Our data here consists of a given set of utterances (given set of questions), reply(output for a given utterance), and intent(the class to which the utterance belongs).

We’re trying to build a model that rightly classifies a given utterance into one of those mentioned intents.

Below mentioned is a methodology to implement a multi-class text classification approach using a BI-LSTM model. Where the words are vectorized using the GloVe embeddings and fed as a sequence into the LSTM.

Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism

Building a Bi-LSTM based classification model

For computers to understand text, text needs to be vectorized. Vectorization is the process of converting text to numbers for a machine…


Photo by Franki Chamaki on Unsplash

Below are a series of publications from FIO team on how a data science rookie can make their Python code productive and deploy the same for the rest of the world to use the models from their web or mobile interfaces.

With this series, you will be able to modularize your code, open up endpoints, deploy your functionality, design a basic UI (or automate one with our free formbuilder), build a catch-all logging platform to continually train and better your model.

If you are stuck anywhere, feel free to reach out to us in the comments section so one of us can handhold your efforts to deployment.

FIO Labs

We develop Data Science and Data Governance solutions using design thinking to help organizations be successful and sustainable by continuously adapting.

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