Fine Tune BERT for Text Classification with TensorFlow
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
この実践的な経験を面接でアピールする
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.
It is assumed that are competent in Python programming and have prior experience with building deep learning NLP models with TensorFlow or Keras
あなたが開発するスキル
- natural-language-processing
- Tensorflow
- machine-learning
- deep-learning
- BERT
ステップバイステップで学習します
ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。
Introduction 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
ガイド付きプロジェクトの仕組み
ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です
分割画面のビデオで、講師が手順ごとにガイドします
レビュー
FINE TUNE BERT FOR TEXT CLASSIFICATION WITH TENSORFLOW からの人気レビュー
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.
The project was well explained and provided good understanding of bert for text classification. Also the quiz were good.
It would be helpful if the course was also offered outside of Google colab environment (standalone).
Helped me cement the basic understanding on how to use BERT for my use case.
よくある質問
よくある質問
ガイド付きプロジェクトはデスクトップとモバイル機器で利用できますか？
ガイド付きプロジェクトの講師にはどのような方がいらっしゃいますか？
完成したガイド付きプロジェクトから成果物をダウンロードできますか？
このガイド付きプロジェクトを行うにはどれくらいの経験が必要ですか？
特別なソフトウェアをインストールせずに、Webブラウザでこのガイド付きプロジェクトを完了できますか？
ガイド付きプロジェクトでの学習体験はどのようなものでしょうか？
さらに質問がある場合は、受講者ヘルプセンターにアクセスしてください。
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