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.

Sentiment Analysis with Deep Learning using BERT

位教师:Ari Anastassiou
访问权限由 New York State Department of Labor 提供
16,385 人已注册
您将学到什么
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
您将练习的技能
要了解的详细信息

添加到您的领英档案
仅桌面可用
了解顶级公司的员工如何掌握热门技能

在 2 小时内学习、练习并应用岗位必备技能
- 接受行业专家的培训
- 获得解决实训工作任务的实践经验
- 使用最新的工具和技术来建立信心

关于此指导项目
分步进行学习
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:
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Introduction to BERT and the problem at hand
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Exploratory Data Analysis and Preprocessing
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Training/Validation Split
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Loading Tokenizer and Encoding our Data
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Setting up BERT Pretrained Model
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Creating Data Loaders
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Setting Up Optimizer and Scheduler
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Defining our Performance Metrics
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Creating our Training Loop
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Loading and Evaluating our Model
推荐体验
Intermediate Python users with some exposure to NumPy, Pandas, and PyTorch.
2个项目图片
位教师

提供方
学习方式
基于技能的实践学习
通过完成与工作相关的任务来练习新技能。
专家指导
使用独特的并排界面,按照预先录制的专家视频操作。
无需下载或安装
在预配置的云工作空间中访问所需的工具和资源。
仅在台式计算机上可用
此指导项目专为具有可靠互联网连接的笔记本电脑或台式计算机而设计,而不是移动设备。
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学生评论
- 5 stars
62.40%
- 4 stars
25.56%
- 3 stars
8.02%
- 2 stars
2%
- 1 star
2%
显示 3/398 个
已于 Mar 1, 2025审阅
There is more to learn in this. But the basics are covered well with the practical example.
已于 May 17, 2020审阅
The instructor explains very well on how to using bert to train a sentiment classifier. Very cool project.
已于 Sep 13, 2020审阅
Very effective course to understand the concept of sentiment analysis using Deep Learning.. Thank you team






