Jayeeta Putatunda
Jayeeta Putatunda
Jayeeta is a Senior Data Scientist with 4+ years of industry experience. Currently, she is working on machine learning and NLP projects and explores a lot of state-of-the-art models to build cool products at Indellient US Inc., a leading software development and IT professional services company working with Fortune 100 companies. Prior to that, she worked at Deloitte. Jayeeta is also engaged with some amazing organizations like Women Who Code and Women Tech Network to promote and inspire more women to take up STEM and often leads technical webinars and talks. She received her Master of Science in Quantitative Methods and Modeling from City University of New York, NY and Bachelor of Science in Economics and Statistics from West Bengal State University, India.
ML conf EU 2020ML conf EU 2020
35 min
Power of Transfer Learning in NLP: Build a Text Classification Model Using BERT
The domain of Natural Language Processing have seen a tremendous amount of research and innovation in the past couple of years to tackle the problem of implementing high quality machine learning and AI solutions using natural text. Text Classification is one such area that is extremely important in all sectors like finance, media, product development, etc. Building up a text classification system from scratch for every use case can be challenging in terms of cost as well as resources, considering there is a good amount of dataset to begin training with.
Here comes the concept of transfer learning. Using some of the models that has been pre-trained on terates of data and fine-tuning it based on the problem at hand is the new way to efficiently implement machine learning solutions without spending months on data cleaning pipeline.
This talk with highlight ways of implementing the newly launched BERT and fine tuning the base model to build an efficient text classifying model. Basic understanding of python is desirable.