Fine Tune BERT for Text Classification with TensorFlow

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estrellas
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Coursera Project Network
En este proyecto guiado, tú:

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

Clock2.5 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

natural-language-processingTensorflowmachine-learningdeep-learningBERT

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction to the Project

  2. Setup your TensorFlow and Colab Runtime

  3. Download and Import the Quora Insincere Questions Dataset

  4. Create tf.data.Datasets 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 tf.data

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

  10. Fine-Tune and Evaluate BERT for Text Classification

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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