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Fine Tune BERT for Text Classification with TensorFlow
  • Level Intermediate
  • Duration 3 hours
  • Course by Coursera Project Network
  • Offered by

About

This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub. Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with deep learning for Natural Language Processing (NLP), and have trained models with TensorFlow or and its Keras API. Note: This course works best for learners who are based in the North America region. We're currently working on providing the same experience in other regions.

Modules

Fine Tune BERT for Text Classification with TensorFlow

    • 2 Readings
    • 1 Plugin
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded Quiz: Test your Project Understanding

1 Labs

  • Fine Tune BERT for Text Classification with TensorFlow

2 Readings

  • Project-based Course Overview
  • Resources on how BERT works

Auto Summary

Learn to fine-tune a BERT model for text classification with TensorFlow in this 2.5-hour guided project. Ideal for those with Python and deep learning experience, you'll preprocess data, build input pipelines, and train and evaluate models using TensorFlow 2 and TensorFlow Hub. Offered by Coursera, this intermediate-level course is free and best suited for learners in North America.

Snehan Kekre
Instructor

Snehan Kekre