on a large news corpus of 10 million news articles to discover relationships between sentences and their categories. The resulting model can then generalize on new, unseen categories as well not available in training data.

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In the demo below, provide an input text and some categories (labels) in which you want to classify the text. Depending upon the results you get, you may want to provide some sub-categories for your main category. Sub-Categories should be domain-related keywords for your main category, for eg: You can define sub-categories like "Pitcher","Home run","batter" for a main category like Baseball.

add your categories
Category Sub-Categories
+ Add New Category
result

Why our Custom Classifier API ?

Accurate

Highly accurate classification of unstructured textual data.

Real Time

State of the art technology to provide accurate results real-time.

Customizable

Can be trained on custom dataset to obtain similar accuracy and performance.

How Our Custom Classifier API Works?

Zero Shot Learning is a way to be able to infer dataset's members without training on it. It is mostly achieved by some form of transfer learning, by which knowledge learned from one dataset can be applied on a different one. While people have proposed multiple zero shot learning approaches for vision tasks where knowledge from imagenet dataset can be used on new ones, we haven't yet seen any example of zero shot learning for text classification.

In our latest research work, we have proposed a method to do zero shot learning on text, where an algorithm trained to learn relationships between sentences and their categories on a large noisy dataset can be made to generalize to new categories or even new datasets. We call the paradigm "Train Once , Test Anywhere". We also propose multiple neural network algorithms that can take advantage of this training methodology and get good results on different datasets. The best method uses an LSTM model for the task of learning relationships. The idea is if one can model the concept of "belongingness" between sentences and classes, the knowledge is useful for unseen classes or even unseen datasets.

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use cases

Text Classification for improving SEO

Custom classifier can help you automatically classify your blog’s content and web pages into your own categories. The benefit of custom classifier is that it doesn't require any training data.

CRM Automation

A custom classifier is highly customizable and can be trained as per business needs. It can assign and analyze CRM tasks based on priority and relevance, thereby reducing significant time and manual work involved in such tasks. Also, it doesn't require any training data and you can define your own categories.

Pricing

Price per 1000 hits (An API hit is a single call to one of our API endpoints containing upto 300 characters.)

$0

100 hits/month

$3

100 - 1M hits/month

$2.75

1M - 10M hits/month

$2.50

More than 10M hits/month

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Custom Solutions

Want to train your own custom model? Contact us to get started

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Integration

For Developers

Ready to integrate? Check out our API wrappers.

API Wrappers
For Analyst

Checkout our Text Analysis plugins to analyze your textual data without writing any code.

Google Sheets add-on MS Excel add-in

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