Back to Fine Tune BERT for Text Classification with TensorFlow
Learner Reviews & Feedback for Fine Tune BERT for Text Classification with TensorFlow by Coursera Project Network
211 ratings
About the Course
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.
Top reviews
YC
Jun 19, 2021
The project is very clear and easy to follow. Would suggest providing some gmail account so that we don't have to log into the colab using our own google credentials.
AT
Feb 1, 2023
it is very helpful and simply explain the idea of Bert model , really it is useful project
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