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Preprocess and clean data for BERT Classification
Load in pretrained BERT with custom output layer
Train and evaluate finetuned BERT architecture on your own problem statement
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge. 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.
Natural Language Processing
Deep Learning
Machine Learning
Sentiment Analysis
BERT
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
Introduction to BERT and the problem at hand
Exploratory Data Analysis and Preprocessing
Training/Validation Split
Loading Tokenizer and Encoding our Data
Setting up BERT Pretrained Model
Creating Data Loaders
Setting Up Optimizer and Scheduler
Defining our Performance Metrics
Creating our Training Loop
Loading and Evaluating our Model
您的工作空间就是浏览器中的云桌面，无需下载
在分屏视频中，您的授课教师会为您提供分步指导
由 SN 提供
Apr 29, 2021Thanks, Ari Anastassiou for the wonderful tutorial. Hoping you do a complete course on NLP using BERT soon.
由 RT 提供
Jul 18, 2020Cool tutorial. It's more clear now how to work with BERT
由 DB 提供
Nov 17, 2021The course covered the material well. Helpful to do the exercises side by side. I feel the course does require prior knowledge of pyTorch and BERT also.
由 RK 提供
Jun 29, 2020Required detail explanation and faculy support for error soliving and explroing alternative
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由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面，因此指导项目不在移动设备上提供。
指导项目授课教师是特定领域的专家，他们在项目的技能、工具或领域方面经验丰富，并且热衷于分享自己的知识以影响全球数百万的学生。
您可以从指导项目中下载并保留您创建的任何文件。为此，您可以在访问云桌面时使用‘文件浏览器’功能。
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指导项目不提供助学金。
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是，您可以在浏览器的云桌面中获得完成指导项目所需的一切。
您可以直接在浏览器中于分屏环境下完成任务，以此从做中学。在屏幕的左侧，您将在工作空间中完成任务。在屏幕的右侧，您将看到有授课教师逐步指导您完成项目。
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