Fine Tune BERT for Text Classification with TensorFlow

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Offered byCoursera
Public/Government Institute

Fine Tune BERT for Text Classification with TensorFlow
at
Coursera
Overview

Gain a comprehensive overview of the TensorFlow principles and concepts

Duration	3 hours
Total fee	Free
Mode of learning	Online
Schedule type	Self paced
Difficulty level	Intermediate
Official Website	Explore Free Course
Credential	Certificate

Fine Tune BERT for Text Classification with TensorFlow

Table of content

Overview
Highlights
Course Details
Curriculum
Faculty
+ 2 more items

Fine Tune BERT for Text Classification with TensorFlow
at
Coursera
Highlights

Gain expertise on widely used skills like natural-language-processing, Tensorflow, machine-learning, deep-learning, BERT

Fine Tune BERT for Text Classification with TensorFlow
at
Coursera
Course details

Skills you will learn

Python

What are the course deliverables?

Build TensorFlow Input Pipelines for Text Data with the tf.data API
Tokenize and Preprocess Text for BERT
Fine-tune BERT for text classification with TensorFlow 2 and TensorFlow Hub

More about this course

This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow
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

Fine Tune BERT for Text Classification with TensorFlow
at
Coursera
Curriculum

ntroduction to the Project

Setup your TensorFlow and Colab Runtime

Download and Import the Quora Insincere Questions Dataset

Create tf.data.Datasets for Training and Evaluation

Download a Pre-trained BERT Model from TensorFlow Hub

Tokenize and Preprocess Text for BERT

Wrap a Python Function into a TensorFlow op for Eager Execution

Create a TensorFlow Input Pipeline with tf.data

Add a Classification Head to the BERT hub.KerasLayer

Fine-Tune and Evaluate BERT for Text Classification

Fine Tune BERT for Text Classification with TensorFlow
at
Coursera
Faculty details

Snehan Kekre

Snehan Kekre is a Documentation Writer at Streamlit, the fastest and easiest way to build and share data apps. He has authored and taught over 40+ guided projects on machine learning and data science at Coursera. He has also worked as a skills consultant at Coursera, and as content strategist at Rhyme.com.