Fine Tune BERT for Text Classification with TensorFlow

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Build TensorFlow Input Pipelines for Text Data with the API

Tokenize and Preprocess Text for BERT

Fine-tune BERT for text classification with TensorFlow 2 and TensorFlow Hub

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Clock2.5 hours
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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 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.

요구 사항

It is assumed that are competent in Python programming and have prior experience with building deep learning NLP models with TensorFlow or Keras

개발할 기술

  • natural-language-processing
  • Tensorflow
  • machine-learning
  • deep-learning
  • BERT

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Introduction to the Project

  2. Setup your TensorFlow and Colab Runtime

  3. Download and Import the Quora Insincere Questions Dataset

  4. Create for Training and Evaluation

  5. Download a Pre-trained BERT Model from TensorFlow Hub

  6. Tokenize and Preprocess Text for BERT

  7. Wrap a Python Function into a TensorFlow op for Eager Execution

  8. Create a TensorFlow Input Pipeline with

  9. Add a Classification Head to the BERT hub.KerasLayer

  10. Fine-Tune and Evaluate BERT for Text Classification

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.



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