Fine Tune BERT for Text Classification with TensorFlow
5.0 /5
- Offered byCoursera
Fine Tune BERT for Text Classification with TensorFlow at Coursera Overview
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
Gain a comprehensive overview of the TensorFlow principles and concepts
Duration | 3 hours |
Start from | Start Now |
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 at Coursera Highlights
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
- 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
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
Skills you will learn
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
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
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
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
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.
Fine Tune BERT for Text Classification with TensorFlow at Coursera Entry Requirements
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
Fine Tune BERT for Text Classification with TensorFlow at Coursera Admission Process
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
Important Dates
May 25, 2024
Course Commencement Date
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Fine Tune BERT for Text Classification with TensorFlow at Coursera Students Ratings & Reviews
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
5/5
2 Ratings- 4-52
A
Abhinav Sanjay Thorat
Fine Tune BERT for Text Classification with TensorFlow
Offered by Coursera
5
Learning Experience: Learning experience was good
Faculty: Instructors taught well
It was live hands on along with tutor
Course Support: No career support provided
Reviewed on 1 May 2022Read More
View 1 Review
Fine Tune BERT for Text Classification with TensorFlow
at Coursera
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